Hurricanes cause widespread damage to buildings, roads, and other infrastructure, making timely post-disaster damage assessment critical for emergency response and recovery planning. Unmanned aerial vehicle (UAV) imagery provides high-resolution observations of affected areas, but post-hurricane scenes are difficult to classify because multiple damage categories often co-occur within the same image, appear at different spatial scales, and include visually similar severity levels as well as rare but operationally important classes. To address these challenges, this study presents MCANet, a multi-label classification framework for post-hurricane UAV damage assessment. MCANet integrates a Res2Net-based backbone for multi-scale representation with class-specific residual attention to aggregate spatial evidence separately for each damage category. Evaluation on the RescueNet dataset, which includes 4,494 UAV images collected after Hurricane Michael and annotated with 10 damage categories, shows that MCANet achieves the highest mean average precision (mAP) among the evaluated models, with an mAP of 91.37%. Compared with Vision Transformer (ViT-B/16), MCANet improves mAP by 1.90 percentage points while using approximately 50% fewer parameters and 52% fewer giga floating-point operations (GFLOPs). Class-specific residual attention contributed an mAP increase of 1.52 percentage points over class-agnostic attention (CA); the Res2Net backbone alone produced concentrated gains on scale-heterogeneous or underrepresented classes such as Road Blocked and Pool. The single-head configuration achieved the highest overall mAP, whereas the two-head configuration provided targeted gains for Road Blocked and Building Major Damage. MCANet supports rapid post-disaster image screening and provides structured outputs for downstream emergency-management workflows.
Post-hurricane damage assessment is crucial towards managing resource allocations and executing an effective response. Traditionally, this evaluation is performed through field reconnaissance, which is slow, hazardous, and arduous. Instead, in this paper we furthered the idea of implementing deep learning through convolutional neural networks in order to classify post-hurricane satellite imagery of buildings as Flooded/Damaged or Undamaged. The experimentation was conducted employing a dataset containing post-hurricane satellite imagery from the Greater Houston area after Hurricane Harvey in 2…
After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation. One way to gauge the damage extent is to quantify the number of flooded/damaged buildings, which is traditionally done by ground survey. This process can be labor-intensive and time-consuming. In this paper, we propose to improve the efficiency of building damage assessment by applying image classification algorithms to post-hurricane satellite imagery. At the known building coordinates (available from public data), we extract square-sized images from the satellite imagery to …
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…
Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goal is through damage assessment. Recently, disaster researchers have been utilizing imagery captured through satellites or drones to quantify the number of flooded/damaged buildings. In this paper, we propose a mixed data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant impro…
Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN
Technical University of Munich（慕尼黑工业大学）；University of the Bundeswehr Munich（慕尼黑联邦国防军大学）；China University of Mining and Technology（中国矿业大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Tongji University（同济大学）
Puerto Rico suffered severe damage from the category 5 hurricane (Maria) in September 2017. Total monetary damages are estimated to be ~92 billion USD, the third most costly tropical cyclone in US history. The response to this damage has been tempered and slow moving, with recent estimates placing 45% of the population without power three months after the storm. Consequently, we developed a unique data-fusion mapping approach called the Urban Development Index (UDI) and new open source tool, Comet Time Series (CometTS), to analyze the recovery of electricity and infrastructure in Puerto Rico. …
Post-Hurricane Debris Segmentation Using Fine-Tuned Foundational Vision Models
Department of Civil and Environmental Engineering, Rice University（土木与环境工程系， Rice大学）；Department of Electrical and Computer Engineering, Rice University（电气与计算机工程系， Rice大学）；Ken Kennedy Institute, Rice University（肯尼迪研究所， Rice大学）
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Texas A&M University（德克萨斯A&M大学）
In this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic segmentation. The dataset consists of around 2000 high-resolution aerial images, with annotated ground-truth data for semantic segmentation. We discuss the challenges of the dataset and train the state-of-the-art methods on this dataset to evaluate how well these methods can recognize the disaster situations. Finally, we discuss challenges for future research.
