8 papers
From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion
Zhenyu Yu, Mohd Yamani Idna Idris, Hua Wang +3
Quantitative remote sensing inversion aims to estimate continuous surface variables-such as biomass, vegetation indices, and evapotranspiration-from satellite observations, support…
SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
Zhenyu Yu, Mohd. Yamani Idna Idris, Pei Wang +3
We propose SatelliteFormula, a novel symbolic regression framework that derives physically interpretable expressions directly from multi-spectral remote sensing imagery. Unlike tra…
DC4CR: When Cloud Removal Meets Diffusion Control in Remote Sensing
Zhenyu Yu, Mohd Yamani Idna Idris, Pei Wang
Cloud occlusion significantly hinders remote sensing applications by obstructing surface information and complicating analysis. To address this, we propose DC4CR (Diffusion Control…
SatelliteCalculator: A Multi-Task Vision Foundation Model for Quantitative Remote Sensing Inversion
Zhenyu Yu, Mohd. Yamani Idna Idris, Pei Wang
Quantitative remote sensing inversion plays a critical role in environmental monitoring, enabling the estimation of key ecological variables such as vegetation indices, canopy stru…
A Diffusion-Based Framework for Terrain-Aware Remote Sensing Image Reconstruction
Zhenyu Yu, Mohd Yamani Inda Idris, Pei Wang
Remote sensing imagery is essential for environmental monitoring, agricultural management, and disaster response. However, data loss due to cloud cover, sensor failures, or incompl…
Rainy: Unlocking Satellite Calibration for Deep Learning in Precipitation
Zhenyu Yu, Hanqing Chen, Mohd Yamani Idna Idris +1
Precipitation plays a critical role in the Earth's hydrological cycle, directly affecting ecosystems, agriculture, and water resource management. Accurate precipitation estimation…