快速震后损伤评估对救援与资源规划至关重要。但现有遥感方法依赖成本高昂的航空影像与专家标注，仅能生成用于早期评估的二值损伤图。尽管来自社交网络的地面图像为填补这一缺口提供了宝贵数据源，但目前仍缺乏适用于该任务的大规模像素级标注数据集。我们推出了EIDSeg，这是首个专门面向震后社交媒体影像的大规模语义分割数据集。该数据集包含2008至2023年间9次大地震的3266张图像，标注涵盖五类基础设施损伤类别：未受损建筑、受损建筑、损毁建筑、未受损道路、受损道路。我们提出了一套实用的三阶段跨学科标注规范及配套标注指南，可支持非专家标注人员完成一致性分割，标注者间一致性超过70%。我们对多个当前最优的分割模型进行了基准测试，发现仅编码器掩码Transformer（Encoder-only Mask Transformer, EoMT）表现最佳，平均交并比（Mean Intersection over Union, mIoU）达80.8%。通过解锁社交网络丰富的地面视角，本研究为震后场景下更快速、更细粒度的损伤评估铺平了道路。
英文摘要：
Rapid post-earthquake damage assessment is crucial for rescue and resource planning. Still, existing remote sensing methods depend on costly aerial images, expert labeling, and produce only binary damage maps for early-stage evaluation. Although ground-level images from social networks provide a valuable source to fill this gap, a large pixel-level annotated dataset for this task is still unavailable. We introduce EIDSeg, the first large-scale semantic segmentation dataset specifically for post-earthquake social media imagery. The dataset comprises 3,266 images from nine major earthquakes (2008-2023), annotated across five classes of infrastructure damage: Undamaged Building, Damaged Building, Destroyed Building, Undamaged Road, and Damaged Road. We propose a practical three-phase cross-disciplinary annotation protocol with labeling guidelines that enables consistent segmentation by non-expert annotators, achieving over 70% inter-annotator agreement. We benchmark several state-of-the-art segmentation models, identifying Encoder-only Mask Transformer (EoMT) as the top-performing method with a Mean Intersection over Union (mIoU) of 80.8%. By unlocking social networks' rich ground-level perspective, our work paves the way for a faster, finer-grained damage assessment in the post-earthquake scenario.
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
In the aftermath of an earthquake, rapid structural inspections are required to get citizens back in to their homes and offices in a safe and timely manner. These inspections gfare typically conducted by municipal authorities through structural engineer volunteers. As manual inspec-tions can be time consuming, laborious and dangerous, research has been underway to develop methods to help speed up and increase the automation of the entire process. Researchers typi-cally envisage the use of unmanned aerial vehicles (UAV) for data acquisition and computer vision for data processing to extract act…
Reinforced concrete buildings are commonly used around the world. With recent earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are crucial for building owners and policy makers to make informed risk management decisions. To improve the efficiency of such inspection, advanced computer vision techniques based on convolution neural networks have been adopted in recent research to rapidly quantify the damage state of structures. In this paper, an advanced object detection neural network, named YOLO-v2, is implemented which achieves 98.2% and 84.5% average precis…
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…
Birla Institute of Technology & Science, Pilani（贝拉理工学院（皮拉尼））
Classification of the extent of damage suffered by a building in a seismic event is crucial from the safety perspective and repairing work. In this study, authors have proposed a CNN based autonomous damage detection model. Over 1200 images of different types of buildings-1000 for training and 200 for testing classified into 4 categories according to the extent of damage suffered. Categories are namely, no damage, minor damage, major damage, and collapse. Trained network tested by the application of various algorithms with different learning rates. The most optimum results were obtained on the…
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）
The role of unmanned vehicles for searching and localizing the victims in disaster impacted areas such as earthquake-struck zones is getting more important. Self-navigation on an earthquake zone has a unique challenge of detecting irregularly shaped obstacles such as road cracks, debris on the streets, and water puddles. In this paper, we characterize a number of state-of-the-art FCN models on mobile embedded platforms for self-navigation at these sites containing extremely irregular obstacles. We evaluate the models in terms of accuracy, performance, and energy efficiency. We present a few op…
School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；Yibin Institute of University of Electronic Science and Technology of China（电子科技大学宜宾研究院）；Intelligent Terminal Key Laboratory of Sichuan Province（四川省智能终端重点实验室）；Lutgert College, Florida Gulf Coast University（佛罗里达海湾海岸大学勒特格特学院）
Zymergen（Zymergen公司）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Berkeley Seismological Laboratory（伯克利地震实验室）；AT&T（美国电话电报公司）；University of California, Berkeley（加州大学伯克利分校）
After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually l…
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）
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）
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）
Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images
Department of Electrical, Computer and Biomedical Engineering, University of Pavia（电气、计算机与生物医学工程系，帕维亚大学）；EUCENTRE Foundation, European Centre for Training and Research in Earthquake Engineering（欧洲地震工程培训与研究中心基金会）
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 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…
