Representative Samples Forming of Urban Aerial and Satellite Imagery for Building Footprint Segmentation
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2025
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This paper presents an approach to forming representative training samples of urban aerial and satellite imagery for building footprint segmentation. It is shown that the performance and generalization ability of convolutional neural networks strongly depend not only on dataset size, but also on controlled coverage of urban scene variability, imaging conditions, and annotation conventions. The proposed methodology combines large-scale satellite benchmarks with polygon footprint labels and aerial imagery from unmanned platforms as complementary domains, explicitly addressing domain shift, occlusions, and perspective distortions. Sample representativeness is assessed through the training and validation behavior of YOLO-family segmentation models, including convergence stability and metric profiles. The experimental results demonstrate stable learning dynamics and the presence of challenging boundary cases typical of real urban environments, confirming the effectiveness of the proposed data formation strategy for robust building footprint segmentation in practical geospatial applications.
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Vit R., Molchanova M., Mazurets O. Representative Samples Forming of Urban Aerial and Satellite Imagery for Building Footprint Segmentation. Modern Perspectives on Global Scientific Solutions. Proceedings of the 6th International Scientific and Practical Conference. December 29-31, 2025. Bergen, Norway. Pp. 193-203