Processing single high-resolution satellite images may provide a lot of important information about the urban landscape or other applications related to the inventory of high-altitude objects. Unfortunately, the direct extraction of specific features from single satellite scenes can be difficult. However, the appropriate use of advanced processing methods based on deep learning algorithms allows us to obtain valuable information from these images. The height of buildings, for example, may be determined based on the extraction of shadows from an image and taking into account other metadata, e.g., the sun elevation angle and satellite azimuth angle. Classic methods of processing satellite imagery based on thresholding or simple segmentation are not sufficient because, in most cases, satellite scenes are not spectrally heterogenous. Therefore, the use of classical shadow detection methods is difficult. The authors of this article explore the possibility of using high-resolution optical satellite data to develop a universal algorithm for a fully automated estimation of object heights within the land cover by calculating the length of the shadow of each founded object. Finally, a set of algorithms allowing for a fully automatic detection of objects and shadows from satellite and aerial imagery and an iterative analysis of the relationships between them to calculate the heights of typical objects (such as buildings) and atypical objects (such as wind turbines) is proposed. The city of Warsaw (Poland) was used as the test area. LiDAR data were adopted as the reference measurement. As a result of final analyses based on measurements from several hundred thousand objects, the global accuracy obtained was ±4.66 m. The development and availability of Earth Observation (EO) data generate enormous possibilities for analyzing the Earth’s surface from various perspectives, starting from macro-level analyses concerning the occurrence of phenomena or changes over large areas of land, such as land cover ,2, land This article primarily analyzes EO data obtained from satellites orbiting the Earth. Data acquired from this altitude may be categorized based on the type of sensor used and its quality. Among the data, one may primarily distinguish those acquired by passive sensors  and active sensors (such as those recording microwave bands). The characteristics of products from different sensors, including spatial, radiometric, and temporal resolution, may vary and are often dependent on the data distribution method, whether commercial (such as the WorldView or Pleiades missions) or open .Optical satellite imagery provides a wealth of information regarding land cover. Especially when utilizing multi-spectral sensors, it can be used for land surface observation, terrain mapping, environmental monitoring, atmospheric studies, detecting landscape changes, monitoring natural disasters, and resource management. Optical satellite data allow for the assessment of vegetation changes, air pollution, water quality, forest degradation, soil erosion, and other environmental hazards. They also support environmental protection and sustainable development efforts . On the The increasing volume of EO data from satellites requires appropriate processing and the development of the relevant applications. The challenges associated with this primarily involve creating algorithms for specific purposes. This is also driven by the need for greater computational resources, in particular efficient solutions based on Graphics Processing Units (GPUs), which are particularly useful for creating machine learning-based algorithms.Based on the available data and developed or modified existing algorithms, the authors decided to explore the possibility of using high-resolution optical satellite data to develop a universal algorithm for a fully automated estimation of object heights within the land cover. For the purposes of this article, the term “land cover objects” is understood as both anthropogenic and natural features that protrude above the ground surface. The existing literature mainly focuses on buildings and lacks full automation ,14,15. TTo address these challenges, this study presents a novel approach to estimating the height of land cover in high-resolution satellite images by analyzing one image at a time. There is a strict correlation between the resulting lengths of shadows and the angles of a sensor. Acquisition metadata help us understand the geometric correlation between buildings and shadows in various situations, and thus to calculate their exact height. The main goal was to create an algorithm that would use shadow segmentation and building segmentation to automatically calculate the height of a building in a pre-defined area. With automatic processing and well-trained models, it is possible to obtain precise results and create novel ideas that will use this kind of data, e.g., for monitoring obstacles near airports.The structure of this article is as follows: In the second chapter, the research background and related works are presented. The third chapter introduces the research area, the materials used, and the research methodology. The fourth chapter presents the results, including a description of the algorithm calibration process. The fifth chapter contains a discussion of the results, along with a description of the limitations of the obtained outcomes. This article concludes with a summary of the research findings and a presentation of the overall conclusions of this study.Geospatial data resources are expanding practically every day, yet their widespread utilization is still not common. While the number of applications built upon these resources continues to increase, it still seems that their full potential is not being fully realized .Open geospatial data resources may be divided based on their origin and type. They may also be collected and shared either globally or at the local level  following specific data opening strategies governed by regulations, such as those introduced by the European Union . These strategies provide quick, structured, and easy access to data collected by national mapping agencies. These data include vector data that represent buildings in a given area from cadastral resources, raster data such as orthophotos, or LiDAR point clouds. On the other hand, globally available data allow for macro-level analysis. Notable examples include missions like Landsat and Sentinel, as well as platforms like EarthExplorer, which offer a wide range of products , and volunteer-based services like OpenStreetMap (OSM) .Furthermore, the amount of data offered by commercial providers is continuously increasing. Satellite missions from companies like Maxar and Airbus provide products with spatial resolutions as fine as 0.30 m, including stereo imagery for generating elevation data. Similarly, SAR data provided by companies like ICEYE also feature high spatial resolution. The spatial resolution offered by the newest satellites may be as fine as 1 m on the ground in multi-looked images. Azimuth resolution may even reach 5 cm, and range resolution may reach up to 50 cm, depending on the acquisition mode.A detailed description of individual satellite missions acquiring geospatial data, often accompanied by data quality assessments, can be found in .Deep learning solutions for geospatial data are becoming increasingly diverse. This applies to tasks related to image segmentation, such as creating land cover maps or segmenting specific objects like buildings , as wellTaking a holistic approach to such data and algorithm development, it is important to acknowledge their diversity. This applies to the spatial, radiometric, and qualitative characteristics of individual data. These diversities pose challenges in developing universal algorithms and constructing appropriate datasets. For example, variations in data representation can arise from different imaging times or orientation parameters . HoweverIn exploring deep learning solutions for geospatial data, our focus will primarily be on the tasks outlined in the project, namely shadow segmentation and building segmentation. However, it should be noted that the goal of the algorithm development was not to create state-of-the-art solutions but rather to utilize existing methods and implement them from scratch. Additionally, the datasets created for training the algorithms will only be partially described.When it comes to building segmentation, it is one of the most common tasks in the context of analyzing data acquired from satellite or aerial platforms. The applications of these solutions vary, ranging from verifying cadastral data  to automLooking specifically at deep learning solutions for building segmentation, the utilized architectures primarily include various variants of UNet ,25, DeepShadow segmentation is a relatively common problem in computer vision, but it is typically addressed in the context of regular images rather than remote sensing. As far as remote sensing imagery is concerned, shadows can be considered as something that interferes with tasks such as calculating remote sensing indices and requires removal or replacement . HoweverShadow detection and segmentation for remote sensing data can be achieved using both classical computer vision methods and deep neural networks. Among the classical methods, the utilization of shadow indices , PrincipIn creating the solution presented here, the authors focused on creating the best possible dataset to train network models that would be capable of detecting most objects located above the ground surface. To create such a dataset, automated data labeling and broad access to geospatial data through an open API were used to enable the generation of high-quality training and validation datasets. This process was carried out similarly for both shadow detection and building segmentation. Thanks to carefully selected network architecture parameters, a reliable and fully automatic solution that combines image processing, machine learning, and data science was developed successfully. Therefore, this solution stands out for its repeatability and unique approach, combining machine learning techniques in parallel with classical image processing and database integration, distinguishing it from other methods presented earlier in this chapter.Taking both the direct content of Earth Observation (EO) data, which is the pixel-level representation, and the indirect content, which consists of the metadata associated with the imagery, allows us to perceive the potential of extracting valuable information from the data. By performing appropriate measurements on the image, locating specific information in the image metadata, integrating these data sources, and subsequently conducting simple calculations based on the aggregated data, highly valuable insights regarding the parameters of objects present in the imagery may be derived ,36. ThisThe main problem that arises is the influence of the sensor’s position, often resulting in the inability to obtain a complete view of the shadow of a given object . This isIn the investigated case, the acquired vector data represent the footprints of objects, and this issue can largely be overlooked. However, it requires aligning the shadow with the vector, taking into account the different signs depending on the satellite’s azimuth. The situation is different, for example, for objects obtained by segmentation, which may be affected by radial displacements. The resulting vector corresponds to the roof of the object, and for such cases, the correction of occlusion or excessive shadow exposure should be taken into account. The complex estimation method presented by V.K. Shettigara and G. Sumerling  is a sigTaking a holistic view of solutions for estimating object heights based on EO data, various approaches can be mentioned, considering both the analyzed measurement product and the type of sensor used. In the literature, solutions based on the sensor–Sun–object relationship can be found, as well as those analyzing depth information or calculating the radial displacements of the objects in the image. Attempts to determine heights are carried out for imagery in the optical spectrum acquired from aerial or satellite platforms, as well as for imagery in the radar spectrum.Solutions based on attempting to determine heights directly from individual images employ an approach based on monocular vision (depth), which aims to analyze the depth information in the image, often using deep machine learning techniques. This approach allows for the generation of normalized Digital Surface Models (nDSM), as demonstrated, for example, in the case of orthophotomaps in , althougAnother group of solutions involves non-optical sensors or the use of multiple images. While the literature provides numerous references for solutions for data acquired from aerial platforms