Detect Residential Buildings from Lidar and Aerial Photographs through Object-Oriented Land-Use Classification

Relating less directly to the physical reflectance from remote sensors, land-use analysis is comparably more challenging than land-cover studies, especially for residential land-uses. This research presents an object-oriented approach to detect residential land use of buildings directly from lidar data, aerial photography, and road maps to enhance urban land-use analysis. Specifically, the proposed methodology applies a multi-directional ground filter to generate a bare ground surface from lidar data, then uses a morphology-based building detection algorithm to identify buildings from lidar and aerial photographs, and finally separates residential buildings using a supervised C4.5 decision tree analysis based on seven land-use characteristics of buildings. Experiments based on the 8.25 km(2) study site located in Austin, Texas proved the possibility and efficiency of directly detecting and identifying residential buildings from remote sensing images with 81.1 percent of residential buildings correctly classified.