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 fully incorporating the geographic terrain within classification models. In contrast, we recently proposed a novel machine learning model called geographical hidden Markov tree that integrates spectral features of pixels and topographic constraints from Digital Elevation Model (DEM) data (i.e., water flow directions) in a holistic manner. This paper evaluates the model through case studies on high-resolution aerial imagery from the National Oceanic and Atmospheric Administration (NOAA) National Geodetic Survey together with DEM. Three scenes are selected in heavily vegetated floodplains near the cities of Grimesland and Kinston in North Carolina during Hurricane Matthew floods in 2016. Results show that the proposed hidden Markov tree model outperforms several state of the art machine learning algorithms (e.g., random forests, gradient boosted model) by an improvement of F-score (the harmonic mean of the user's accuracy and producer's accuracy) from around 70% to 80% to over 95% on our datasets.
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…
University of Oxford（牛津大学）；TU Munich（慕尼黑工业大学）；University of Kent（肯特大学）；LIST Luxembourg（卢森堡LIST研究所）；DFKI（德国人工智能研究中心）；TU Kaiserslautern（凯泽斯劳滕工业大学）；Czech Geological Survey（捷克地质调查局）；University of Warsaw（华沙大学）
We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our model significantly expedites the generation of satellite imagery-based flood maps, crucial for first responders and local authorities in the early stages of flood events. By incorporating multitemporal satellite imagery, our model allows for rapid and accurate post-disaster damage assessment and can be used by governments to better coordinate medium- and long-term financial assistance programs for affected ar…
Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods
Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学（LUMS））
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）
The increasing intensity and frequency of floods is one of the many consequences of our changing climate. In this work, we explore ML techniques that improve the flood detection module of an operational early flood warning system. Our method exploits an unlabelled dataset of paired multi-spectral and Synthetic Aperture Radar (SAR) imagery to reduce the labeling requirements of a purely supervised learning method. Prior works have used unlabelled data by creating weak labels out of them. However, from our experiments we noticed that such a model still ends up learning the label mistakes in thos…
Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies
Image Processing Department, Fraunhofer ITWM（弗劳恩霍夫ITWM图像处理部门）；Algorithms for Computer Vision, Imaging and Data Analysis Group, Hochschule Darmstadt（计算机视觉算法、成像与数据分析小组，达姆施塔特应用技术大学）
AIFloodSense: A Global Aerial Imagery Dataset for Semantic Segmentation and Understanding of Flooded Environments
Department of Management Science and Technology（管理科学与技术系）；Hellenic Mediterranean University（希伯伦地中海大学）；Institute of Computer Science, FORTH（信息科学研究所，FORTH）
Department of Computer and Electrical Engineering, Rutgers University（计算机与电气工程系，罗格斯大学）；Cloud to Street（云街（Cloud to Street））
Accurate flood detection in near real time via high resolution, high latency satellite imagery is essential to prevent loss of lives by providing quick and actionable information. Instruments and sensors useful for flood detection are only available in low resolution, low latency satellites with region re-visit periods of up to 16 days, making flood alerting systems that use such satellites unreliable. This work presents H2O-Network, a self supervised deep learning method to segment floods from satellites and aerial imagery by bridging domain gap between low and high latency satellite and coar…
Carted；NVIDIA（英伟达）；Frontier Development Lab（前沿发展实验室）；SpaceML
Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies. The NASA Impact Flood Detection competition tasked participants with predicting flooded pixels after training with synthetic aperture radar (SAR) images in a supervised setting. We propose a semi-supervised learning pseudo-labeling scheme that derives confidence estimates from U-Net ensembles, progressively improving accuracy. Concretely, we use a cyclical approach involving multiple stages (1) training an ensemble model of multiple U-Net architectures with the …
Enhancing Geo-localization for Crowdsourced Flood Imagery via LLM-Guided Attention
Department of Urban Planning and Design, The University of Hong Kong（香港大学城市规划与设计系）；Urban Systems Institute, The University of Hong Kong（香港大学都市系统研究所）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（香港理工大学土木与环境工程系）
LLM-Powered Flood Depth Estimation from Social Media Imagery: A Vision-Language Model Framework with Mechanistic Interpretability for Transportation Resilience
EvaNet: Elevation-Guided Flood Extent Mapping on Earth Imagery (Extended Version)
Indiana University Bloomington（印第安纳大学伯明顿分校）；The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；University of Florida（佛罗里达大学）；St. Cloud State University（圣克劳德州立大学）；Auburn University（奥本大学）
Cloud to Street；Rutgers University（罗格斯大学）；European Space Agency（欧洲空间局）
To address the mounting destruction caused by floods in climate-vulnerable regions, we propose Street to Cloud, a machine learning pipeline for incorporating crowdsourced ground truth data into the segmentation of satellite imagery of floods. We propose this approach as a solution to the labor-intensive task of generating high-quality, hand-labeled training data, and demonstrate successes and failures of different plausible crowdsourcing approaches in our model. Street to Cloud leverages community reporting and machine learning to generate novel, near-real time insights into the extent of floo…