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Nanjing University of Science and Technology（南京理工大学）
Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before an effective response is conducted. High-resolution satellite images provide rich information with pre- and post-disaster scenes for analysis. However, most existing works simply use pre- and post-disaster images as input without considering their correlations. In this paper, we propose a novel cross-directional fusion strategy to better explore the correlations between pre- and post-disaster images. Moreover, the da…
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…
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Texas A&M University（得克萨斯农工大学）
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 tasks (e.g. image classification, segmentation, object detection), these datasets are hardly suitable for post disaster damage assessments. On the other hand, existing natural disaster datasets include mainly satellite imagery which have low spatial resolution and a high revisit period. Therefore, they do not have a scope to provide quick and efficient damage assessment tasks. Unm…
High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels
Department of Civil Engineering Florida State University Tallahassee, FL, USA；Dept. of Biosystems \& Agricultural Engineering Michigan State University East Lansing, MI, USA
This study aims to enable more reliable automated post-disaster building damage classification using artificial intelligence (AI) and multi-view imagery. The current practices and research efforts in adopting AI for post-disaster damage assessment are generally (a) qualitative, lacking refined classification of building damage levels based on standard damage scales, and (b) trained based on aerial or satellite imagery with limited views, which, although indicative, are not completely descriptive of the damage scale. To enable more accurate and reliable automated quantification of damage levels…
Massachusetts Institute of Technology（麻省理工学院）；MIT Lincoln Laboratory（麻省理工学院林肯实验室）
Aerial images provide important situational awareness for responding to natural disasters such as hurricanes. They are well-suited for providing information for damage estimation and localization (DEL); i.e., characterizing the type and spatial extent of damage following a disaster. Despite recent advances in sensing and unmanned aerial systems technology, much of post-disaster aerial imagery is still taken by handheld DSLR cameras from small, manned, fixed-wing aircraft. However, these handheld cameras lack IMU information, and images are taken opportunistically post-event by operators. As su…
Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recovery
UrbanResilience.AI Lab（UrbanResilience.AI 实验室）；Zachry Department of Civil and Environmental Engineering（Zachry 土木与环境工程系）；Texas A&M University（德克萨斯A&M大学）；Department of Earth, Marine and Environmental Sciences（地球、海洋与环境科学系）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Simon Fraser University（西蒙弗雷泽大学）；Texas A&M University（德克萨斯A&M大学）
This paper presents \dahitra, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real-time and high-coverage information and offers opportunities to inform large-scale post-disaster building damage assessment, which is critical for rapid emergency response. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures the temporal differences in the …
Improved MambdaBDA Framework for Robust Building Damage Assessment Across Disaster Domains
Department of Computer Engineering, Istanbul Technical University（计算机工程系，伊斯坦布尔技术大学）；Division of Engineering, New York University Abu Dhabi（工程学院，纽约大学阿布扎比分校）
Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery
Urban Resilience.AI Lab（城市韧性人工智能实验室）；Zachry Department of Civil and Environmental Engineering（扎克里土木与环境工程系）；Texas A&M University（德克萨斯A&M大学）；College Station, TX（学院站, 德克萨斯）
Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models
Department of Geography, Texas A&M University（地理系，德克萨斯A&M大学）；Department of Geography, Pennsylvania State University（地理系，宾夕法尼亚州立大学）；Department of Geography and Sustainability, University of Tennessee（地理与可持续发展系，田纳西大学）；Spatial Sciences Institute, University of Southern California（空间科学研究所，南加州大学）
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）
Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before relief effort is deployed. With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings. With the powerful ability of feature representation, deep neural networks have been successfully applied to building damage assessment. Most existing works simply concatenate pre- and post-disaster images as input of a deep neural network without consideri…
Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model
School of Computing, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院计算学院）；College of Business, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院商学院）；Graduate School of Data Science, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院数据科学研究生院）；Division of Social Science, Hong Kong University of Science and Technology (HKUST)（香港科技大学社会科学部）；Max Planck Institute for Security and Privacy (MPI-SP)（马克斯·普朗克安全与隐私研究所）
AB2CD: AI for Building Climate Damage Classification and Detection
IBM Consulting, Germany（IBM咨询公司（德国））；Karlsruhe Institute of Technology, Germany（卡尔斯鲁厄理工学院）；IBM Research – Europe, Switzerland（IBM欧洲研究院）；University of Bayreuth, Germany（拜罗伊特大学）
Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN（理化学研究所先进智能项目中心（AIP），理化学研究所）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）；State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与测绘工程学院）