based on classical photogrammetric methods, the scope of this article primarily focuses on satellite data. In this case, height estimation is performed by measuring pairs of optical images. The authors of  proposedIndeed, height estimation attempts have also been made based on SAR data. However, due to the nature of SAR, these methods often require the use of projected heights . DespiteThe final group comprises solutions similar to the proposed approach, but they involve non-automated methods or focus on specific types of objects. Some of these studies concentrate on shadow detection based on image operations and then calculate the heights of specific buildings using the elevation angle in a single iteration . In 47]47], an iOur method combines several of the aforementioned approaches to achieve the best possible results and is comprehensive in nature, not focusing solely on buildings but also on other objects.During the implementation of tasks related to algorithm development, a wide range of publicly available solutions described in the previous section were utilized. These include primarily the described architectures of deep neural network models, as well as algorithms for post-processing and for the height estimation of objects from single images.Furthermore, during the process of training the deep neural network models, a computer with the following specifications was used: NVIDIA Quadro P5000 GPU, 2 × Intel XEON Gold 5220R CPUs, and 256 GB RAM.During the algorithm development process, the Python programming language was primarily used along with a wide range of geospatial analysis libraries such as rasterio, GDAL, geopandas, as well as communication with PostgreSQL databases using the PostGIS extension. The TensorFlow library was utilized for building and training the neural network models. The input data for the algorithm were standardized and followed the GeoTIFF format.Research was primarily conducted in the area of Warsaw, the capital of Poland, due to the availability of relevant data for this region and its diverse characteristics, including densely built urban areas as well as forested regions. However, it should be noted that the datasets created for machine learning methods during the research were generated based on a much broader area encompassing other regions in Poland and worldwide. When their quality was suitable, open data sources were also utilized after proper verification. The data were divided based on the type of task and process, including training, validation, and testing.A sample image covering the analyzed area is shown in The data used for the final analysis of the Warsaw area were obtained from the DigitalGlobe mission—WorldView-3, with an acquisition date of 5 April 2020. As reference data for comparison with the final results, laser scanning data provided by the Polish national mapping agency (GUGiK) through its geoportal  were useThe algorithm begins its operation by loading the data . There aImportantly, the presented algorithm is designed to be universal, meaning that it should support both high-resolution satellite imagery and conventional RGB imagery, such as orthophotos. However, it is important to note that additional data, as described in the previous paragraph, may be required to process the images accurately.The next step is data preparation. At this stage, various processes are applied depending on the data type, such as georeferencing correction through transformation if Ground Control Points (GCPs) are available. Additionally, sharpening or file validation processes may be performed. Following that, normalization takes place. Due to the aim of universality and handling different data types, each processed image is saved using 8 bits for each of the three channels: Red, Green, and Blue. This approach poses a challenge since it often results in information loss for satellite imagery. However, it allows for a universal approach. Image normalization can be done in various ways. In our case, the authors chose to use a simple linear normalization method, which involves clipping the lowest and highest percentile values and assigning values from 0 to 255 to the remaining pixels based on the histogram distribution.The next stage involves utilizing Deep Neural Networks (DNNs) to extract specific information from the imagery. Here, the main focus are two aspects: buildings and object shadows. During the early stages of algorithm development, the authors experimented with PCA- and GAN-based approaches for shadow segmentation, but they did not yield satisfactory results . Throughout the development process, various architectures were tested, including HRNETv2, DeepLabV3+, different variants of UNet, and transformer-based models. However, it was ultimately decided to use a modified version of the UNet model, which incorporates skip connections and includes a channel spatial attention module with a ResNetV2-101 backbone. The same model architecture was applied for both building extraction and shadow detection. Additionally, a combination of boundary-aware loss and weighted binary cross-entropy dice loss was used to accurately reconstruct the positions of both buildings and shadows.The database of objects obtained through machine learning was supplemented with objects available in open resources  or calculated in the correction process;θ—elevation angle of the sun.Then, objects that are completely in shadow, such as smaller objects near a tall object, are discarded. Additionally, a shadow map correction is performed based on the objects. Subsequently, morphological operations are applied to refine the shadows. Dilation with a 3 × 3 kernel size is primarily used to close the holes in the shadow map often caused by objects such as bright cars. These operations help to enhance the accuracy and quality of the shadow length estimation.Next, cutting lines are created with a defined frequency, based on the reverse azimuth to the azimuth of the sun. The starting points of the lines are placed along the two edges of the image located on the sunny side. Frequency refers to the value of the spacing between each subsequent cutting line. In this study, in successive iterations, the best balance between the length of the processing time and the quality of the results was obtained when this parameter was set to 2.5 m.BASE object is calculated (based on Equation (2)). Thanks to this operation, for objects that are in the database (for which there is an existing vector on the ground), there is no need to carry out a correction due to the lack of visibility of the shadow at a later stage of the work. The parameter e is calculated based on the lengthening or shortening of the cutting line. An object can be associated with multiple heights, so using statistical methods based on percentiles and standard deviation, outliers are discarded, and the object is assigned the maximum value from the remaining heights. In the research phase, the best results were obtained for a value equal to the 90th percentile. This allowed for the removal of height values that could represent a potential arising error, for example, from the merging of several shadows into one. Furthermore, shadows for which no relationship with an object is found are validated, clustered, and assigned the class “unknown”.In the next step, the cutting lines intersect with the shadow map to form shadow lines. For each line, the potential height of the object is calculated by multiplying the length of the shadow by the tangent of the sun’s elevation (based on Equation (1)). The shortest lines that are less than 5 m long are then dropped. This results in a reduction in the possible noise in the shadow map and significantly reduces computation time. Then, by applying a line extension (depending on the baseline shadow length) in the direction of the sun’s azimuth, a shadow line–object relationship is created based on the intersection. Once again, from the lines drawn for the object (shortened or extended depending on the position of the sensor), the height of the HBASEH. This is achieved by intersecting the translated building vectors based on the baseline height  with the shadow map. In the case of a vector derived from segmentation, this operation is not carried out because it represents an outline outside the roof or, in part, the roof and façade. Once again, cutting lines are created based on the reverse azimuth to the azimuth of the sun, along the edges of the buildings located on the sunny side. An intersection is then performed with the shadow map to create shadow lines. The maximum height value roofh is then determined (based on the shadow lines and Equation (1)) and added to the baseline height of the object. The final formula for buildings can therefore be rearranged as:BUILDINGH—total value of the building height;BASEH—base height of the object;roofh—additional height value from the analysis of objects located on the roof.In the next step, objects belonging to the “building” class are analyzed, and roof analysis is performed to add additional height elements to base height of the object This process, which was not commonly used before, allows for a more reliable estimation of the height of the buildings.After the global analysis, a detailed analysis is conducted through the iteration of objects that require individual approaches. These include, e.g., power towers, wind turbines, and masts. Wind turbines, due to their characteristics and moving parts, require the calculation of the base height and the length of the blades. An approach that consists of calculating the base height based on the shadow from the turbine’s location to the intersection point of the blades (rotation center) determined by computer vision methods is implemented. Then, using existing catalogs, the blade length value is added to the baseline height.Power towers and masts require individual approaches due to their often latticed structure, which makes it difficult to create a universal shadow model. To address this, a model was trained based on a modified UNet architecture specifically designed for segmenting lattice shadows. Using this model, the vertex of the lattice structure is identified in the direction opposite to the azimuth of the sun, allowing for the estimation of the object’s height.After the extraction of information and calculation of object heights, the process of auto-validation and correction of results takes place, as described in the previous chapter. Auto-validation aims to detect and reject objects that have unrealistically assigned heights, such as trees. Therefore, the maximum parameters for the height attribute for each class were set to reduce the risk of gross errors. This may occur especially in the case of a shadow map problem and the merging of shadows of several objects. Furthermore, the correction process uses the formulae described in ,15,35 toThe obtained heights are relative values, so in order to give them a global character, a value is derived based on the Digital Elevation Model, which is acquired from local, national, or global resources .To effectively sort the results and perform an accurate analysis and auto-validation, a PostGIS database was used, enabling fast processing of geospatial queries on large collections of object data. Each object comes with certain attributes, including a classified class and the polygon of its outline.Finally, vector layers representing individual objects along with their assigned class and height parameters are obtained. The schema representing the overall algorithm is included in In summary, the entire process begins with the normalization and standardization of input data to ensure their consistent format and compliance with the algorithm’s requirements. By selecting Ground Control Points (GCPs), the imagery can be matched with data from open sources. Next, the data are tiled, and satellite imagery metadata are loaded to initiate shadow and building segmentation on each subsequent tile. Additionally, for atypical objects, a solution for detecting intermittent and less visible shadows is activated. Subsequently, the most appropriate line passing through the potentially longest shadow segment is determined, allowing for the calculation of the object’s height, coordinates, and outline using metadata. Finally, integration with a database takes place to enable the analysis of a large number of obstacles. The resulting solution operates on the principles of a data lake.Data Normalization and Unification: The system begins by normalizing and standardizing the input data to ensure consistency and compatibility with the algorithm’s requirements. This step is crucial for accurate processing and analysis.Ground Control Points (GCPs): The system utilizes GCPs to match satellite imagery with data from open sources. GCPs help establish spatial references and align different datasets, enabling reliable analysis.Tiling: The