  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.
