Disaster mapping is a critical task that often requires on-site experts and is time-consuming. To address this, a comprehensive framework is presented for fast and accurate recognition of disasters using machine learning, termed DisasterNets. It consists of two stages, space granulation and attribute granulation. The space granulation stage leverages supervised/semi-supervised learning, unsupervised change detection, and domain adaptation with/without source data techniques to handle different disaster mapping scenarios. Furthermore, the disaster database with the corresponding geographic information field properties is built by using the attribute granulation stage. The framework is applied to earthquake-triggered landslide mapping and large-scale flood mapping. The results demonstrate a competitive performance for high-precision, high-efficiency, and cross-scene recognition of disasters. To bridge the gap between disaster mapping and machine learning communities, we will provide an openly accessible tool based on DisasterNets. The framework and tool will be available at https://github.com/HydroPML/DisasterNets.
RIKEN Center for Advance Intelligence Project（理化学研究所革新智能研究中心）；the University of Tokyo（东京大学）；Hiroshima University（广岛大学）；Chiba University（千叶大学）；Tokyo Institute of Technology（东京工业大学）；Tohoku University（东北大学）
Earth observation technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to monitor ever-growing urban environments continuously. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster's aftermath. However, due to several factors, such as weather and satellite coverage, it is often uncertain which data modality will be the first available for rapid disaster respo…
The use of satellite imagery has become increasingly popular for disaster monitoring and response. After a disaster, it is important to prioritize rescue operations, disaster response and coordinate relief efforts. These have to be carried out in a fast and efficient manner since resources are often limited in disaster-affected areas and it's extremely important to identify the areas of maximum damage. However, most of the existing disaster mapping efforts are manual which is time-consuming and often leads to erroneous results. In order to address these issues, we propose a framework for chang…
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…
Computational methods to accelerate natural disaster response include change detection, map alignment, and vision-aided navigation. Current software functions optimally only on near-nadir images, though off-nadir images are often the first sources of information following a natural disaster. The use of off-nadir images for the aforementioned tasks requires the computation of geocentric pose, which is an aerial vehicle's spatial orientation with respect to gravity. This study proposes a deep learning ensemble framework to predict geocentric pose using 5,923 near-nadir and off-nadir RGB satellit…
Decoding the Disaster: Multi-Task Geospatial Reasoning with Vision-Language Models and Crowdsourced Imagery for Disaster Mapping
Shandong University of Science and Technology（山东科技大学）；Technical University of Munich（慕尼黑工业大学）；Wuhan University（武汉大学）；Tsinghua University（清华大学）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；University of Iceland（冰岛大学）
Lancaster University（兰卡斯特大学）；University of Bath（巴斯大学）；University of Oxford（牛津大学）
High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastructure status such as building damage, flooding, and obstructions to access routes. Damage mapping at this scale would require hundreds of expert person-hours. However, a combination of crowdsourcing and recent advances in deep learning reduces the effort needed to just a few hours in real time. Asking volunteers to place point marks, as opposed to shapes of actual damaged areas, significantly decreases the required ana…
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…
Leibniz University Hannover（莱布尼茨汉诺威大学）；bpi Hannover–Beratende Ingenieure（汉诺威咨询工程师有限公司）；Federal Institute for Geosciences and Natural Resources（联邦地球科学与自然资源研究所）；Shandong University of Science and Technology（山东科技大学）；China University of Geosciences（中国地质大学）
With increasing urbanization, flooding is a major challenge for many cities today. Based on forecast precipitation, topography, and pipe networks, flood simulations can provide early warnings for areas and buildings at risk of flooding. Basement windows, doors, and underground garage entrances are common places where floodwater can flow into a building. Some buildings have been prepared or designed considering the threat of flooding, but others have not. Therefore, knowing the heights of these facade openings helps to identify places that are more susceptible to water ingress. However, such da…
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）
Smart Transfer: Leveraging Vision Foundation Model for Rapid Building Damage Mapping with Post-Earthquake VHR Imagery
