Deep Semantic Segmentation for Building Detection Using Knowledge-Informed Features from LiDAR Point Clouds

Airborne LiDAR point clouds record three-dimensional structures of ground surfaces with high precision, and have been widely used to identify geospatial objects, facilitating the understanding of the distribution and changing dynamics of the environment. Detection can be complicated by the complex structures of ground objects and noises in LiDAR point clouds. Related work has explored the use of deep learning techniques such as YOLO in detecting geospatial objects (e.g., building footprints) on both optical imagery and LiDAR point clouds. However, deep networks are data hungry and there are often limited labeled samples available for many geospatial object mapping tasks, making it difficult for the models to generalize to unseen test regions. This paper describes the framework used in the 11th SIGSPATIAL Cup Competition (GIS CUP 2022), which received the top-3 performance. Our framework incorporates domain knowledge to reduce the difficulty of learning and the model's reliance on large training sets. Specifically, we present knowledge-informed feature generation and filtering based on morphological characteristics to improve the generalizability of learned features. Then, we use a deep segmentation backbone (U-Net) with training- and test-time augmentation to generate preliminary candidates for building footprints. Finally, we utilize domain rules (e.g., geometric properties) to regularize and filter the detections to create the final map of building footprints. Experiment results show that the strategies can effectively improve detection results in different landscapes.