Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications
Ozer Lab, Dept. of Computer Science, Ozyegin University（奥泽实验室，计算机科学系，奥兹根大学）；Dept. of Electrical and Computer Engineering, California State Polytechnic University, Pomona（电气与计算机工程系，加州州立理工大学庞纳分校）
Alain P. Ndigande, Josiah Wiggins, Sedat Ozer
备注：
This work has been accepted for publication at IEEE Transactions on Geoscience and Remote Sensing
Recent natural disasters have highlighted the urgent need for efficient data-driven approaches to disaster management. Machine learning (ML) and deep learning (DL) techniques have shown considerable promise in enhancing the key phases of disaster management including mitigation, preparedness, detection, response, and recovery. A critical enabler of successful ML or DL based applications in remote sensing, however, is the accessibility and quality of annotated datasets. With the growing availability of high-resolution imagery from unmanned aerial vehicles (UAVs) and satellites, computer vision and remote sensing algorithms have become essential tools for rapid detection, situational assessment, and decision-making in disaster scenarios. This survey provides a comprehensive overview of publicly available image-based datasets relevant to ML/DL-based disaster management pipelines. Emphasis is placed on datasets that support computer vision and remote sensing tasks across all phases of disaster events including pre-disaster, during, and post-disaster. The goal of this work is to serve as a centralized reference for researchers and practitioners seeking high-quality datasets for rapid development and deployment of remote sensing-driven disaster response solutions.
RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events
School of Earth Sciences, Zhejiang University（浙江大学地球科学学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Provincial Key Laboratory of Geographic Information Science（浙江省地理信息科学重点实验室）；Key Laboratory of Spatio-temporal Information and Intelligent Services (LSIIS), Ministry of Natural Resources of the People’s Republic of China（国家自然资源部空间时空信息与智能服务重点实验室）
Massachusetts Institute of Technology（麻省理工学院）；Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）
Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety o…
3DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessment
Department of Computer Science and Engineering, Lehigh University（计算机科学与工程系，莱维大学）；Department of Civil and Environmental Engineering, Lehigh University（土木与环境工程系，莱维大学）
CSAIL at MIT（麻省理工学院计算机科学与人工智能实验室）；Technical University of Denmark (DTU)（丹麦技术大学）；Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；QCRI（卡塔尔计算研究所）
Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the Earth undergoes global warming. It is difficult to predict when and where an incident will occur, so timely emergency response is critical to saving the lives of those endangered by destructive events. Fortunately, technology can play a role in these situations. Social media posts can be used as a low-latency data source to understand the progression and aftermath of a disaster, yet parsing this data is tedious without automated methods. Prior work has mostly focused on text-based filtering, yet image…
Skolkovo institute of Science and technology（斯科尔科沃科学技术研究院）；Moscow（莫斯科）
When major disaster occurs the questions are raised how to estimate the damage in time to support the decision making process and relief efforts by local authorities or humanitarian teams. In this paper we consider the use of Machine Learning and Computer Vision on remote sensing imagery to improve time efficiency of assessment of damaged buildings in disaster affected area. We propose a general workflow that can be useful in various disaster management applications, and demonstrate the use of the proposed workflow for the assessment of the damage caused by the wildfires in California in 2017.
