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G. Mutreja

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Open access Jul 2026

GeoRGMAE: Geospatially Guided Masked Autoencoders for Building Segmentation

The proposed GeoRGMAE, a geospatially guided masked autoencoder pretraining strategy for building segmentation, introduces three masking strategies that prioritize semantically relevant building regions under the varying urban densities and suggests that incorporating geospatial priors into masked image modelling (MIM) can improve representation learning for downstream building segmentation tasks.

Tuğba Eraslanoğlu, G. Mutreja, Martin Kada et al. · 0 citations