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 data augmentation method CutMix is exploited to tackle the challenge of hard classes. The proposed method achieves state-of-the-art performance on a large-scale building damage assessment dataset -- xBD.
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 …
Purdue University（普渡大学）；Lockheed Martin Space（洛克希德·马丁太空公司）
When disaster strikes, accurate situational information and a fast, effective response are critical to save lives. Widely available, high resolution satellite images enable emergency responders to estimate locations, causes, and severity of damage. Quickly and accurately analyzing the extensive amount of satellite imagery available, though, requires an automatic approach. In this paper, we present Siam-U-Net-Attn model - a multi-class deep learning model with an attention mechanism - to assess damage levels of buildings given a pair of satellite images depicting a scene before and after a disa…
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…
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）
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 …
Renmin University of China（中国人民大学）；Connected Robotics Inc.（互联机器人公司）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能项目中心）
In the field of post-disaster assessment, for timely and accurate rescue and localization after a disaster, people need to know the location of damaged buildings. In deep learning, some scholars have proposed methods to make automatic and highly accurate building damage assessments by remote sensing images, which are proved to be more efficient than assessment by domain experts. However, due to the lack of a large amount of labeled data, these kinds of tasks can suffer from being able to do an accurate assessment, as the efficiency of deep learning models relies highly on labeled data. Althoug…
Lebanese University（黎巴嫩大学）；Islamic University of Lebanon（黎巴嫩伊斯兰大学）；MINES ParisTech（巴黎高科矿业学院）；National Center for Remote Sensing - CNRS（法国国家科学研究中心国家遥感中心）
Buildings classification using satellite images is becoming more important for several applications such as damage assessment, resource allocation, and population estimation. We focus, in this work, on buildings damage assessment (BDA) and buildings type classification (BTC) of residential and non-residential buildings. We propose to rely solely on RGB satellite images and follow a 2-stage deep learning-based approach, where first, buildings' footprints are extracted using a semantic segmentation model, followed by classification of the cropped images. Due to the lack of an appropriate dataset…
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）
Carnegie Mellon University（卡内基梅隆大学）；Software Engineering Institute（软件工程研究所）；Defense Innovation Unit（国防创新部门）；Department of Defense（国防部）；CrowdAI, Inc.（CrowdAI公司）
We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-person damage assessments within 24-48 hours of a disaster. Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life. In collaboration with multiple disaster response agencies, xBD prov…
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…
Massachusetts Institue of Technology（麻省理工学院）；WL Research
Automatic change detection and disaster damage assessment are currently procedures requiring a huge amount of labor and manual work by satellite imagery analysts. In the occurrences of natural disasters, timely change detection can save lives. In this work, we report findings on problem framing, data processing and training procedures which are specifically helpful for the task of building damage assessment using the newly released xBD dataset. Our insights lead to substantial improvement over the xBD baseline models, and we score among top results on the xView2 challenge leaderboard. We relea…
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…
The usage of Unmanned Aerial Vehicles (UAVs) in the context of structural health inspection is recently gaining tremendous popularity. Camera mounted UAVs enable the fast acquisition of a large number of images often used for mapping, 3D model reconstruction, and as an assisting tool for inspectors. Due to the number of images captured during large scale UAV surveys, a manual image-based inspection analysis of entire assets cannot be efficiently performed by qualified engineers. Additionally, comparing defects to past inspections requires the retrieval of relevant images which is often impract…
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.
DamageArbiter: A Multimodal Arbitration Framework for Disaster Damage Assessment from Street-View Imagery
organization= Department of Geography, Texas A\&M University , city= College Station , country= USA；organization= Department of Landscape Architecture \& Urban Planning, Texas A\&M University , city= College Station , country= USA；organization= Department of Industrial；Systems Engineering, University of Florida , city= Gainesville , country= USA；organization= Spatial Sciences Institute, University of Southern California , city= Los Angeles , country= USA；organization= Department of Geography；Sustainability, University of Tennessee , city= Knoxville , country= USA
BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）；RIKEN Center for Advanced Intelligence Project (AIP), RIKEN（日本理化学研究院先进智能项目中心）；Department of Photogrammetry and Remote Sensing, ETH Zürich（苏黎世联邦理工学院测绘与遥感系）；Microsoft Research Asia（微软亚洲研究院）
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.
Bridge Damage Detection from Low-Light UAV Imagery via Degradation-Aware Mixture-of-Experts Enhancement
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Sustainability X-Lab, The University of Hong Kong（香港大学可持续发展X实验室）；School of Architecture, Building, and Civil Engineering, Loughborough University（拉夫堡大学建筑、建造与土木工程学院）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（香港科技大学土木与环境工程系）
Qatar Computing Research Institute（卡塔尔计算研究所）；Hamad bin Khalifa University（哈马德·本·哈利法大学）
We propose Nazr-CNN1, a deep learning pipeline for object detection and fine-grained classification in images acquired from Unmanned Aerial Vehicles (UAVs) for damage assessment and monitoring. Nazr-CNN consists of two components. The function of the first component is to localize objects (e.g. houses or infrastructure) in an image by carrying out a pixel-level classification. In the second component, a hidden layer of a Convolutional Neural Network (CNN) is used to encode Fisher Vectors (FV) of the segments generated from the first component in order to help discriminate between different lev…
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）
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）
Google AI（谷歌人工智能）；UN World Food Programme（联合国世界粮食计划署）
In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite images is slow and labor-intensive. In this work, we use machine learning to automate the detection of building damage in satellite imagery. We compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake. We…
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…
Accurate and fine-grained information about the extent of damage to buildings is essential for directing Humanitarian Aid and Disaster Response (HADR) operations in the immediate aftermath of any natural calamity. In recent years, satellite and UAV (drone) imagery has been used for this purpose, sometimes aided by computer vision algorithms. Existing Computer Vision approaches for building damage assessment typically rely on a two stage approach, consisting of building detection using an object detection model, followed by damage assessment through classification of the detected building tiles…
In this manuscript, an image analytics based deep learning framework for wind turbine blade surface damage detection is proposed. Turbine blade(s) which carry approximately one-third of a turbine weight are susceptible to damage and can cause sudden malfunction of a grid-connected wind energy conversion system. The surface damage detection of wind turbine blade requires a large dataset so as to detect a type of damage at an early stage. Turbine blade images are captured via aerial imagery. Upon inspection, it is found that the image dataset was limited and hence image augmentation is applied t…
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…
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…
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 Academy for Mathematics, Science, and Engineering（数学、科学与工程学院）
Natural disasters ravage the world's cities, valleys, and shores on a regular basis. Deploying precise and efficient computational mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, we take a machine learning-based remote sensing approach and train multiple convolutional neural networks (CNNs) to assess building damage on a per-building basis. We present a novel methodology of interpretable deep learning that seeks to explicitly investigate the most usef…
