Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations

arXiv:2311.01188v2 Announce Type: replace
Abstract: 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 …

abstract annotations applications arxiv building cs.cv data deep learning development disaster disaster management extraction geospatial however importance lidar management maps planning post-processing processing segmentation self-supervised learning supervised learning type urban urban planning