Identifying the locations and footprints of buildings is vital for many practical and scientific purposes. Such information can be particularly useful in developing regions where alternative data sources may be scarce. In this work, we describe a model training pipeline for detecting buildings across the entire continent of Africa, using 50 cm satellite imagery. Starting with the U-Net model, widely used in satellite image analysis, we study variations in architecture, loss functions, regularization, pre-training, self-training and post-processing that increase instance segmentation performance. Experiments were carried out using a dataset of 100k satellite images across Africa containing 1.75M manually labelled building instances, and further datasets for pre-training and self-training. We report novel methods for improving performance of building detection with this type of model, including the use of mixup (mAP +0.12) and self-training with soft KL loss (mAP +0.06). The resulting pipeline obtains good results even on a wide variety of challenging rural and urban contexts, and was used to create the Open Buildings dataset of 516M Africa-wide detected footprints.
@article{arxiv.2107.12283,
title = {Continental-Scale Building Detection from High Resolution Satellite Imagery},
author = {Wojciech Sirko and Sergii Kashubin and Marvin Ritter and Abigail Annkah and Yasser Salah Eddine Bouchareb and Yann Dauphin and Daniel Keysers and Maxim Neumann and Moustapha Cisse and John Quinn},
journal= {arXiv preprint arXiv:2107.12283},
year = {2021}
}