Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations. (arXiv:2311.01188v1 [cs.CV])

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 …

annotations applications arxiv building data deep learning development disaster disaster management extraction face geospatial importance lidar management maps planning post-processing processing segmentation self-supervised learning supervised learning urban urban planning