data are divided into tiles, which are smaller, manageable sections of the overall dataset. Tiling allows for the efficient processing and analysis of large quantities of geospatial data.Shadow and Building Segmentation: The system employs segmentation algorithms to detect and distinguish between shadows and buildings in satellite imagery. This enables the identification and measurement of objects above the ground.Handling Atypical Objects: The system includes a solution for detecting intermittent and less visible shadows, which helps to accurately identify and analyze atypical objects that might be challenging to detect using conventional methods.Height and Geospatial Data Calculation: The system calculates the height, coordinates, and outline of the detected objects using metadata and other relevant information from satellite imagery. This allows for a comprehensive understanding of the identified objects.Integration with Database: The system integrates with a database to store and analyze the results of the analysis. This enables the processing of a large volume of obstacles and facilitates data retrieval and querying.Data Lake Operation: The system operates based on the principles of a data lake. A data lake is a centralized repository that allows for the storage of structured and unstructured data. It provides a flexible and scalable architecture for data processing and analysis.The main characteristics of the system are the following:In summary, the system is characterized by its ability to handle large geospatial datasets, accurately detect and analyze objects above the ground, and integrate with a database for further analysis and decision-making. It combines various image processing, machine learning, and data science techniques to create a comprehensive and automated solution for geospatial analysis.The training of models requires appropriate datasets. For both tasks, namely building segmentation and shadow detection, datasets were prepared. Both open resources, such as true orthophotos, and satellite imagery were used. Building vectors served as labels, obtained from local databases  as well as global sources like OSM.For shadow detection, an iterative approach was employed, where shadow masks were first obtained using computer vision methods and then manually verified and corrected.Additionally, available open datasets such as INRIA  were useThe input data for the datasets covered various different regions of the world and Poland. These included images obtained at both the aerial and satellite levels. Aerial imagery was primarily from services provided locally (by the NMA), while global imagery came from our resources and included World-View 2, World-View 3, and Pleiades Neo mission imagery.As mentioned in 1 = 0.9, β2 = 0.999, and ε = 10 − 7.The training process was the same for both tasks. It involved training for 50 epochs with a learning rate α = 0.001 and Adam optimizer parameters βThe accuracy of the trained network was evaluated using the mean Intersection over Union (IoU) metric. The Intersection over Union metric is calculated by finding the number of pixels where two objects overlap and dividing it by the total number of pixels covered by both objects.The obtained mean Intersection over Union (IoU) metrics for the test set were 0.851 for the building segmentation task and 0.946 for the shadow segmentation task.Below are the results of the model’s performance in various areas, as well as a comparison between the building prediction maps and vectors from open resources.The results obtained for building segmentation are presented in As the aim was to develop a universal algorithm that would be capable of conducting predictions on various images, of either aerial or satellite origin, the training data had to be adapted and normalized accordingly. The normalization process involved scaling the data from 0 to 255 and then further normalizing it to values ranging from 0 to 1 for the purpose of model training. This normalization procedure may result in the darkening or brightening of the images, which in turn can significantly impact predictions. This effect is particularly visible for objects with smaller footprints, those with more complex geometries, and those located in shadowed areas.The results obtained for shadow segmentation are presented in During the implementation of the algorithm, a problem was also identified with wetlands, water bodies, and water streams. This was due to the characteristic reflection of such objects, which are represented as dark areas that can be mistakenly interpreted as shadows by the algorithm. These objects were also eliminated during the post-processing stage to improve the accuracy of the segmentation results.The overall results of the deep neural network models’ performance are presented in This section presents the process of calculating the height of objects based on the method described in The input data for the calculations consist of vector representations of objects (from open databases as well as segmentation results) and a shadow map. In the initial phase, the shadow map undergoes pre-processing using visual morphological operations, and a vector representation of shadows is created to perform spatial analyses.Next, cutting lines are created according to the reverse azimuth to the azimuth of the sun. This process is illustrated in In order to evaluate the performance of the entire automated algorithm, the authors decided to compare the results obtained from satellite data with reference LiDAR data for the study area. The publicly available LiDAR point cloud has a density of 12 points per square meter and was acquired on 4 April 2018.ih is the estimated height of the object and The Mean Absolute Error (MAE) and the Root Mean Square error (RMSE) were chosen as accuracy metrics:The reference height values were obtained by creating a raster that represents the Height Above Ground (HAG) from the LiDAR point cloud. For each vector object, the highest point within its extent was extracted for comparison with the height values obtained from the satellite data.Initially, it was planned to automate the process of calculating the accuracy parameters by aligning the results from both datasets based on spatial parameters. However, due to the disparity in the acquisition time, this process had to be carried out semi-automatically with manual verification. This is because the Warsaw area is undergoing rapid development, and many new buildings are being constructed that did not exist in 2018 or were still under construction during that period. The preliminary RMSE obtained from the automatic process was 9.7 m.During the final evaluation of the results, the obtained values of MAE and RMSE were 4.66 and 5.65, respectively.The next step was the visual evaluation of the results. For this purpose, objects were grouped based on the difference in height between the determined height and the reference height. Four intervals were created: 0–3 m, 3–6 m, 6–10 m, and >10 m. The illustrations below present the images with the results, without considering the exclusion of objects that changed during the period from 2018 to 2020.The figure  below clThe developed algorithm is one of the components of the SAMPLE system (Automatic Air Navigation Obstacles Monitoring System) implemented by Creotech Instruments. SA  The SAMPLE system is used for the automatic measurement, processing, visualization, and export of data on aviation obstacles located in a selected area of interest. In addition to a component that utilizes satellite data, there is also the possibility of analyzing point clouds. The system enables the identification of objects that may pose a potential threat to air traffic, especially in airport areas.Images presenting the software interface are shown below .At the outset, it should be noted that the obtained results are satisfactory. Obtaining depth from a single image is undoubtedly a significant challenge that may pose certain difficulties in accurately estimating object height. However, it appears that the algorithm, apart from boundary cases (discussed in the previous chapter and further elaborated later), operates at a satisfactory level. The global accuracy of calculating the height of objects in the scene was ±4.66 m. The results obtained are comparable to other results in the literature, in cases where similar quantitative results are available ,49,53.The key component for the operation of the entire system is an image with the appropriate parameters and metadata that allow for the spatial identification of the sensor and the photographed area. Through empirical analysis and based on existing cases in the literature, it was noted that the image must also meet certain requirements regarding the aforementioned parameters. This primarily includes the position of the sun, which should not be too low (below 30 degrees) or too high (above 70 degrees). Low sun elevation results in a significant elongation of shadows, which can pose challenges to accurate height estimation. On the other hand, high sun elevation combined with a sensor positioned on the sunny side can lead to an inability to identify shadows in the image as they may be obscured by objects.One clear advantage of the presented solution is its modularity and the ability to adjust various parameters according to the specific imaging conditions . Modifying the models for better efficiency is not a hurdle. Additionally, there are no existing solutions in the subject literature that allow for the height estimation of virtually any object covering the terrain within a given area. It should be noted, of course, that for dense forest areas, height estimation will be limited to forest edges. However, this can still provide valuable information for the entire area, for example by providing the average value.Another advantage of the solution is its applicability. In addition to its ability to assess the detection of aerial obstacles, it may also be applied in urban planning and mapping of unmapped areas. Height determination may also be used for estimating the population living in a particular area.There are indeed possibilities to add additional modules, such as utilizing image pairs or SAR (Synthetic Aperture Radar) data. Multi-source verification could potentially generate better results and increase the reliability of the obtained height estimations.On the other hand, the authors are aware that the developed system has certain limitations. Firstly, it requires images and imagery with specific parameters (as described earlier). Additionally, in densely built-up areas, there may be difficulties in accurately estimating heights due to shadow occlusions on objects or overlapping shadows. Very tall objects and large radial displacements may also introduce errors or make it impossible to determine the heights of objects.An important aspect that should also be addressed is the accurate and precise georeferencing of the imagery. Precise georeferencing is particularly crucial when using vector objects from open datasets. In cases where there are displacements, the results may be distorted and lead to an unnatural elongation or compression of objects.Another element is the normalization of satellite images to represent a more realistic RGB image. The normalization technique used can potentially be replaced with style transfer solutions, which could lead to even better results.The study indicates that the developed automatic algorithm may be used in preliminary works related to height estimation of buildings. However, for more precise measurements, when the required accuracy is below 1 m, direct measurement methods like geodetic techniques or calculations based on point clouds are necessary. Additionally, during the research, requirements regarding the algorithm’s correct operation were identified, which are related to sensor positioning and sun orientation.The solution presented in this article allows us to conclude that the analysis of elements from various sources in an image can expand the dimensionality of the obtained data. This, in turn, opens up significant possibilities for spatial analysis using individual imagery.The authors identified further possibilities for improving the algorithm by creating relationships between objects to assess their mutual influence. They also identified the need for further research to expand the applicability and accuracy of solutions based on single images, as well as attempts to increase the dimensionality of such data.In the context of describing future work, it is possible to perform a detailed analysis of roofs based on the shadows present on buildings, to generate three-dimensional object models at a defined Level Of Detail (LOD), possibly even LOD 3. This approach may further increase the applicability of the solution, for example, in selecting suitable objects for the installation of solar panels.