Generating Physically-Consistent Satellite Imagery for Climate Visualizations
Massachusetts Institute of Technology（麻省理工学院）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；University of British Columbia（不列颠哥伦比亚大学）；Peaceful Society, Science and Innovation Foundation（和平协会科学与创新基金会）；University of Granada（格拉纳达大学）；Scripps Institute of Oceanography（斯克里普斯海洋研究所）；US Forest Service（美国林务局）；University of Oxford（牛津大学）；Riot Games（拳头游戏）；Pasteur Labs（巴斯德实验室）；Surgical Data Science Collective（外科数据科学集体）
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
Urban Flood Observations: A hand-labeled training and validation dataset of post-flood inundation
Pacific Northwest National Laboratory（太平洋西北国家实验室）；University of Arizona（亚利桑那大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Utah State University（犹他州立大学）；Massachusetts Institute of Technology（麻省理工学院）；Columbia University（哥伦比亚大学）；University of Virginia（弗吉尼亚大学）
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…
Evaluating the effects of preprocessing, method selection, and hyperparameter tuning on SAR-based flood mapping and water depth estimation
EDF R&D（EDF研究与发展）；Laboratoire National d’Hydraulique et Environnement (LNHE)（国家水利与环境实验室）；ENPC（巴黎综合理工学院）；Institut Polytechnique de Paris（巴黎理工学院）；Inria（法国国家信息与自动化技术研究所）
FM-LC: A Hierarchical Framework for Urban Flood Mapping by Land Cover Identification Models
Department of Environmental Sciences, College of Natural and Health Sciences, Zayed University（环境科学系，自然与健康科学学院，扎耶德大学）；Computer Science department of Arizona State University（亚利桑那州立大学计算机科学系）
Mapping Global Floods with 10 Years of Satellite Radar Data
Microsoft AI for Good Research Lab（微软人工智能公益研究院）；Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison（威斯康星大学麦迪逊分校合作气象卫星研究所）
Building structures detection and information about these buildings in aerial images is an important solution for city planning and management, land use analysis. It can be the center piece to answer important questions such as planning evacuation routes in case of an earthquake, flood management, etc. These applications rely on being able to accurately retrieve up-to-date information. Being able to accurately detect buildings in a bounding box centered on a specific latitude-longitude value can help greatly. The key challenge is to be able to detect buildings which can be commercial, industri…
Large scale cross-regional remote sensing flood monitoring framework for operative mapping and impact analysis
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）；Trofimuk Institute of Petroleum Geology and Geophysics SB RAS（俄罗斯科学院西伯利亚分院特罗菲穆克石油天然气地质与地球物理研究所）；King Fahd University of Petroleum and Minerals（法赫德国王石油与矿产大学）；Tyumen Industrial University（秋明工业大学）；Huawei Russian Research Institute（华为俄罗斯研究院）
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…
Leveraging AI multimodal geospatial foundation models for improved near-real-time flood mapping at a global scale
North Carolina State University（北卡罗来纳州立大学）；LabCorp（实验室集团）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Earth and Life Institute, UCLouvain（鲁汶天主教大学地球与生命研究所）；University of Helsinki（赫尔辛基大学）；Clark University（克拉克大学）；Thünen Institute of Farm Economics（蒂嫩农场经济研究所）
FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images
Annam.AI CoE, MoE, Indian Institute of Technology Ropar, Punjab, India（Annam.AI联合研究所、教育部、印度理工学院罗帕尔分校）；Department of Mathematical Sciences, Rajiv Gandhi Institute of Petroleum Technology, Jais, UP, India（数学科学系、拉贾吉安石油技术学院）；Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Punjab, India（计算机科学与工程系、印度理工学院罗帕尔分校）
The Technical University of Kenya（肯尼亚技术大学）；IRISA（法国国家信息与自动化研究所雷恩分院（IRISA））；Université Bretagne Sud（南布列塔尼大学）
Deep-learning-based image classification and object detection has been applied successfully to tree monitoring. However, studies of tree crowns and fallen trees, especially on flood inundated areas, remain largely unexplored. Detection of degraded tree trunks on natural environments such as water, mudflats, and natural vegetated areas is challenging due to the mixed colour image backgrounds. In this paper, Unmanned Aerial Vehicles (UAVs), or drones, with embedded RGB cameras were used to capture the fallen Acacia Xanthophloea trees from six designated plots around Lake Nakuru, Kenya. Motivated…
Global surface water detection in very-high-resolution (VHR) satellite imagery can directly serve major applications such as refined flood mapping and water resource assessment. Although achievements have been made in detecting surface water in small-size satellite images corresponding to local geographic scales, datasets and methods suitable for mapping and analyzing global surface water have yet to be explored. To encourage the development of this task and facilitate the implementation of relevant applications, we propose the GLH-water dataset that consists of 250 satellite images and manual…
The Ohio State University（俄亥俄州立大学）；Byrd Polar and Climate Research Center（伯德极地与气候研究中心）
Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and …
Large-scale flood modeling and forecasting with FloodCast
Technical University of Munich（慕尼黑工业大学）；University of Bristol（布里斯托尔大学）；Institute of Mountain Hazards and Environment, Chinese Academy of Sciences（中国科学院成都山地灾害与环境研究所）
