Ni‐Bin Chang
Papers
2
Total Citations
170
H-Index
2
About
Ni‑Bin Chang is a leading environmental data scientist whose research centers on big Earth data analytics, air quality monitoring, and multimodal data fusion. His most transformative contribution is the development of the Long-term Gap-free High-resolution Air Pollutant concentration (LGHAP) dataset, a breakthrough framework that integrates satellite, model, and ground-based observations to produce seamless, high-resolution air pollution maps. The original LGHAP paper (2022) has garnered 143 citations, while its global extension, LGHAP v2 (2024), already has 27 citations, reflecting the field’s rapid adoption of his work. By applying tensor-flow-based deep learning to fuse heterogeneous aerosol optical depth and PM2.5 data, Chang has enabled unprecedented spatiotemporal continuity in pollution records since 2000. His datasets are now essential tools for environmental management, epidemiological studies, and Earth system science, allowing researchers to analyze long-term exposure trends and policy impacts with confidence. Chang’s work exemplifies how advanced computational methods can bridge data gaps, turning fragmented observations into actionable environmental intelligence.
Research Focus
Key Achievements
Top Papers
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