Finally, the results were triangulated with various experts, and the critical points were classified in terms of their potential to diverge from consumers’ expectations. Our results show a high dependency of the organic sector on the conventional breeding process and its specialized breeds. This fact involves critical points which contradict the ideology of organic farming. The loopholes in the organic EU regulations in transport and slaughter were identified as additional threats to consumer trust in the organic system. The overall not better animal health compared to the conventional poultry system and the high numbers of poultry kept on some organic farms are also possible causes of disappointment in consumers’ vision of organic livestock farming. Therefore, we recommend an adjustment of some organic EU regulations regarding these points. Further, a linkage of the organic certification of a slaughterhouse to higher animal welfare standards during slaughter should be considered.Over the last decade, there has been growing societal concern about the welfare of farmed animals. Although organic agriculture provides higher living standards, there are still critical points which can damage consumers’ trust in organic livestock farming. That is a risk, as especially organic farming relies on consumer trust. A hotspot analysis was conducted to identify critical points within the organic laying hen husbandry in Germany. This methodology aims to examine the sustainability of a product along its whole life cycle. Based on literature reviews, the life phases  Over the last decade, there has been a change in the society regarding how animals are perceived, and thus the expectations of how animals should be kept and treated have increased  is of high importance for the organic sector in order to meet its own and society’s demands for more sustainable animal husbandry and to justify the trust placed in the organic industry and therewith keep the consumers’ willingness to buy organic products in the long term. The hotspot analysis (HSA) conducted in this study investigates and evaluates the sustainability of organic laying hen husbandry along the entire production process by means of a literature review , and the criterion raw material was changed to breeds/genetic lines used including the breeding process. As animal welfare is an especially important criterion for organic poultry farming, it received its own category. Special attention was paid to it because animal welfare presents a variable with high relevance to assess agricultural sustainability : scientists (professors), practitioners , and opinion leaders . All experts worked for several years in their field. Table The evaluation of the expert interviews was performed by a qualitative content analysis. A short summery of the main statements is given at the end of the results chapter.First, each life phase will be described, and their rating will be discussed briefly. The identified hotspots will be described according to the life phases they belong to. Hot spots with high ratings  get a more detailed description than hot spots with lower ratings (6). The ratings of the hot spots are displayed after the respective criteria.breeding received a rating of high relevance (3) because breeding impacts several areas and affects the animals’ life to a high degree.Poultry breeding is a highly specialized process. Modern meat and egg production are characterized by the use of high-performance hybrid breeds Cheng . Therebybreeding, which are described below:Breeds/genetic lines (6): Dependency and monopolization in animal breeding (Idel ing Idel . In receing Idel .Land use (6): Slow-growing lines and dual-purpose breeds are less resource-efficient (Schmidt and Bellof Biodiversity (9): In the life phase breeding, biodiversity refers to the genetic diversity of different breeds and lines as well as the genetic variability within the breeds : The use of conventional high-performance laying hybrids in organic farming leads to a by-product, male chicks, which are commonly killed immediately after hatching : An ethical animal welfare issue in laying hens is the killing of male layers as described above. Further, the alignment of the breeding objective which is strongly focused on performance traits leads to breeding-related health disorders for laying hens : The higher demand on resources is caused by lower yields : The use of high-yielding breeds places greater demands on the management of feeding and keeping conditions in organic farming (Le Bris  Le Bris . Outdoor Le Bris .Education and training (6): Health management is crucial for animal health. Therefore, education and training are of great importance in this area : “Animal health” represents an important criterion in animal welfare. Diseases and injuries are usually accompanied by suffering and represent a serious impairment to animal welfare. Despite the preventive principle in organic farming and the more animal-friendly husbandry system, studies show mostly the same and, in some areas, a slightly inferior and in others a somewhat better health state in organic poultry farming . The regulations refer to the required space during transport, its length, and the transportability of the animals. For national transport, the duration should not exceed eight hours. Long-distance transport allows durations of up to 24 h without a break : There are often deficiencies in the transportability of the animals and the planning and management of the transport. Prerequisites for adequate transportation in terms of animal welfare include, for example, the animals’ health condition at the beginning of transportation; the management, such as the planning of the route; and the suitability and maintenance of the transport vehicles are also important. In practice, there are often deficiencies with regard to these influencing factors (von Holleben and von Wenzlawowicz Education and training (6): Good knowledge of animal behavior is essential for animal-friendly handling during transport : Injuries and stress during transport can affect meat quality : In connection with transport, there are a series of animal welfare problems. The catching and loading of poultry is a very stress-related part of transportation . Nevertheless, animals that are not fit for transport are frequently transported regardless of their constitution  rate. This rate should not exceed 0.2% Rabitsch –0.5% Ho of the tslaughter includes the arrival of the animals at the slaughterhouse, and their stay in the waiting area, as well as the stunning and killing process.The life phase Tierschutz-Schlachtverordnung) apply to organic animals. This regulates the handling and care of animals at the slaughterhouse, as well as the use and application of stunning methods. For poultry, stunning by means of electric baths, electric forceps, and CO2 stunning (except for waterfowl) is permitted : Poor maintenance of the equipment and training of personnel lead to animal welfare problems during slaughter : The water consumption occurring in the slaughter and subsequent further processing can be described as high (Westfleisch Education and training (6): Most animal welfare problems at slaughter are directly related to human activity. Despite the required certificates of competence, slaughterhouses often disregard animal welfare regulations, and stunning techniques are applied improperly (Fötschl  Fötschl .Working conditions (9): Working conditions in the German slaughter and meat industry have long been criticized : Stress and injuries during the slaughter process reduce meat quality : Animal welfare problems occur in all areas in connection with slaughtering. Serious violations of animal welfare exist from the delivery of the animals, and their handling while being brought to the stunning facilities, as well as during stunning and bleeding . Reasons given for this were that the minimum standards of the EU regulations for organic animal farming are still too low for a good animal welfare level and also do not include regulations on herd and group sizes .Overall, the hotspots found through the literature research coincide well with the experts’ opinions. Experts also saw especially the upstream and downstream areas as hotspots and as possible risk points to not meet consumers’ expectations . There were lighter discrepancies in the ratings of the life phases Three experts  stated that they would rate the area of animal health somewhat lower. They noted that it should be differentiated by which health concept is referred to (I9). A light parasite load cannot be considered a disease and belongs, to some extent, to a natural husbandry system (I8).There were no major discrepancies among the answers provided by the various expert groups .Experts see in particular the upstream and downstream areas (breeding and transport/slaughter) as critical areas for loss of trust due to grievances and lack of specific organic standards . Regarding husbandry, especially stock sizes  and animal health  are seen as a risk for the loss of consumer trust in organic livestock production. In breeding, particularly the killing of day-old male chicks of layers can disappoint consumers . However, it was also highlighted that most consumers have very little knowledge on this topic . Almost all experts rated the areas of transport and slaughter as a possible trust hotspot . The general grievances in this area pose a particular problem for organic livestock production, as it cannot distinguish itself from the conventional system here, and consumers could expect better conditions . Further, it was mentioned that it is difficult and costly for the organic sector to enforce its own standards in this area, as it is a structural problem . Here, too, consumers are often not aware of the grievances . Some experts also considered reports on poor animal health in organic farming to be a possible risk to consumer trust . With regard to husbandry, management deficiencies (I3) and the large poultry stocks  on some organic farms were seen as risk points for a loss of consumer trust.breeding and slaughter show a number of problematic areas. There are also some weaknesses in the life phases animal health and transport. In contrast, the life phases keeping and feeding show no or only single hotspots due to their low rating with regard to sustainability weaknesses. Nevertheless, there are also ecological deficits and challenges in animal welfare in these life phases.The results show that especially the life phases keeping itself received a low rating.In the following, an initial classification of the life phases with their sustainability weaknesses is made with regard to a potential risk of losing consumer trust. However, a distinction must be made here between the hotspots in the area of sustainability and a potential trust risk. Some hotspots, such as high-water consumption and water pollution during slaughter, can be considered a low trust risk compared to animal welfare hotspots, due to a lower emotionality of the topic. However, the partly large farm structures of organically raised poultry of up to 30,000 animals can entail a high potential for the loss of consumer trust, even when the life phase Generally, breeding is a problematic area in organic animal husbandry that needs to be discussed. The use of one-sided specialized breeds is difficult to reconcile with the basic idea of a holistic approach. From a social point of view, highly specialized poultry breeding also poses problems in terms of power distribution and access to animal genetic resources, and genetic diversity. On the other hand, the existing problems in this field must always be seen under the given framework conditions since there is often a lack of economically profitable alternatives to the specialized breeds in organic farming. In addition, it is not easy to implement one’s own demands in practice, since, for example, dual-purpose chickens are currently only of small value on the market  in this HSA, it does hold potential for a loss of trust. The more animal-friendly keeping conditions in organic farming can lead to high expectations among consumers. If deficiencies in animal husbandry occur in practice, for example, due to poor management, this can lead to consumer disappointment. Further, the requirements for a housing system that is particularly oriented to the needs of the animals, with more space and outdoor access, carry some challenges and dangers in keeping the animals healthy, such as predators and an increased risk of disease . But there are some promising approaches to circumvent existing problems, like the Ökologische Tierzucht GmbH, RegioHuhn, ÖkoGen or alternative slaughter methods. Further, organic agriculture is leading the way in the area of management control with the development and application of animal-related indicators to measure animal welfare , research for organic farming should also be promoted more strongly. Compared to conventional agriculture, there is a high research deficit here with regard to suitable varieties, cultivation methods, husbandry practices, and technology.Limitations of this study are, in particular, the authors’ subjective perspective regarding the evaluation of the life phases and criteria on the basis of a literature review. Although this is reduced by a systematic approach and by supplementing the results with expert interviews, it can never be completely eliminated. Due to the heterogeneity of the object of investigation, only a very general overview of existing problem areas in organic poultry farming can be provided. Finally, it must be considered that not all hotspots exist to the same degree on every organic farm, and for some, they do not exist at all.


