  Estimating building footprint maps from geospatial data is of paramount
importance in urban planning, development, disaster management, and various
other applications. Deep learning methodologies have gained prominence in
building segmentation maps, offering the promise of precise footprint
extraction without extensive post-processing. However, these methods face
challenges in generalization and label efficiency, particularly in remote
sensing, where obtaining accurate labels can be both expensive and
time-consuming. To address these challenges, we propose terrain-aware
self-supervised learning, tailored to remote sensing, using digital elevation
models from LiDAR data. We propose to learn a model to differentiate between
bare Earth and superimposed structures enabling the network to implicitly learn
domain-relevant features without the need for extensive pixel-level
annotations. We test the effectiveness of our approach by evaluating building
segmentation performance on test datasets with varying label fractions.
Remarkably, with only 1% of the labels (equivalent to 25 labeled examples), our
method improves over ImageNet pre-training, showing the advantage of leveraging
unlabeled data for feature extraction in the domain of remote sensing. The
performance improvement is more pronounced in few-shot scenarios and gradually
closes the gap with ImageNet pre-training as the label fraction increases. We
test on a dataset characterized by substantial distribution shifts and labeling
errors to demonstrate the generalizability of our approach. When compared to
other baselines, including ImageNet pretraining and more complex architectures,
our approach consistently performs better, demonstrating the efficiency and
effectiveness of self-supervised terrain-aware feature learning.
