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High Resolution Building and Road Segmentation from Sentinel-2 Imagery

Abdoulaye Diack
Abel Tesfaye Korme
Emmanuel Asiedu Brempong
Jason Hickey
Juliana Marcos
Krishna Sapkota
Mohammed Alewi Hassen
Wojciech Sirko
arXiv, https://arxiv.org/abs/2310.11622 (2023)
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Mapping buildings and roads automatically with remote sensing typically requires imagery of at least 50 cm resolution, which is expensive to obtain and often sparsely available. In this work we demonstrate how public, worldwide imagery from the Sentinel-2 Earth observation mission can be used to carry out this task at a much higher level of detail than the 10 m raw pixel resolution would suggest. To do this, we employ a teacher-student method in which a model with access to a temporal stack of Sentinel-2 images is trained to make the same predictions as a high-resolution model with access to corresponding 50 cm imagery. Evaluating at 50cm resolution, we achieve mIOU of 0.78, equivalent in accuracy to applying a single-frame high resolution model with imagery of 4m resolution. This work opens up new possibilities for using freely available Sentinel-2 imagery for a range of downstream tasks that previously could only be done with high resolution satellite imagery. The model will be made available soon to non-commercial, non-governmental entities at https://sites.research.google/open-buildings/ upon request.

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