Number of lost occlusal contacts is not in correlation with masticatory efficiency parameters . Conclusions: Children with lost antagonistic contacts have an impaired masticatory efficiency in comparison to children with complete dentition, but there is no difference regarding the aetiology of contact loss.Background: Tooth extraction, changing dentition and malocclusion can decrease area of occlusal contact and negatively affect masticatory efficiency. Aim of this study was to evaluate difference in masticatory efficiency in association with previously named factors. Materials and methods: In this cross-sectional study masticatory efficiency parameters  determined with optical scanning method were compared between children with healthy dentition  and children with lost antagonistic contacts due to tooth extraction, changing dentition and malocclusions . Results: Number of chewed particles is significantly higher in a group of children with healthy dentition ( One of the basic prerequisites for oral and general health is the masticatory function . The greThe mastication apparatus as a part of the stomatognathic system consists of four different tissues, four major components: bones, muscles, teeth, and soft tissues. With mastication begins a complex process of processing food, that is, food is prepared for swallowing, digestion, and absorption of nutrients. It implies a combination of cyclical and learned reflex jaw movements that enable the mechanical processing, crushing of food. During the act of chewing, forces are transferred from the teeth to the food bolus, whereby mobile soft tissues  and facial and masticatory muscles position the food bolus between the teeth maximizing the mechanical breakage of the food. Muscle growth, strength, and coordination are needed not just for positioning the food bolus between teeth but for applying forces through bones (upper and lower jaw) and teeth onto the food bolus. Alveolar processes of jaws provide anchorage for erupted teeth which come in contact with food and transfer the muscle forces. The different occlusal surface of different teeth ensures the right direction of forces for the breakage of food particles that are dissolved and moistened with saliva, forming the appropriate food bolus. Saliva also contains enzymes for food decomposition. Masticatory apparatus, with all its compounds and its proper growth and development, enables proper masticatory performance, and processing of a wide variety of foods ,2.The process of food ingestion (solid and semi-solid food) is described in four steps . The food ingestion process can be stopped at each of the three checkpoints .After food choice and selection, if the process passes the first checkpoint (food selection before ingestion), food is being put in the mouth. The first checkpoint is one’s acceptance or refusal of given food based on individual preferences. After selecting the food and passing the first checkpoint food portions are transported from the frontal to the lateral teeth which represents the first step of ingestion . The second checkpoint is the oral sensory analysis of the food. From the olfactory, gustatory, and oral receptors of the somatosensory system nerve impulses are transmitted to the cerebral cortex and truncus encephali. The cephalic phase is then activated, and the preparation of the digestive system for food digestion starts. The cephalic phase also continues during Stage II. Stage II, proper mastication, includes common activities of masticatory apparatus  to create a proper food bolus by placing food within the occlusal surfaces of the teeth, and with the aid of saliva. In the third stage, food bolus is being transported more orally toward the esophagus and stops at the pharyngo epiglottic folds. The second and third stage are not happening separately one after the other but they exchange cyclically, food is moving from the folds through the fauceus isthmus to the places between teeth, tongue, and cheeks to be crushed. This continues until the food bolus is properly formed which is the positive signal for the third checkpoint. Adequate food bolus, passed third checkpoint, activates Stage 4, swallowing of the food. With deglutition, food is transported from the oral cavity to the pharynx and then the esophagus ,7. It is evident that the food bolus properties are essential for food deglutition, controlled by the third checkpoint. Food bolus must be slippery, cohesive, and plastic which is achieved in the second and third stage of food ingestion. Chewed particle sizes are one of the determining factors for reaching the necessary properties of the bolus and consecuntelly safely swallowing the bolus. Peyron et al. Have shown that deglutition starts only when the required size of the chewed particles is being achieved . To formThe masticatory function can be assessed by objective methods  and subjective methods oriented to the patient’s experience (questionnaires). The most frequently used objective methods are the measurement of muscle activity (masticatory force strength and electromyography) and the measurement of masticatory efficiency. Masticatory efficiency (ME) is defined as the individual ability to grind food, whereby a smaller surface area and a larger number of particles imply better ME. It can be measured by colorimetric and spectrophotometric techniques, scales for visual assessment, sieving, optical scanning of chewed particles, etc. ,11.Sieving implies fractionation of the sample through a set of sieves of precisely defined diameters and has long been considered the gold standard in the measurement of ME. However, the development of computer technology enabled the development of the method of optical particle scanning, the main advantage of which is the ability to precisely measure the dimensions and determine the shape of each particle in the sample. In addition to precision, a great advantage is the speed and simplicity of sample processing compared to the sieving technique . Today, Different test foods can be used to evaluate ME, most often gelatin, peanuts, carrots, and almonds. Since natural food can vary significantly in physical properties and composition and has high water content, since the 1980s, the material for impressions based on condensation silicones has been considered the standard. The advantages of silicone materials are dimensional stability, easy shaping of test samples of standard dimensions, appropriate hardness, and strength. The lack of smell and taste of silicone materials can be considered as a disadvantage compared to natural foods. However, the smell and taste of the test food can affect the patient’s mastication depending on individual preferences, and this is another reason why the silicone material chewing sample has become a standardized procedure .It is believed that the premature tooth loss in children can have various negative consequences: changes in occlusion and mastication, speech disorders, development of bad oral habits, loss of space for permanent successors and consequent malocclusions, impairment of aesthetics, reduction in quality of life and altered psychological condition of the individual ,16,17.Neuromotor control of chewing depends on occlusal contacts between dental arches and receptors in the temporomandibular joint, periodontium, and dental pulp. Therefore, any condition that affects the structure and position of the teeth can have an impact on mastication. Masticatory efficiency can be influenced by the state of dentition, precisely the number of teeth and occlusion contact area . Tooth lThere is a small number of studies that have investigated the relation of the reduced number of antagonistic contacts to ME in the younger population. In order to contribute to the understanding of the connection between these factors in the child population, this research was carried out. The aim of this study was to examine the association between ME and the reduced number of antagonistic contacts due to tooth extraction, malocclusion, and changing dentition in children.This study included 48 children aged 3 to 14 years of both genders  in different stages of growth and development. Participants were children with healthy dentition and those with a reduced number of antagonistic contacts due to physiological tooth exfoliation, tooth extraction, or orthodontic anomalies. The sample from the population was obtained by including patients who came for a dental visit at the Pediatric Dentistry Department of the Clinic for Dental Medicine of Clinical Hospital Center Rijeka from May 2021 to November 2022.Only children whose parents/legal guardians gave informed consent to participate in the study were included in this research.tooth extraction due to caries (subgroup 2a),orthodontic anomalies (subgroup 2b)or dentition change (subgroup 2c).Participants were assigned to one of the two groups. The first group included patients with healthy dentition and preserved all antagonistic contacts. The second group included children with bilaterally lost antagonistic contacts in the posterior segment due to: In between the two groups, children were matched by gender and age.Children with non-cooperative behaviour; pathological changes of the temporomandibular joint, masticatory muscles, periodontal tissues, or any other structure of the stomatognathic system were exclusion criteria for participants, as well as drugs and diseases that affect neuromuscular functions ,19.n = 8, 4 per group) was calculated to achieve a power of 80% and a level of significance of 5%, using data from the research by Kreulen et al.  [Sample size  according to the Protocol for standardized production of artificial test food by Albert et al. and packed in an impermeable 0.25 mm latex membrane [Participants chewed the standardized artificial test food that was previously prepared from Optosilmembrane . ArtificChewed particles were spread on a black photographing background . StandarExcept for the assessment of ME via optical scanning method, each participant was examined during their dental visit. Dental status and data on aetiology and time of tooth loss were collected. Antagonistic contacts were evaluated by a 12 μm thick dental articulating paper. Also, the number of preserved and lost antagonistic contacts was determined with the recorded reason for contact loss .ClinicalTrials.gov Database under the number NCT05000385.The research was approved by the Ethics Committee of the Clinical Hospital Center Rijeka, the Ethics Committee of the Faculty of Dental Medicine and the Ethics Committee for Biomedical Research of the Faculty of Medicine of the University of Rijeka. Also, the research is registered in The difference in ME parameters  and the time interval between the two groups was tested using the Mann-Whitney test. Also, differences in ME between different aetiologies of antagonistic contact loss were tested with the Mann-Whitney test. The correlation between the number of lost antagonistic contacts and ME parameters  was tested with Spearman’s correlation. α value of 0.05 was considered statistically significant. Statistical analysis was done using the SPSS 13.0 software .p = 0.992 independent sample t-test), and groups had the same number of male (12) and female (12) participants. Data on age, gender, dentition and number of extracted teeth of participants in both groups is shown in There was no statistical difference in age between the two groups .The time interval for 20 masticatory cycles was not significantly different between the two groups , and chewed particles’ mean diameter and surface