article · National Science Review
Integrating deep learning with imagery from the LuoJia3-01 satellite enables near real-time detection of explosions and assessment of urban building damage during conflict. Analysis of the Israel-Palestine conflict between 17 October 2023 and 2 March 2024 revealed continually increasing destruction across five governorates in the Gaza Strip. The approach identified 3,747 missile craters with precise coordinates, sizes, and timestamps on vital infrastructure. These detailed measurements pinpoint potential unexploded ordnance locations to assist demining and chemical decontamination operations. Findings indicate that residential and educational structures suffered severe harm, representing 58.4 percent of total damage across destroyed, severely damaged, moderately damaged, and slightly damaged categories. Additionally, cultivated agricultural land decreased by 34.1 percent, threatening food security. This monitoring method provides a scalable, cost-effective framework for impartial conflict evaluation and planning subsequent reconstruction efforts.
Armed conflict causes widespread harm to civilian infrastructure and farmland, driving displacement and food insecurity. Automated satellite analysis delivers objective, near real-time data on destruction patterns. This information is vital for international organisations coordinating emergency aid, locating hazardous unexploded munitions, and preparing practical reconstruction strategies while avoiding the dangers of immediate on-the-ground surveys.
The method represents an applied and tested spatial analytics tool combining deep learning with satellite remote sensing. It is primarily applicable to humanitarian agencies, civil protection teams, and post-war reconstruction bodies requiring rapid mapping of hazards such as unexploded ordnance. Given its demonstrated performance in an active conflict, the workflow is well positioned for integration into commercial geospatial intelligence and risk-monitoring software platforms.
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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.
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DOI: 10.1093/nsr/nwae304
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