The detection of flooded areas using high-resolution synthetic aperture radar (SAR) imagery is a critical task with applications in crisis and disaster management, as well as environmental resource planning. However, the complex nature of SAR images presents a challenge that often leads to an overestimation of the flood extent. To address this issue, we propose a novel differential attention metric-based network (DAM-Net) in this study. The DAM-Net comprises two key components: a weight-sharing Siamese backbone to obtain multi-scale change features of multi-temporal images and tokens containing high-level semantic information of water-body changes, and a temporal differential fusion (TDF) module that integrates semantic tokens and change features to generate flood maps with reduced speckle noise. Specifically, the backbone is split into multiple stages. In each stage, we design three modules, namely, temporal-wise feature extraction (TWFE), cross-temporal change attention (CTCA), and temporal-aware change enhancement (TACE), to effectively extract the change features. In TACE of the last stage, we introduce a class token to record high-level semantic information of water-body changes via the attention mechanism. Another challenge faced by data-driven deep learning algorithms is the limited availability of flood detection datasets. To overcome this, we have created the S1GFloods open-source dataset, a global-scale high-resolution Sentinel-1 SAR image pairs dataset covering 46 global flood events between 2015 and 2022. The experiments on the S1GFloods dataset using the proposed DAM-Net showed top results compared to state-of-the-art methods in terms of overall accuracy, F1-score, and IoU, which reached 97.8%, 96.5%, and 93.2%, respectively. Our dataset and code will be available online at https://github.com/Tamer-Saleh/S1GFlood-Detection.
Urban Flood Mapping Using Satellite Synthetic Aperture Radar Data: A Review of Characteristics, Approaches and Datasets
Technical University of Munich（慕尼黑工业大学）；Vienna University of Technology（维也纳工业大学）；ETH Zurich（苏黎世联邦理工学院）；Wuhan University（武汉大学）；DiXplorer AG（4DiXplorer公司）；Luxembourg Institute of Science and Technology (LIST)（卢森堡科学技术研究院）
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）
Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency response and disaster management. In this work, we propose a flood detection network using bi-temporal SAR acquisitions. The proposed segmentation network has an encoder-decoder architecture with two Siamese encoders for pre and post-flood images. The network's feature maps are fused and enhanced using attention blocks to achieve more accurate detection of the flooded areas. Our proposed network is evaluated on publicly …
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…
Benchmarking Attention Mechanisms and Consistency Regularization Semi-Supervised Learning for Post-Flood Building Damage Assessment in Satellite Images
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 …
Leveraging Self-Supervised Features for Efficient Flooded Region Identification in UAV Aerial Images
Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India（电子与通信工程系，Manipal理工学院，Manipal高等教育学院，Manipal，印度）
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）
Human civilization has an increasingly powerful influence on the earth system. Affected by climate change and land-use change, natural disasters such as flooding have been increasing in recent years. Earth observations are an invaluable source for assessing and mitigating negative impacts. Detecting changes from Earth observation data is one way to monitor the possible impact. Effective and reliable Change Detection (CD) methods can help in identifying the risk of disaster events at an early stage. In this work, we propose a novel unsupervised CD method on time series Synthetic Aperture Radar~…
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…
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）
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…
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…
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）
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 …
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…
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））
Floods are large-scale natural disasters that often induce a massive number of deaths, extensive material damage, and economic turmoil. The effects are more extensive and longer-lasting in high-population and low-resource developing countries. Early Warning Systems (EWS) constantly assess water levels and other factors to forecast floods, to help minimize damage. Post-disaster, disaster response teams undertake a Post Disaster Needs Assessment (PDSA) to assess structural damage and determine optimal strategies to respond to highly affected neighbourhoods. However, even today in developing coun…
Flood Data Analysis on SpaceNet 8 Using Apache Sedona
Renmin University of China（中国人民大学）；Connected Robotics Inc.（Connected Robotics公司）；National Taiwan University（国立台湾大学）；China Unicom Research Institute（中国联通研究院）；Tohoku University（东北大学）；International Research Institute of Disaster Science（灾害科学国际研究所）
A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake
Department of Electrical, Computer and Biomedical Engineering, University of Pavia（帕维亚大学电气、计算机与生物医学工程系）；Department of Engineering, University of Sannio（萨诺尼奥大学工程系）；Italian Space Agency (ASI)（意大利航天局）
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…
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）
Multiclass Post-Earthquake Building Assessment Integrating High-Resolution Optical and SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers
Department of Civil and Environmental Engineering, University of Houston, TX, USA（土木与环境工程系，德克萨斯大学）；Department of Electrical and Computer Engineering, University of Houston, TX, USA（电气与计算机工程系，德克萨斯大学）；Department of Earth and Atmospheric Science, University of Houston, TX, USA（地球与大气科学系，德克萨斯大学）；German Aerospace Center (DLR), Microwaves and Radar Institute, Munich, Germany（德国航天中心（DLR）微波与雷达研究所）
Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images
Graphics-Visualization-Computing Lab, International Institute of Information Technology Bangalore, Karnataka 560100, India（国际信息学院信息技术研究所，班加罗尔，卡纳塔克邦，印度）；Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India（电子与通信工程系，曼尼普尔理工学院，曼尼普尔高等教育学院，曼尼普尔，卡纳塔克邦，印度）
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…
Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR
Télécom ParisTech（巴黎高等电信学院）；Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；Bordeaux INP（波尔多国立理工学院）；University of Bordeaux（波尔多大学）；UJM-Saint-Etienne（圣埃蒂安大学）；Institut d Optique Graduate School（光学研究所研究生院）；University of Lyon（里昂大学）
本文提出基于 RABASAR 去噪与简化广义似然比 $S_{GLR}$ 的多时相 SAR 变化检测框架，并结合变化幅度、谱聚类分类及 REACTIV 可视化，在多类地表变化中表现良好。
FloodNet is a high-resolution image dataset acquired by a small UAV platform, DJI Mavic Pro quadcopters, after Hurricane Harvey. The dataset presents a unique challenge of advancing the damage assessment process for post-disaster scenarios using unlabeled and limited labeled dataset. We propose a solution to address their classification and semantic segmentation challenge. We approach this problem by generating pseudo labels for both classification and segmentation during training and slowly incrementing the amount by which the pseudo label loss affects the final loss. Using this semi-supervis…