are significantly higher in Group 2  . The number of chewed particles is significantly higher in a group of children with healthy dentition . Also, there is no correlation between the time interval and the number of antagonistic contacts (p = 0.079). Participants in Group 2 had on average 5.83 ± 2.55 missing antagonistic contacts . There is no statistically significant correlation between the number of antagonistic contacts and masticatory efficiency; particles number, diameter, and surface (p = 0.121), diameter (p = 0.121), and surface (p = 0.139). When comparing masticatory efficiency variables between different aetiologies of lost antagonistic contact, there is no significant difference in particle number  in a group of children with a decrease in the number of occluding teeth (Group 2). The Masticatory Normative index, MNI is defined as a chewed particle diameter of 4 mm. If the chewed particles’ diameter is 4 mm or smaller it is considered that the masticatory function is adequate . The meaBigger chewed particle diameter in participants with less occluding teeth can be compensated by a bigger number of masticatory cycles, masticatory force change, and jaw movement change. With the bigger number of masticatory cycles before swallowing children with loss antagonistic tooth contacts can potentially reach a mean particle diameter smaller than 4 mm, achieve adequate plasticity, cohesiveness, and slipperiness of food bolus, and still have an unobstructed start of digestive function. But, if they do not increase the number of masticatory cycles sufficiently and do not reach a diameter of less than 4 mm, meaning that the food bolus does not reach requirements for deglutition their masticatory and consequently digestive function is impaired . ReducedPrevious studies have shown that the masticatory efficiency is in correlation with the number of occluding teeth ,29, and These results on reduced masticatory performance in participants with missing occluding units are supported by other studies that were conducted on adult participants and that have shown a statistically significant increase in chewed particle sizes in participants with a fewer number of occluding teeth ,33,34. SOrthodontic anomalies are considered one of the factors that can affect the masticatory process. A lower ME has been demonstrated in patients with an open or crossbite compared to patients with normoocclusion, and in patients with Angle class II or III compared to class I patients ,36. In tIn addition, the question arises about the ME during physiological changes—tooth exfoliation and eruption when all antagonistic tooth contacts are not present but there are no pathological changes in the stomatognathic system. Does the reduced number of antagonistic contacts during changing dentition correlate with reduced ME or does the stomatognathic system adapt to this physiological process? In this research, there was no difference in ME of participants with contact loss due to changing dentition in comparison to ME in children with antagonistic tooth contact missing due to extraction or missing occlusal contacts due to malocclusion. However, when analyzing the chewed particle sizes in Group 2 it is evident that children with a decreased number of occluding teeth due to pathological changes  have a mean chewed particle diameter higher than the MNI  while children with contact loss due to changing dentition have a mean chewed particle diameter smaller than the MNI (2.77 mm). So, although there was no significant difference in ME between different aetiologies when looking at the mean chewed particle diameter it is evident that children in a phase of changing dentition have adequate mastication while children with extracted teeth and orthodontic anomalies that reduce the number of occluding teeth have impaired mastication. Once again, children with extracted teeth and orthodontic anomalies that reduce the number of occluding teeth can compensate for this reduced chewing efficiency in several ways. These findings suggest that tooth exfoliation and eruption, as physiological processes, do not need compensatory changes to achieve proper food boluses. The main limitation of the study was the small number of participants for the ME comparison in Group 2 based on different aetiologies. Therefore, further studies should be conducted on ME in children with a reduced number of antagonistic contacts, but with taking into account different aetiologies of contact loss.To conclude, children with a reduced number of antagonistic contacts have a significantly reduced masticatory performance in comparison to children with full healthy dentition. Reduced ME is not in correlation with the number of antagonistic contacts and does not differentiate between different causes of contact loss.


The goal of this paper is to better understand the importance of a long-erased border between two empires that once partitioned Poland and its ongoing effect on regional educational achievement. Previous research has indicated that inherited norms towards education may explain the observed gap in the achievement of students in the former Austrian and Russian partitions of Poland. Findings suggest that although a gap in favour of the former Austrian partition does indeed exist in four school subjects, there is no convincing evidence of the causal effect of historically rooted norms on the achievements of today’s students. If such an effect exists, it is probably indirect and works through the accumulated educational attainments of adult generations and quality of instruction rather than by directly influencing the attitudes of today’s students. Previous research [Present-day differences between territories are sometimes difficult to explain by referring only to the contemporary factors that may have caused them. Instead, these discrepancies seem to reflect divisions created by long-forgotten borders between territories that are now constituent parts of the same state. The goal of this paper is to better understand the impact, if any, on regional differences in educational achievement today of a long-erased border between two empires that once partitioned Poland, namely, the 19research  identifiThe main research question of the present study is therefore as follows: what are the exact channels through which social norms have affected educational outcomes in two of Poland’s historical partitions? In particular, this investigation brings us closer to understanding whether the observed differences in educational attainment should be attributed to a direct influence of social norms on students’ efforts and commitment to learning or whether we should think of a mediated and staggered impact–one contributing over decades to the divergent levels of more obvious, contemporary inputs into the educational quality in the two regions. Naturally, a sharp separation of direct and indirect effects of norms in an empirical study is not feasible. However, by adopting a nuanced methodological approach and using previously unavailable data, I provide new evidence and shed more light on the role of historical partitions in determining present-day educational quality. Although the present study confirms the existence of a gap in favour of the Austrian partition in terms of achievement in four different school subjects, no strong evidence is found to support the hypothesis of a direct influence of social norms on educational achievement. In this sense, the results of the analysis are not fully consistent with the interpretation presented in previous studies on this issue.In terms of methodological approach, I propose several enhancements to previous studies investigating the persistent effect of the partitions on today’s gap in student achievements between the regions of Poland. All previous studies have relied on educational achievement statistics aggregated to the level of municipal averages –4. The lIn the present study, achievements are measured at the school, rather than the municipal, level, thus increasing the number of observations and the statistical reliability of results. Furthermore, I go beyond using a single measure of achievement on the basis that if the divergence of educational outcomes between regions is determined by cultural norms towards education, then it should manifest itself at different stages of students’ educational careers and with respect to different subjects taught at school. However, depending on their nature, these norms may also have distinct effects on students’ attitudes towards particular subjects.Importantly, while I use several alternative measures of educational achievements, they all pertain to the achievements of the same group of students, and this allows direct comparisons without possible bias due to the change in the composition of the sample.I also test the largely overlooked hypothesis that the observed gap in achievements may reflect a difference in the quality of instruction that is uncaptured by the measurable inputs. Thus, I also apply a value-added approach using students’ test scores from the earlier educational stage.Finally, I address the norm towards education more directly than in previous studies, using newly available data on the voluntary enrolment of six-year-old children in schools collected during the unsuccessful attempt made between 2009 and 2016 to lower the age at which children start school in Poland.The remainder of this paper is organised as follows. Section 2 reviews the literature on how former institutions may affect different aspects of socioeconomic development, including education. Section 3 introduces the historical background of the research, explaining the link between the history of Poland in the period 1795–1918 and the institutional diversity of today’s Polish regions. This section also reviews some earlier studies on the effect of the partitions on educational quality in Poland. Section 4 describes the identification strategy, including the model specification and data used in the analysis. It also explains in detail the role of alternative specifications and the sensitivity analysis I use. Section 5 presents the results of estimations, starting with a replication of Bukowski’s [th century was largely due to the strength of domestic institutions predating the era of Atlantic trade. The rise of European empires in the following centuries led to the emergence of a colonial order. Its institutions proved, in turn, to have long-term effects on the colonised regions, contributing to the persistence of socioeconomic differences between now-independent countries with divergent colonial experiences [Differences in the historical development of institutions may be responsible for the divergent development of countries and the regions within them. In their seminal work, Acemoglu et al.  argue theriences –9.History manifests itself through economic indicators, but also in people’s attitudes and the choices they make. Norms are persistent because they are transmitted between generations in the process of raising children or within social groups ,11. HoweLike many other aspects of socioeconomic life, education has been studied in the context of the institutional differences that emerged as a consequence of the colonial division of the world. Research has shown that former British and French colonies have tended to invest more in education than other once-colonised nations since gaining independence . Howeverth century, academic achievement is much higher in the north, in line with differences in per capita income [The projection of historical borders