Satellite images are an extremely valuable resource in the aftermath of natural disasters such as hurricanes and tsunamis where they can be used for risk assessment and disaster management. In order to provide timely and actionable information for disaster response, in this paper a framework utilising segmentation neural networks is proposed to identify impacted areas and accessible roads in post-disaster scenarios. The effectiveness of pretraining with ImageNet on the task of aerial image segmentation has been analysed and performances of popular segmentation models compared. Experimental results show that pretraining on ImageNet usually improves the segmentation performance for a number of models. Open data available from OpenStreetMap (OSM) is used for training, forgoing the need for time-consuming manual annotation. The method also makes use of graph theory to update road network data available from OSM and to detect the changes caused by a natural disaster. Extensive experiments on data from the 2018 tsunami that struck Palu, Indonesia show the effectiveness of the proposed framework. ENetSeparable, with 30% fewer parameters compared to ENet, achieved comparable segmentation results to that of the state-of-the-art networks.
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）
xBD: A Dataset for Assessing Building Damage from Satellite Imagery
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）
我们提出了xBD，一个新的，大规模的数据集，用于人道主义援助和灾难恢复研究的变化检测和建筑物损坏评估的进步。自然灾害响应需要准确了解受灾地区受损的建筑物。目前的应对战略要求在灾害发生后24-48小时内进行现场损失评估。将航空图像与计算机视觉算法相结合，以评估损害并减少对人类生命的潜在危险，这方面存在着巨大的潜力。xBD与多个灾害响应机构合作，提供各种灾害事件的事件前和事件后卫星图像，包括建筑物多边形，损坏程度的顺序标签和相应的卫星元数据。此外，数据集包含环境因素（如火、水和烟）的边界框和标签。xBD是迄今为止最大的建筑物损坏评估数据集，包含45，362 km \textsuperscript{2}图像中的850，736个建筑物注释。
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）
Open-Vocabulary vs Supervised Learning Methods for Post-Disaster Visual Scene Understanding
Department of Informatics and Telematics, Harokopio University of Athens（信息与电信学系，雅典哈罗科波斯大学）；Department of Networks and Digital Media, Kingston University（网络与数字媒体系，金士顿大学）；Department of Electrical and Computer Engineering, University of Western Macedonia（电子与计算机工程系，西马其顿大学）；Archimedes, Athena Research Center（阿基米德，雅典研究中心）
Concept-based explanations of Segmentation and Detection models in Natural Disaster Management
Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany（人工智能系，弗劳恩霍夫海因里希·赫兹研究所，柏林，德国）；Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece（信息系，亚里士多德大学，塞萨洛尼基，希腊）
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）
