Urban sustainability is shaped by complex and dynamic interactions between environmental processes and human activities. These coupled human–environment interactions influence urban climate, land-use change, ecosystem services, carbon emissions, resource consumption, mobility, environmental exposure, inequality, and resilience. Recent advances in Earth observation, geospatial big data, in-situ and crowdsourced sensing, cloud computing, machine learning, and artificial intelligence provide unprecedented opportunities to observe, model and predict these interactions across spatial and temporal scales.
This session invites contributions that use new observations, integrated datasets, data-driven analytics, and AI-based approaches to advance the understanding of urban human–environment systems. We particularly welcome studies addressing multi-source urban data integration, machine learning and GeoAI, urban climate and heat, land-use change, urban ecosystems, carbon emissions, mobility, environmental exposure, inequality, vulnerability, resilience, open datasets, benchmarks, uncertainty assessment, explainable AI, and reproducible workflows.
The session aims to foster interdisciplinary exchange among remote sensing, geospatial science, urban studies, environmental science, climate science, and data science communities. We encourage contributions from local to global scales, especially those demonstrating how new data and analytics can reveal interactions between people and urban environments and support more sustainable and resilient urban futures.