High-Resolution Building and Road Detection from Sentinel-2
Wojciech Sirko Emmanuel Asiedu Brempong Juliana T. C. Marcos Abigail Annkah Abel Korme Mohammed Alewi Hassen Krishna Sapkota Tomer Shekel Abdoulaye Diack Sella Nevo Jason Hickey John Quinn
Mapping buildings and roads automatically with remote sensing typically
requires high-resolution imagery, which is expensive to obtain and often
sparsely available. In this work we demonstrate how multiple 10 m resolution
Sentinel-2 images can be used to generate 50 cm resolution building and road
segmentation masks. This is done by training a `student' model with access to
Sentinel-2 images to reproduce the predictions of a `teacher' model which has
access to corresponding high-resolution imagery. While the predictions do not
have all the fine detail of the teacher model, we find that we are able to
retain much of the performance: for building segmentation we achieve 78.3%
mIoU, compared to the high-resolution teacher model accuracy of 85.3% mIoU. We
also describe a related method for counting individual buildings in a
Sentinel-2 patch which achieves R^2 = 0.91 against true counts. This work opens
up new possibilities for using freely available Sentinel-2 imagery for a range
of tasks that previously could only be done with high-resolution satellite
imagery.