Automatic Reconstruction of Cities from Remote Sensor Data

Abstract

In this paper, we address the complex problem of rapid modeling of large-scale areas and present a novel approach for the automatic reconstruction of cities from remote sensor data. The goal in this work is to automatically create lightweight, watertight polygonal 3D models from LiDAR data (Light Detection and Ranging) captured by an airborne scanner. This is achieved in three steps: preprocessing, segmentation and modeling, as shown in Figure 1. Our main technical contributions in this paper are: (i) a novel, robust, automatic segmentation technique based on the statistical analysis of the geometric properties of the data, which makes no particular assumptions about the input data, thus having no data dependencies, and (ii) an efficient and automatic modeling pipeline for the reconstruction of large-scale areas containing several thousands of buildings. We have extensively tested the proposed approach with several city-size datasets including downtown Baltimore, downtown Denver, the city of Atlanta, downtown Oakland, and we present and evaluate the experimental results.