on the maps of educational quality is observed on every continent. In Italy, a country that was unified in the mid-19a income .th century, which happened earlier in industrialised countries than in agricultural areas. According to some interpretations, physical capital accumulation in the process of industrialisation enhanced the importance of human capital in production and generated incentives for capitalists to support the provision of public education for the masses, triggering the demise of the existing class structure [The positive correlation between the level of economic development of a territory and its educational performance may be considered in the context of the formation of public education systems in the 19tructure . Other structure  notes thThe sociological and economic literature indicates that the most likely channels through which historical processes may affect today’s academic performance are the reproduction of parental educational attainment ,24, qualIn terms of individual motivation, the norm towards education can be related to a student’s identity, their exposure to social pressure, and the perceived importance of education in realising life projects. Depending on which channel is dominant, the attitude towards education may persist between generations due to parental efforts, social norms prevailing in the neighbourhood, and peer pressure experienced in schools ,33,34.Finally, the attitude towards education may have more to do with trust in institutions in general than in schooling in particular. The relevance of trust in institutions, as an important category differentiating the societies in Central and Eastern Europe, has already been demonstrated by Becker et al. , who fouPoland is a valuable case for research on the impact of history on socioeconomic development. The first reason for this is its turbulent history. Between 1772 and 1795, following a series of military defeats, Poland was divided by its three neighbouring powers, Russia, Prussia, and Austria. This division took place in three stages and, in 1795, involved the final remnant of independent Polish territory. Until the reunification of Poland in 1918, the three regions were exposed to very different political and administrative cultures and experienced very different rates of economic growth, with the territories under Russian rule being generally less advanced economically and lagging in terms of the development of modern social and political structures.Indeed, research shows that territorial differences in many socioeconomic phenomena in today’s Poland clearly reflect the historical partitions. For example, the former borders are reflected in the political choices of Polish voters. Voters in the post-Prussian territory, and in the west of Poland generally, tend to be more pro-European and liberal than those in the post-Russian region, which, in turn, is more conservative –38.Some studies on the persistent traces of the empires that previously ruled Polish territory refer directly to education and student achievements. All these studies were carried out after the introduction of countrywide standardised tests in Poland in 2002 and used the test scores as the main endogenous variable. Nonetheless, these studies are quite differentiated in their approaches and methods. The earlier works, which used the first round of school tests, relied mostly on descriptive and cartographic analyses ,40. Lateth-grade test in mathematics and science to demonstrate (in the RDD framework) the persistent gap in educational achievements between the former Austrian and Russian partitions. In contrast, the author found no difference in educational achievements between the former Russian and Prussian territories. Bukowski argues that the historical foundation of individual identity might be crucial to understanding this phenomenon. In the Austrian educational system, the positive framing of Polish identity (including the use of Polish as the language of instruction) might have created a positive social norm regarding education among Poles. Conversely, in the Russian system, Polish identity had a negative framing and thus might have led to either a neutral or a negative social norm. Bukowski finds partial confirmation of this hypothesis in general surveys containing education-related questions.Since the paper by Bukowski  was a keSummarising the existing research on the geography of educational quality in Poland, most authors observe that the former Austrian partition outperforms other regions of Poland in terms of academic achievements, as measured by the standardised school tests –4,39. Soth-century Austrian schools, as opposed to schools in other partitions [Although the higher quality of education in the former Austrian partition is commonly recognised, there is little understanding of the mechanisms behind this phenomenon. The channels whereby regional differences in educational achievement have persisted in Poland offer a challenging and unsolved research puzzle. Past studies elaborate on possible explanations, pointing out regional differences in monetary returns to education ,3, the prtitions , and thertitions .The major problem with the existing evidence on a distinct social norm towards education in southern Poland is that it is not directly linked to data on student achievement; thus, it is difficult to assess the extent to which the norms explain the observed achievement gap between the former partitions. The attempts to verify the causal relationship amount to either qualitative insights or indirect quantitative analyses based on circumstantial evidence.th century, this does not necessarily mean that these norms must have affected the educational choices of subsequent generations. After all, individuals in each generation since the partitions were affected have been making concrete educational decisions that, in turn, have incrementally led to differences in the resources that shape students’ achievements and the norms they profess today. Thus, it is not only easy to mistake cause and effect but also to overestimate the direct contribution of norms. Indeed, the achievement gap may reflect entirely typical determinants of academic performance, such as parental educational attainment or a dominant career model in the peer group. Although these factors that may have been gradually determined by historically driven norms, but their effect on contemporary student achievements is straightforward and not strongly related to students’ beliefs.It is important to bear in mind that even if the partitioning of Poland resulted in divergent norms towards schooling in the 19th century might have led to higher educational attainment in consecutive generations. The better achievements of today’s students may therefore be due to the well-studied phenomenon of the reproduction of parental socioeconomic status.In sum, according to the existing literature, there are several channels through which the historical divisions of Poland may cause a discrepancy in student achievements across the former partitions today despite the fact these territories were recovered by Poland a century ago. Some channels are rather direct in nature: that is, better access to education and its positive framing in the 19Other potential channels are, however, less obvious: the divergent historical experience of Poland’s partitions might have contributed to the development of a different social norm towards education–one that manifests itself in how much parents, independent of their own educational credentials, trust educational institutions, how much effort students exert while taking low-stakes tests at school, or, finally, the importance attributed to education by local politicians and administrators and the portion of public resources spent on it.A useful analogy addressing the nature of such ‘norm-mediated’ effects of past events can be found in the literature on the evaluation of public policies. Angelucci and Di Maro  distinguAn additional explanatory lead that has been largely unexplored in previous research is related to differences in the quality of school instruction. It is worth emphasising that compulsory schooling in the Austrian partition was introduced in 1869 (as in the rest of the Austrian empire), while education in the former Russian partition did not become compulsory until 1918, when Poland regained its independence. The Austrian partition not only had a longer tradition of schooling but also more experience in teacher training, which took place at universities in Lviv and Krakow. Unlike in the Russian partition, teachers in Galicia  were recruited from the Polish population, as Polish was the main language of instruction . All theIn the next section I address all these channels while presenting a conceptual framework of sensitivity analysis.As the first phase of my work, I measure the achievement discrepancies at the former border between the Austrian and Russian partitions. Like Bukowski , I use aThe basic equation estimated within the RDD is:Smsi represents the dependent variable, namely, the average combined score in mathematics and science achieved in the 9th-grade test in 2015 in middle school i. In this first specification, for the sake of comparability with Bukowski’s [Di is a treatment variable (denoting the former partition), and disti is the distance from the historical border. I estimate Eq , but it is weighted by school size (number of students); thus, larger schools have a larger impact on the outcomes. The exact locations of schools included in the sample (within a 50 km bandwidth from the border) are shown on the map in As measuring the change in levels of student achievement at the former Russian–Austrian border is, to a large extent, a replication of the earlier analysis by Bukowski  althoug.In the further stage of analysis, specification (1) is expanded and then estimated for different alternative dependent variables to test potential explanatory channels. All estimations are performed within the RDD framework with the same bandwidth around the former Austrian–Russian border as applied in the primary estimation.th-century empires concerning the cultural and political autonomy of the Polish population [Previous research suggests that the persistence of an achievement gap between Poland’s former partitions may be related to divergent norms towards education that emerged as a consequence of different policies administered by the 19pulation . Althougpulation , the dirNorms towards education can manifest in many different ways. Therefore, I use a nuanced strategy of testing, as described below.th-grade test administered in 2015, we can separately measure students’ scores in mathematics, science, Polish, and history. If the gap in educational achievements between former partitions is caused by long-established norms towards school education, it is it is reasonable to assume that students in different partitions score differently in all these subjects. However, the magnitude of the partition effect may not be perfectly symmetrical, depending on the nature of the underlying norm. Bukowski [In the case of the 9Bukowski  noticed th century may have resulted in a higher level of trust towards educational institutions than that seen in the other two partitions of Polish territory [Previous studies suggest that the Austrian approach to developing public education in the 19erritory ,3. It iserritory . In thisThe uneven importance attached to education can also be seen in the level of resources spent on providing it to the public. Norms may shape policies in