Signal-Based Regularization for Building Footprint generated from Airborne LiDAR data

Authors: Xiao Li*, The University of Texas at Dallas, Qiu Fang, The University of Texas at Dallas
Topics: Remote Sensing, Urban Geography, Geographic Information Science and Systems
Keywords: Building footprint, Airborne LiDAR
Session Type: Paper
Day: 4/5/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Roosevelt 6, Marriott, Exhibition Level
Presentation File: No File Uploaded


Two-dimensional digital building models (building footprint) are essential for multiple GIS application, including urban planning, infrastructure development, disaster management, and so on. It also serves as a significant prior shape estimation in constructing the 3-D digital building models. Using airborne LiDAR point cloud to extract the building footprint has drawn a lot of attention in the past decade due to the accurate and dense measurements provided by LiDAR technology. However, the initial building outline directly generated from LiDAR points cloud often suffered from the irregular “zig-zag” shape. The straight edge of building footprint usually appears as jagged form. How to eliminate such irregular shape while keeping the integrity of building footprint structure remains a challenging task. We examined this problem from a completely different perspective through converting the irregular initial building outline polygon into a signal. The converted signal is a 1-D function that records the angular deviation at each vertex of the irregular polygon. By doing this, the 2-D irregular outline polygon can be represented by a 1-D signal function, on which the signal processing technique can be used to eliminate the noise, the converted form of “zig-zag” shape in the signal. At last, the noise-free signal is used to determine the real shape of the building outline and generate the regularized building footprint.

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