Explainable AI maps what drives glacier flow in the Western Himalaya
An explainable GeoAI framework identifies ice thickness and elevation as the dominant predictors of glacier surface velocity variability on Chhota ...
An explainable GeoAI framework identifies ice thickness and elevation as the dominant predictors of glacier surface velocity variability on Chhota ...
Satellite records from 1984 to 2024 show built-up land in southern Punjab rising from about 1 percent to nearly 10 ...
Combining permanent field plots in Mizoram with Sentinel-2 satellite data, researchers estimated that low-altitude tropical forests hold about 456 tonnes ...
A four-decade Landsat analysis of Gauteng Province shows rising urban heat island intensity, more frequent heatwaves, and growing thermal stress ...
A new study combines machine learning and spatial explainable AI to predict surface urban heat island intensity across the Mumbai ...
A Sentinel-2 and Google Earth Engine study of Nepal's 2021 Melamchi debris flood found NDVI outperformed water indices for mapping ...
A 2013–2024 satellite analysis of 7,738 surface mining patches in the Yellow River Basin found that 56.78% of patches showed ...
Radar time-series features from Sentinel-1 lifted land-cover classification in India's cloud-prone Cauvery Delta to 72 percent accuracy, with simple logistic ...
A machine learning framework combining satellite radar, optical, elevation and rainfall data mapped flood inundation across Mumbai, with hydrologic predictors ...
A GIS and Google Earth Engine framework classified Zimbabwe's groundwater potential nationwide and tested it against nearly 10,000 borehole records.
© 2025 Scienmag - Science Magazine
© 2025 Scienmag - Science Magazine