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This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affecte...

Spatial accessibility to healthcare services is commonly determined by three core components: demand, supply, and traffic speed. Although understanding which factors contribute to accessibility changes can help prioritize interventions to enhance acc...

Forecasting the spatio-temporal evolutions of cellular traffic is crucial for urban management. However, achieving accurate forecasting is challenging due to “complex correlation modeling” and “model-blindness” issues. Specifi...

Pedestrian trajectory prediction plays a vital role in autonomous driving and intelligent surveillance systems. Graph neural networks (GNNs) have shown remarkable effectiveness in this task by explicitly modeling social interactions among pedestrians...

Rebalancing shared bikes poses a significant challenge for dockless bike-sharing (DLBS) operators, as inevitable spatiotemporal mismatches between demand and supply lead to high redistribution costs. Despite its operational significance, empirical re...

The walking accessibility of primary healthcare institutions (PHCIs) is a pivotal determinant of health equity. However, prior studies often lack a comprehensive assessment that integrates the spatiotemporal dynamics of both multi-faceted supply and...

Temporal reasoning is an important part of the field of time geography and spatio-temporal data science. Recent advances in qualitative temporal reasoning have developed a set of 74 relations that apply between discretized time intervals of at least...

In the context of the “Dual Carbon” goals, accurately predicting the spatiotemporal evolution of urban Net Primary Productivity (NPP) is crucial for resilient urban planning. While recent studies have coupled land use models with ecosyste...

Ensuring the safety of autonomous driving systems (ADS) requires scenario-based testing that reflects the complexity and variability of real-world driving conditions. However, the nondeterministic nature of actual traffic environments makes physical...

This article studies the application of deep neural networks for automatic building detection in aerial RGB images. Special focus is put on accuracy robustness in both well-structured and poorly planned urban scenarios, which pose significant challen...