KIOS Research and Innovation Center of Excellence（KIOS卓越研究与创新中心）；University of Cyprus（塞浦路斯大学）
Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas. In addition, by utilizing an embedded platform and deep learning UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. To this end, this pa…
Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning
Built Environment Department, College of Science and Technology, North Carolina A&T State University（北卡罗来纳农工州立大学科技学院建筑环境系）；United Nations University Institute for Water, Environment and Health（联合国大学水、环境与健康研究所）
KIOS Research and Innovation Center of Excellence（KIOS研究与创新卓越中心）；University of Cyprus（塞浦路斯大学）
Deep learning-based algorithms can provide state-of-the-art accuracy for remote sensing technologies such as unmanned aerial vehicles (UAVs)/drones, potentially enhancing their remote sensing capabilities for many emergency response and disaster management applications. In particular, UAVs equipped with camera sensors can operating in remote and difficult to access disaster-stricken areas, analyze the image and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. However, th…
We have developed a framework for crisis response and management that incorporates the latest technologies in computer vision (CV), inland flood prediction, damage assessment and data visualization. The framework uses data collected before, during, and after the crisis to enable rapid and informed decision making during all phases of disaster response. Our computer-vision model analyzes spaceborne and airborne imagery to detect relevant features during and after a natural disaster and creates metadata that is transformed into actionable information through web-accessible mapping tools. In part…
ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understanding
School of Computer Science and Technology and the Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University（计算机科学与技术学院和教育部智能网络与网络安全重点实验室，西安交通大学）；State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences（遥感与数字地球国家重点实验室，航空信息研究所，中国科学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗斯托克研究所）；Lancaster Environment Centre, Lancaster University（兰卡斯特大学环境研究中心）；Faculty of Electrical and Computer Engineering, University of Iceland（冰岛大学电气与计算机工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
New York University（纽约大学）；University of the Philippines Diliman（菲律宾大学迪里曼分校）；University of Washington（华盛顿大学）；German Corporation for International Cooperation GmbH (GIZ)（德国国际合作机构（GIZ））
Rapid damage assessment is of crucial importance to emergency responders during hurricane events, however, the evaluation process is often slow, labor-intensive, costly, and error-prone. New advances in computer vision and remote sensing open possibilities to observe the Earth at a different scale. However, substantial pre-processing work is still required in order to apply state-of-the-art methodology for emergency response. To enable the comparison of methods for automatic detection of damaged buildings from post-hurricane remote sensing imagery taken from both airborne and satellite sensors…
SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images
After a natural disaster, such as a hurricane, millions are left in need of emergency assistance. To allocate resources optimally, human planners need to accurately analyze data that can flow in large volumes from several sources. This motivates the development of multimodal machine learning frameworks that can integrate multiple data sources and leverage them efficiently. To date, the research community has mainly focused on unimodal reasoning to provide granular assessments of the damage. Moreover, previous studies mostly rely on post-disaster images, which may take several days to become av…
Global Mapping of Exposure and Physical Vulnerability Dynamics in Least Developed Countries using Remote Sensing and Machine Learning
UKRI CDT in AI for Environmental Risks（英国研究与创新人工智能环境风险博士培训中心）；Centre for Risk in the Built Environment（建筑环境风险中心）；University of Cambridge（剑桥大学）；German Aerospace Center (DLR)（德国航空航天中心）；University of Bonn（波恩大学）
the University of Tokyo（东京大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Disaster Prevention Research Institute, Kyoto University（京都大学防灾研究所）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；Hiroshima University（广岛大学）
We propose a framework that estimates inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learning and numerical simulation. A water and debris flow simulator generates training data for various artificial disaster scenarios. We show that regression models based on Attention U-Net and LinkNet architectures trained on such synthetic data can predict the maximum water level and topographic deformation from a remote sensing-derived change detection map and a digital elevation model. The proposed framework has an in…
Rapid post-disaster infrastructure damage characterisation enabled by remote sensing and deep learning technologies -- a tiered approach
University of Birmingham（伯明翰大学）；Centre for Research and Technology Hellas (CERTH)（希腊研究与技术中心）；Lviv National Polytechnic University（利沃夫国立理工大学）；University of Agriculture in Krakow（克拉科夫农业大学）；Brunel University London（伦敦布鲁内尔大学）
A Multimodal Large Language Model-Driven Framework for Context-Aware UAV Emergency Landing Site Selection
School of Information Science and Engineering, Southeast University（信息科学与工程学院，东南大学）；LASER, International Digital Economy Academy（LASER，国际数字经济学院）；Department of Electronic Engineering, East China Normal University（电子工程系，华东师范大学）
