HSG-AIML/Global-NO2-Estimation
Code repository for "Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing", IEEE TGSRS, 2022
This project helps environmental scientists and urban planners estimate ground-level nitrogen dioxide (NO2) pollution. It takes in satellite imagery (Sentinel-2 and Sentinel-5P) along with ground station data and OpenStreetMap land cover statistics to produce accurate NO2 concentration maps. The primary users are researchers and policymakers needing to monitor air quality and understand pollution patterns.
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Use this if you need to generate high-resolution estimates of ground-level NO2 pollution across broad geographic areas, especially where physical ground stations are sparse.
Not ideal if you only need NO2 estimates for a single, well-monitored location or prefer methods that don't involve satellite data processing.
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