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This research aims to improve building segmentation from aerial imagery using geospatially guided masked autoencoders.

GeoRGMAE: Geospatially Guided Masked Autoencoders for Building Segmentation

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This research aims to improve building segmentation from aerial imagery using geospatially guided masked autoencoders.
Developed GeoRGMAE for building segmentation with three predefined masking strategies: core, balanced, and density-aware.
Leveraged available building footprint annotations during pretraining to enhance masking approaches.
Evaluated the method on Roof3D and WHU Building datasets to analyze performance improvements over standard MAE.
Demonstrated consistent, though modest, improvements in segmentation accuracy with GeoRGMAE over standard MAE.
Specific effectiveness of masking strategies varied based on dataset characteristics, suggesting tailored approaches may enhance results.

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Randomized trial explores geospatially guided masking for improved building segmentation, suggesting new paradigms for urban applications.

Eraslanoğlu et al. (2026) studied this question.

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Randomized trial explores geospatially guided masking for improved building segmentation, suggesting new paradigms for urban applications.