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.

使用 Sentinel-2 卫星图像进行建筑物和道路的自动标记，为高分辨率遥感图像的代替提供了可能。学生模型通过训练来复原教师模型的预测结果，虽然没有教师模型那么精细，但建筑物分割中的 mIoU 达到了 78.3%、个别建筑物计数达到 R^2=0.91。新的方法可以利用免费的 Sentinel-2 图像进行以往只能使用高分辨率卫星图像完成的各种任务。