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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. [ABSTRACT FROM AUTHOR]

Road traffic crashes are becoming thorny issues being faced worldwide. Traffic crashes are spatiotemporal events and the research on the spatiotemporal patterns and variation trends of traffic crashes has been carried out. However, the impact of built environment on traffic crash spatiotemporal trends has not received much attention. Moreover, the spatial non-stationarity between the variation trends of traffic crashes and their influencing factors is usually neglected. To make up for the lack of analysis of built environment factors influencing spatiotemporal hotspot trends in traffic crashes, this paper proposed a method of "ST-GWLR" for analyzing the influence of built environment factors on spatiotemporal hotspot trends of traffic crashes by combining the spatiotemporal hotspot trend analysis and Geographically Weighted Logistic Regression (GWLR) modeling methods. Firstly, the traffic crash spatiotemporal hotspot trends were explored using the space-time cube model, hotspot analysis, and Mann-Kendall trend test. Then, the GWLR was introduced to capture the spatial non-stationarity neglected by the classic Global Logistic Regression (GLR) model, to improve the accuracy of the model estimation. GWLR model is used for the first time to analyze the significant local correlation between the traffic crash spatiotemporal hotspot trends and the built environment factors, to accurately and effectively identify the built environment factors that have significant influences on the hotspot trends of traffic crashes. The performance of the GWLR models and GLR models was examined and compared sufficiently. The results showed that the proposed ST-GWLR, which captured spatial non-stationarity, performed better than the classic GLR combined with spatiotemporal analysis, and improved the prediction accuracy of the models by 14.9%, 13.9%, and 15.1%, respectively. There were significant local correlations between intensifying hotspots and persistent hotspots of traffic crashes and the built environment factors. The findings of this paper have positive implications for traffic safety management and urban built environment planning. [ABSTRACT FROM AUTHOR]

Many existing efforts have taken advantage of large-scale spatial-temporal data to partition cities via constructed human interaction networks. However, few studies focus on communities emerging between adjacent cities in big urban agglomerations, which we call "cross-city" communities. In this study, we introduce a novel framework to detect cross-city communities in urban agglomerations under different scales leveraging a large number of fine-grained mobile signaling data aiming to break the original administrative boundaries. Taking the Pearl River Delta (PRD) urban agglomeration in China as study area, we investigate the existence of potential communities at three scales, i.e. city-group level, city level and sub-city level. The partition results are expected to benefit transportation planning, urban zoning and administrative boundary re-delineation. The results from our study highlight the necessity of considering cross-city communities and their scale effects when examining urban spatial interactions. [ABSTRACT FROM AUTHOR]

The Ultra-Wideband (UWB) Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution. However, the None-Line-of-Sight (NLOS) propagation may reduce the ranging accuracy for UWB localization system in indoor environment. So it is important to identify LOS and NLOS propagations before taking proper measures to improve the UWB localization accuracy. In this paper, a deep learning-based UWB NLOS/LOS classification algorithm called FCN-Attention is proposed. The proposed FCN-Attention algorithm utilizes a Fully Convolution Network (FCN) for improving feature extraction ability and a self-attention mechanism for enhancing feature description from the data to improve the classification accuracy. The proposed algorithm is evaluated using an open-source dataset, a local collected dataset and a mixed dataset created from these two datasets. The experiment result shows that the proposed FCN-Attention algorithm achieves classification accuracy of 88.24% on the open-source dataset, 100% on the local collected dataset and 92.01% on the mixed dataset, which is better than the results from other evaluated NLOS/LOS classification algorithms in most scenarios in this paper. [ABSTRACT FROM AUTHOR]