Building footprint extraction and 3-D reconstruction from LIDAR data

Building information is extremely important for many applications within the urban environment. Automated techniques and user-friendly tools for information extraction from remotely sensed imagery are urgently needed. This paper presents an automatic approach for building footprint extraction and 3-D reconstruction from airborne light detection and ranging (LIDAR) data. First a digital surface model (DSM) is generated from the LIDAR point data. The approach then extracts objects higher than the ground surface. Based on general knowledge about building geometric characteristics such as size, height and shape, buildings are separated from other objects (trees, etc.). The extracted building footprints are then simplified using an orthogonal algorithm to obtain better cartographic quality. Watershed analysis is conducted to extract the ridgelines of building roofs. The ridgelines as well as slope information are used to classify building roof types. The buildings are reconstructed using three basic parametric building models (flat, gabled, hipped) common to the study area. Finally, the results of extraction are compared with manually digitized building reference data to conduct an accuracy assessment.