International Research Center of Big Data for Sustainable Development Goals

Fine-Grained Tree Species Classification in Urban Forests via Phenology–Structure Synergistic Fusion of Multi-Source Remote Sensing Data

Urban forest tree species composition and spatial distribution are essential for refined greenspace management, ecosystem-service assessment, and forest-health monitoring. High-resolution imagery captures crown texture and spatial boundaries, multi-temporal NDVI reflects phenolog...

Detection of linear deformation features in arctic landfast ice using InSAR and deep learning

Interferometric Synthetic Aperture Radar (InSAR) provides a powerful tool for observing landfast-ice deformation. Linear deformation features (LDFs) are narrow structures associated with fracture development in landfast ice. However, studies on the automated detection of LDFs fro...

Assessing Land Cover Change and Forest Sustainability in a World Heritage Context: Evidence from the Okapi Wildlife Reserve

Long-term monitoring of land cover dynamics is essential for evaluating conservation outcomes in World Heritage sites, yet remains challenging in conflict-affected regions with limited field observations. This study aims to reconstruct annual land cover dynamics and assess forest...

Need-responsive accessibility supports equitable sustainable development and reshapes migration as evolving human needs

Realizing the Sustainable Development Goals’ commitment to “leave no one behind” requires accessibility that responds to evolving human needs, yet prevailing accessibility assessments remain static and sector-specific, overlooking hierarchical need evolution and behavioral feedba...

Evolution of water clarity across the Beijing-Tianjin-Hebei region over the past four decades (1984–2024): Driven by hydrological expansion and anthropogenic intervention

Given escalating water scarcity and rapid urbanization, characterizing the long-term evolution of surface water environments across the Beijing-Tianjin-Hebei (BTH) region is vital for coordinated regional development. Using Landsat imagery spanning 1984–2024, this study quantifie...

Automated Extraction of Long-Term Cyanobacteria Blooming Series from Landsat Imagery Using Deep Learning

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious threats to inland water ecology and environmental security. Despite advances, accurately capturing their long-term dynamics for effective governance remains challenging due to limited datasets and segmentation model acc...