首页|Time-series satellite remote sensing reveals gradually increasing war damage in the Gaza Strip

Time-series satellite remote sensing reveals gradually increasing war damage in the Gaza Strip

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War-related urban destruction is a significant global concern,impacting national security,social stability,people's survival and economic development.The effects of urban geomorphology and complex geological contexts during conflicts,characterized by different levels of structural damage,are not yet fully understood globally.Here we report how integrating deep learning with data from the independently developed LuoJia3-01 satellite enables near real-time detection of explosions and assessment of different building damage levels in the Israel-Palestine conflict.We found that the damage continually increased from 17 October 2023 to 2 March 2024.We found 3747 missile craters with precision positions and sizes,and timing on vital infrastructure across five governorates in the Gaza Strip on 2 March 2024,providing accurate estimates of potential unexploded ordnance locations and assisting in demining and chemical decontamination.Our findings reveal a significant increase in damage to residential and educational structures,accounting for 58.4％of the total-15.4％destroyed,18.7％severely damaged,11.8％moderately damaged and 12.5％slightly damaged-which exacerbates the housing crisis and potential population displacement.Additionally,there is a 34.1％decline in the cultivated area of agricultural land,posing a risk to food security.The LuoJia3-01 satellite data are crucial for impartial conflict monitoring,and our innovative methodology offers a cost-effective,scalable approach to assess future conflicts in various global contexts.These first-time findings highlight the urgent need for an immediate ceasefire to prevent further damage and support the release of hostages and subsequent reconstruction efforts.

war damage monitoringsatellite remote sensingmissile crater detectiontype of damaged buildingbuilding damage level detectionGaza Strip

Shimaa Holail、Tamer Saleh、Xiongwu Xiao、Jing Xiao、Gui-Song Xia、Zhenfeng Shao、Mi Wang、Jianya Gong、Deren Li

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State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430072,China

Geomatics Engineering Department,Faculty of Engineering at Shoubra,Benha University,Cairo 11629,Egypt

School of Computer Science,Wuhan University,Wuhan 430072,China

Collaborative Innovation Center of Geospatial Technology,Wuhan 430079,China

National Engineering Research Center for Multi-media Software,Institute of Artificial Intelligence,Wuhan University,Wuhan 430072,China

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National Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Hubei Province,ChinaNatural Science Foundation of Hubei Province,ChinaKey Research and Development Program of Hubei Province,ChinaChutian Scholar Program of Hubei ProvinceYellow Crane Talent Scheme

2023YFB3906102421014494209001261825103U22B2011623251112022CFB7732020CFA0012022BAA048

2024

10.1093/nsr/nwae304

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ISSN：
年,卷(期)：2024.11(9)