this matter, as the decisions taken by the relevant authorities need to follow the preferences of the voters. Thus, one would expect the positive norm to be associated with a higher level of funding. Therefore, I use individual data on the wages of teachers to test whether students’ achievements change at the former border between the partitions. By doing so, I exploit the Polish regulations that require local educational authorities to pay teachers a certain minimum wage  but give them the discretion to set the wages above these minimal levels .th-graders in 2012 were only tested on mathematics and Polish, I constructed a best-matching dependent variable, namely, the combined score from these two subjects as achieved in the 2015 9th-grade examination.In the final step of the analysis, I address a hypothesis that has been largely neglected in the earlier research, namely, that the gap in educational achievements observed between the two historical territories is related to the difference in the effectiveness of school instruction . Measuring the effectiveness of schooling has always been problematic because of the obvious difficulty of isolating the contribution of schools from all other factors determining the educational achievements of students. I overcome this problem by applying a value-added approach. As the dependent variable in the analysis refers to student scores at the end of middle school (grade 9), at the right side of the equation I include the average score achieved by the same students at an earlier stage of education, that is, at the end of primary school (grade 6). However, since 6th and 9th grades are both affected by parental human capital and social norms. There is no reason to believe that social attitudes towards education only matter for one exam. However, it seems reasonable to assume that norms have a stronger impact on achievement at a younger age. Firstly, younger children may spend more time with their parents and thus be influenced by them. Secondly, whereas the results of the 9th-grade exam play an important role in secondary school admissions, the 6th-grade test is low-stakes. Hence, the effort put in by students may depend on their personal attitude.The idea behind the value-added approach is that the achievements of students in the 6Consequently, if the gap between partitions remains significant after controlling for student achievement in grade 6, this suggests that it ‘emerges’ (at least in part) between grades 6 and 9 and has something to do with the quality of educational institutions rather than social norms (which presumably remain constant over such a short period).Typically, student outcomes are strongly correlated with parental education attainment, the latter being approximated by level, number of years in school, or–if available–the actual measure of cognitive skills . AlthougDyslexia is more commonly diagnosed on the Austrian side of the border. The Austrian partition also has a slightly lower level of income deprivation (5.4% vs. 6.1), and visibly higher educational attainment among female adults. However, the voluntary enrolment of six-year-olds in schools is more common in the former Russian territory. The average teacher wage is also higher in the former Russian partition, although in this case, the gap is very small (about 1.5% for experienced teachers and 1% for novice teachers).th grade in the final, compulsory examination taken by all students graduating from middle schools in 2015. However, for the sake of comparability with previous research or with students’ earlier achievements, alternative definitions of student achievement are applied in different sections. Initially .In the main part of the analysis, the dependent variable is the school-level mean from test scores achieved by students in 9nitially , I use tnitially , the depThe results presented in I use data for individual teachers as extracted from the System of Information on Education (SIO), a database administered by Poland’s Ministry of Education. The third dependent variable is the percentage of six-year-old children enrolled voluntarily in the first grade of primary school for which data are available at the municipality level. There are 2,477 municipalities in Poland. These self-governing administrative units are responsible for providing many public services, including primary and lower secondary schooling. The data were extracted from the Local Data Bank (BDL) provided by Poland’s Central Statistical Office (GUS).Some of the model specifications in this study include additional covariates to obtain more precise point estimates or for the sake of sensitivity analysis. The school-level proportion of students by gender and the share of dyslexic students are extracted from the CKE examination data. The percentage of female adults holding a degree comes from the national census (2011) and is available at the municipal level. Finally, the percentage of families receiving social benefits (2008), used as an approximation of income deprivation, is derived from the POMOST system used by municipalities to administer their social assistance programmes.Importantly, the variables available at the municipal level characterise the whole population of the municipality, not just the parents of the students present. Although this may be considered a limitation, it is worth remembering that graduation from middle school was compulsory in Poland’s education system in 2015, implying that the municipal population is a good approximation for the middle school community.A graphical representation of the effect of the former partitions on student achievements is shown in The results in In the last variant, the specification is expanded to include the covariates related to the level of educational attainment among adult females in the municipality as well as the scale of income deprivation proxied by the percentage of families receiving social benefits  and for the socio-economic status of the municipal community , this finding is not confirmed when the bias-corrected robust estimators are applied.Similarly, there seems to be no significant difference between the former Austrian and Russian territories in terms of the voluntary enrolment of six-year-old children in the first grade when the school starting age was changed in Poland.Therefore, I do not find evidence that the supposedly different norms towards education contributes to either different levels of public spending on schooling or willingness to enrol children in school before it becomes compulsory.th-grade examination score (averaged at the school level) as a dependent variable. However, I expand the original specification of the model by including the earlier achievements of students as an explanatory variable. For each school in the database, I calculate the average score achieved by its students when graduating from primary school, that is, in the 6th grade. The dependent variable is adjusted to match the subjects covered in the earlier examination and is thus based on the combined score in mathematics and Polish.In the final step of the analysis, I return to using the 9Overall, as shown in th-grade examination are available only as a combined score covering two subjects, I cannot assess the value added between the 6th and 9th grades for particular subjects. Such an analysis is worth performing in future studies if more disaggregate data on early achievements become accessible.Unfortunately, since the achievements in the 6th-century border between the Austrian and Russian empires on the territory of today’s Poland–a result previously demonstrated by myself [This study confirms the persistence of the achievement gap along the 19y myself . MoreovePrevious research provides partial evidence supporting the hypothesis that the high achievements of today’s students living in the former Austrian partition are explained by their positive norm towards education, which originated in the liberal policy of the Habsburg Empire that allowed for the cultural autonomy of conquered territories. While this study confirms that the achievement gap between Poland’s historical regions is not fully explained by more standard factors, such as the socioeconomic status of the adult population, I do not find direct evidence that the social norm is the major explanatory factor behind the long-lasting differences in school achievements between the partitions.th century’s educational system in the Austrian partition and the performance of today’s students in this territory.After controlling for the basic characteristics of the school communities–gender balance, dyslexia, the educational attainment of adults, and income deprivation within municipalities–the gap in achievements is reduced by roughly 20%. In the case of history and civic education, a school subject particularly associated with national and cultural identity, the gap between the partitions becomes insignificant when covariates are included in the specification. This finding casts doubt on the alleged causal link between the positive framing of Polish identity in the 19Further analysis shows no change at the former border between the partitions concerning two alternative dependent variables: wages paid to teachers and voluntary enrolment of children in school before they reach the compulsory school age. Both these are subject to decisions taken ‘locally’, by either local authorities or parents, and both are arguably related to the social norm towards education. The absence of significant differences between the levels observed on either side of the historical border raises doubts about the decisive role of social attitude towards education in the emergence of the achievement gap between partitions.Finally, after a value-added approach is applied by controlling for the past achievements of students, it turns out that the size of the achievement gap remains unchanged , but the gap becomes statistically insignificant. Therefore, there is no clear evidence that the quality of instruction is higher in the former Austrian partition than in the former Russian one.What do these results tell us about the transmission of norms towards education as a main channel explaining the ongoing gap between the former partitions? The evidence does not strongly support this line of reasoning, at least not in a way that corresponds to the contextual equilibrium effect defined by Angelucci and Di Maro  and thatFirstly, if the positive attitude towards education in the former Austrian partition originates from the opportunity to cultivate Polish identity and culture under Habsburg rule (as suggested by earlier research), then one could expect that the gap in achievements in history and Polish would be at least as large as that in mathematics and that it would be robust to the inclusion of additional covariates in the specification. Indeed, one could expect it to be even larger, given I noted  that parThis study shows that one should be cautious when linking the interregional gap in educational achievements to the contemporary cultural norms of students, especially if many alternative explanations exist. This certainly does not mean that individual attitudes towards education are unimportant in determining students’ success. However, one needs to keep in mind that these attitudes are also shaped by other factors affecting the outcomes of the educational process and subject to reverse causality; that is, a positive norm may simply result from higher educational attainment or a better personal experience at school.S1 Data(DTA)Click here for additional data file.