3D Semantic Segmentation for Post-Disaster Assessment

TL;DR

This paper introduces a new 3D dataset from UAV footage of Hurricane Ian for post-disaster assessment and evaluates current segmentation models, revealing their limitations in disaster environments.

Contribution

The creation of a specialized 3D dataset for post-disaster scenes and the evaluation of state-of-the-art models highlighting their shortcomings.

Findings

Existing models perform poorly on disaster datasets.

Significant limitations in current 3D segmentation methods.

Need for specialized models and datasets for post-disaster analysis.

Abstract

The increasing frequency of natural disasters poses severe threats to human lives and leads to substantial economic losses. While 3D semantic segmentation is crucial for post-disaster assessment, existing deep learning models lack datasets specifically designed for post-disaster environments. To address this gap, we constructed a specialized 3D dataset using unmanned aerial vehicles (UAVs)-captured aerial footage of Hurricane Ian (2022) over affected areas, employing Structure-from-Motion (SfM) and Multi-View Stereo (MVS) techniques to reconstruct 3D point clouds. We evaluated the state-of-the-art (SOTA) 3D semantic segmentation models, Fast Point Transformer (FPT), Point Transformer v3 (PTv3), and OA-CNNs on this dataset, exposing significant limitations in existing methods for disaster-stricken regions. These findings underscore the urgent need for advancements in 3D segmentation techniques and the development of specialized 3D benchmark datasets to improve post-disaster scene understanding and response.