Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean

TL;DR

This paper presents a deep learning approach that fuses VHR aerial imagery and LiDAR data to accurately classify roof types and materials, aiding disaster resilience efforts in the Caribbean.

Contribution

It introduces a multimodal data fusion method that outperforms single-source approaches for roof classification using deep learning.

Findings

F1 scores of 0.93 for roof type classification

F1 scores of 0.92 for roof material classification

Fusion of VHR imagery and LiDAR improves accuracy

Abstract

Accurate and up-to-date information on building characteristics is essential for vulnerability assessment; however, the high costs and long timeframes associated with conducting traditional field surveys can be an obstacle to obtaining critical exposure datasets needed for disaster risk management. In this work, we leverage deep learning techniques for the automated classification of roof characteristics from very high-resolution orthophotos and airborne LiDAR data obtained in Dominica following Hurricane Maria in 2017. We demonstrate that the fusion of multimodal earth observation data performs better than using any single data source alone. Using our proposed methods, we achieve F1 scores of 0.93 and 0.92 for roof type and roof material classification, respectively. This work is intended to help governments produce more timely building information to improve resilience and disaster response in the Caribbean.