3D Semantic Segmentation for Post-Disaster Assessment

2025-12-31 · Nhut Le, Maryam Rahnemoonfar arxiv

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.

📄 PDF Abstract BibTeX arXiv:2512.24593

Code (0)

등록된 구현이 없습니다.

Tasks

3D Semantic Segmentation Scene Understanding Point Clouds

RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment

2022-02-24 · Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy

Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment. In this paper, we prese…

Scene Understanding Segmentation Semantic Segmentation

3DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessment

2025-09-14 · Nhut Le, Ehsan Karimi, Maryam Rahnemoonfar arxiv

Timely assessment of structural damage is critical for disaster response and recovery. However, most prior work in natural disaster analysis relies on 2D imagery, which lacks depth, suffers from occlusions, and provides …

3D Semantic Segmentation Scene Understanding Point Clouds

FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

2020-12-05 · Maryam Rahnemoonfar, Tashnim Chowdhury, Argho Sarkar, Debvrat Varshney 외

Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tas…

image-classification Image Classification object-detection Object Detection+6

OPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentation

2026-05-16 · Nhut Le, Ehsan Karimi, Maryam Rahnemoonfar arxiv

Post-disaster damage assessment requires rapid and accurate semantic segmentation of 3D point clouds to identify critical infrastructure such as damaged buildings and roads. Early Point Transformers (e.g., PTv1, PTv2) re…

3D Semantic Segmentation Point Clouds

CNN-Based Semantic Change Detection in Satellite Imagery

2020-06-10 · Ananya Gupta, Elisabeth Welburn, Simon Watson, Hujun Yin

Timely disaster risk management requires accurate road maps and prompt damage assessment. Currently, this is done by volunteers manually marking satellite imagery of affected areas but this process is slow and often erro…

Change Detection Management valid