Multisense

if you use this dataset please cite the following paper:

GAZADeepDav: A High Resolution Geotagged Satellite Imagery Dataset For Analyzing War-Induced Damage
M. Bouabid and M. Farah, "GAZADeepDav: A High Resolution Geotagged Satellite Imagery Dataset For Analyzing War-Induced Damage," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 8876-8879, doi: 10.1109/IGARSS53475.2024.10642306.keywords: {Training;Accuracy;Recurrent neural networks;Image resolution;Geoscience and remote sensing;Satellite images;Task analysis;Satellite images;Deep learning;Gaza War;damage Detection;SqueezeNet;BiLSTM},

This dataset, named MultiSense, is designed to enhance disaster response by providing comprehensive data from multiple sources. It comes in two versions: balanced and unbalanced. The dataset consists of five distinct classes, each representing different types of events or conditions:

Syria Earthquake: This class includes imagery and video footage related to earthquake damage. The data captures the aftermath of seismic events, showcasing various degrees of destruction.

Gaza War: This class contains data depicting war-related damage. It includes imagery and videos from conflict zones, highlighting the impact of warfare on infrastructure and urban areas.

Hurricane Harvey: This class encompasses data related to hurricane damage. It includes imagery and footage showing the effects of strong winds, flooding, and storm surges associated with hurricanes.

Libya Flood: This class features imagery and videos of flood damage. It documents areas affected by flooding, capturing the extent of water damage to buildings, roads, and landscapes.

No Damage: This class provides imagery and footage of areas with no significant damage. It serves as a control group, representing normal conditions without the impact of natural disasters or conflicts.

若您使用本数据集，敬请引用以下论文：
GAZADeepDav：一种用于分析战争诱导破坏的高分辨率地理标记卫星影像数据集
作者：M. Bouabid 和 M. Farah
论文题目：GAZADeepDav：一种用于分析战争诱导破坏的高分辨率地理标记卫星影像数据集
发表于：2024 IEEE 国际地学遥感会议（IGARSS 2024），雅典，希腊，2024年，第8876-8879页，DOI：10.1109/IGARSS53475.2024.10642306
关键词：训练；准确度；循环神经网络；图像分辨率；地学遥感；卫星影像；任务分析；卫星影像；深度学习；加沙战争；破坏检测；SqueezeNet；双向长短期记忆网络（BiLSTM）。
本数据集名为MultiSense，旨在通过提供多源数据的综合信息来提升灾害响应能力。数据集分为平衡和不平衡两种版本，包含五大类不同的类别，每类代表不同类型的事件或状态：
叙利亚地震：本类别包含与地震破坏相关的影像和视频资料，数据捕捉了地震事件的影响，展示了不同程度的破坏情况。
加沙战争：本类别包含描绘战争相关破坏的数据，包括冲突区域的影像和视频，突显了战争对基础设施和城市区域的影响。
哈维飓风：本类别涵盖与飓风破坏相关的数据，包括展示强风、洪水和风暴潮影响的影像和视频资料。
利比亚洪水：本类别展示洪水破坏的影像和视频，记录了受洪水影响区域，捕捉了水对建筑、道路和景观的损害程度。
无破坏：本类别提供无显著破坏区域的影像和视频，作为对照组，代表正常条件，不受自然灾害或冲突的影响。