Department of Geography, National University of Singapore（新加坡国立大学地理系）；Urban Governance and Design Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）城市治理与设计学域）；School of Environment and Spatial Informatics, China University of Mining and Technology（中国矿业大学环境与空间信息学院）；Disaster Management Institute, Istanbul Technical University（伊斯坦布尔技术大学灾害管理研究所）；Department of Complexity Science and Engineering, University of Tokyo（东京大学复杂科学与工程系）；School of Automation, Southeast University（东南大学自动化学院）
A Decade of PhotoMappers: A Longitudinal Study of Crowdsourced Disaster Photos Geolocalization with Vision-Language Models and Geospatial Reasoning
National University of Singapore（新加坡国立大学）；College of Geodesy and Geomatics, Shandong University of Science and Technology（山东科技大学测绘与空间信息学院）；University of the Bundeswehr Munich（慕尼黑联邦国防军大学）；Wuhan University（武汉大学）；University of Florida（佛罗里达大学）；Institute of Remote Sensing and GIS, Peking University（北京大学遥感与地理信息系统研究所）；Department of Architecture, National University of Singapore（新加坡国立大学建筑系）；Department of Real Estate, National University of Singapore（新加坡国立大学房地产系）
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）
Humanitarian organizations must have fast and reliable data to respond to disasters. Deep learning approaches are difficult to implement in real-world disasters because it might be challenging to collect ground truth data of the damage situation (training data) soon after the event. The implementation of recent self-paced positive-unlabeled learning (PU) is demonstrated in this work by successfully applying to building damage assessment with very limited labeled data and a large amount of unlabeled data. Self-PU learning is compared with the supervised baselines and traditional PU learning usi…
Efficient On-Board Processing of Oblique UAV Video for Rapid Flood Extent Mapping
IDLab, Department of Information Technology, Ghent University - imec（IDLab，信息科技系，根特大学 - imec）；Environmental Intelligence Unit, VITO (Flemish Institute for Technological Research)（环境智能单元，VITO（佛兰德斯技术研究院））
Road network and building footprint extraction is essential for many applications such as updating maps, traffic regulations, city planning, ride-hailing, disaster response \textit{etc}. Mapping road networks is currently both expensive and labor-intensive. Recently, improvements in image segmentation through the application of deep neural networks has shown promising results in extracting road segments from large scale, high resolution satellite imagery. However, significant challenges remain due to lack of enough labeled training data needed to build models for industry grade applications. I…
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）
Change detection methods applied to monitoring key infrastructure like airport runways represent an important capability for disaster relief and urban planning. The present work identifies two generative adversarial networks (GAN) architectures that translate reversibly between plausible runway maps and satellite imagery. We illustrate the training capability using paired images (satellite-map) from the same point of view and using the Pix2Pix architecture or conditional GANs. In the absence of available pairs, we likewise show that CycleGAN architectures with four network heads (discriminator…
Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product
University of California - Los Angeles（加利福尼亚大学洛杉矶分校）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Joint Institute for Regional Earth System Science and Engineering, University of California - Los Angeles（加利福尼亚大学洛杉矶分校区域地球系统科学与工程联合研究所）
本文基于 OPERA RTC-S1 数据训练无需标签的自监督 vision transformer，通过逐像素基线分布偏差检测地表扰动，并在三地灾害事件中取得优于现有方法的结果。
Department of Computer Science, the University of Alabama（阿拉巴马大学计算机科学系）
Flood extent mapping plays a crucial role in disaster management and national water forecasting. In recent years, high-resolution optical imagery becomes increasingly available with the deployment of numerous small satellites and drones. However, analyzing such imagery data to extract flood extent poses unique challenges due to the rich noise and shadows, obstacles (e.g., tree canopies, clouds), and spectral confusion between pixel classes (flood, dry) due to spatial heterogeneity. Existing machine learning techniques often focus on spectral and spatial features from raster images without full…
University of Kaiserslautern-Landau（凯泽斯劳滕-兰道大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；University of Yamanashi（山梨大学）
Climate change has increased the severity and frequency of weather disasters all around the world. Flood inundation mapping based on earth observation data can help in this context, by providing cheap and accurate maps depicting the area affected by a flood event to emergency-relief units in near-real-time. Building upon the recent development of the Sen1Floods11 dataset, which provides a limited amount of hand-labeled high-quality training data, this paper evaluates the potential of five traditional machine learning approaches such as gradient boosted decision trees, support vector machines o…