As one of the most destructive disasters in the world, earthquake causes death, injuries, destruction and enormous damage to the affected area. It is significant to detect buildings after an earthquake in response to reconstruction and damage evaluation. In this research, we proposed an automatic rooftop detection method based on the convolutional neural network (CNN) to extract buildings in the city of Christchurch and tuned hyperparameters to detect small detached houses from the aerial image. The experiment result shows that our approach can effectively and accurately detect and segment bui…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Current methods of practice for inspection of civil infrastructure typically involve visual assessments conducted manually by trained inspectors. For post-earthquake structural inspections, the number of structures to be inspected often far exceeds the capability of the available inspectors. The labor intensive and time consuming natures of manual inspection have engendered research into development of algorithms for automated damage identification using computer vision techniques. In this paper, a novel damage localization and classification technique based on a state of the art computer visi…
Google Research（谷歌研究院）；Stanford University（斯坦福大学）；United Nations World Food Programme（联合国世界粮食计划署）
To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which indicate what buildings and population centers have been most affected. Recent research combines machine learning with remote sensing to automatically extract such information from satellite imagery, reducing manual labor and turn-around time. A major impediment to using machine learning methods in real disaster response scenarios is the difficulty of obtaining a sufficient amount of labeled data to train a model for a…
This paper presents a few comprehensive experimental studies for automated Structural Damage Detection (SDD) in extreme events using deep learning methods for processing 2D images. In the first study, a 152-layer Residual network (ResNet) is utilized to classify multiple classes in eight SDD tasks, which include identification of scene levels, damage levels, material types, etc. The proposed ResNet achieved high accuracy for each task while the positions of the damage are not identifiable. In the second study, the existing ResNet and a segmentation network (U-Net) are combined into a new pipel…
Silesian University of Technology（西里西亚工业大学）；Polish Academy of Sciences（波兰科学院）
The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem of detecting damage after sudden events by using an interconnected set of deep machine learning models organized in a single pipeline and allowing for easy modification and swapping models seamlessly. Models in the pipeline were trained with a synthetic dataset and were adapted to be further evalua…
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）
Massachusetts Institute of Technology（麻省理工学院）；Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）
Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety o…
University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Image data has a great potential of helping post-earthquake visual inspections of civil engineering structures due to the ease of data acquisition and the advantages in capturing visual information. A variety of techniques have been applied to detect damages automatically from a close-up image of a structural component. However, the application of the automatic damage detection methods become increasingly difficult when the image includes multiple components from different structures. To reduce the inaccurate false positive alarms, critical structural components need to be recognized first, an…
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…
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；University of Petroleum and Energy Studies（石油与能源研究大学）
Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. The vulnerability of a building to earthquake can be assessed through inspection that takes into account the expected damage progression of the associated component and the component's contribution to structural system performance. Most of these inspections are done manually, leading to high utilization of manpower, time, and cost. This paper proposes a methodology to auto…
Microsoft Corporation（微软公司）；Columbia University（哥伦比亚大学）
The machine learning community has recently had increased interest in the climate and disaster damage domain due to a marked increased occurrences of natural hazards (e.g., hurricanes, forest fires, floods, earthquakes). However, not enough attention has been devoted to mitigating probable destruction from impending natural hazards. We explore this crucial space by predicting building-level damages on a before-the-fact basis that would allow state actors and non-governmental organizations to be best equipped with resource distribution to minimize or preempt losses. We introduce PreDisM that em…
Earthquakes and tropical cyclones cause the suffering of millions of people around the world every year. The resulting landslides exacerbate the effects of these disasters. Landslide detection is, therefore, a critical task for the protection of human life and livelihood in mountainous areas. To tackle this problem, we propose a combination of satellite technology and Deep Neural Networks (DNNs). We evaluate the performance of multiple DNN-based methods for landslide detection on actual satellite images of landslide damage. Our analysis demonstrates the potential for a meaningful social impact…
