Using Machine Learning to Extract Building Inventory Information Based on LiDAR Data

Abstract

The extraction of building inventory information is vital for damage assessment and planning and modelling studies. In the last few years, the conventional data extraction for building inventory was overcome using various remote sensing data and techniques. The main objectives of this study were to supply the necessary data for the structural engineers to calculate the seismic performance of existing structures. Thus, we investigated light detection and ranging (LiDAR) derivatives data to classify buildings and extract building inventory information, such as different heights of the buildings and footprint area. The most important data to achieve this was also investigated and classified using machine learning methods, such as Random Forest, Random Tree, and Optimized Forest, over the object-based segmentation results. All of the machine learning methods successfully classified the buildings with high accuracy, whereas the other methods outperformed RT. The height and footprint area results show that the archived sensitivity of the building inventory information is sufficient for the data to be further used in different applications, such as detailed structural health monitoring. Overall, this study presents a methodology that can accurately extract building information. In light of the results, future studies can be directed for investigations on determining the construction year using remote sensing data, such as multi-temporal satellite imagery.

Keywords

• remote sensing
• LiDAR
• building
• height
• machine learning
• random forest