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Towards Explainable AI4EO: an explainable DL approach for crop type mapping using SITS
AI4EO Hyperview Machine Learning Challenge
Predicting Groundwater depletion in the Rhine River region using Satellite Gravity (GRACE) & Weather Data. Features: LSTM/GRU comparison, Explainable AI (Permutation Feature Importance (PFI)) for climate analysis, and Uncertainty Quantification for ethical AI.
This project uses SENTINEL-1/2 machine learning for flood extent and depth estimation, with cross-scene generalisation and explainable AI, to detect extent and depth of flooding in South Yorkshire (Fishlake and Bentley/Toll Bar), during flood events of 2019 and 2021.
A short guide to 2 methods of unsupervised image classification (K-Means and Gaussian Mixture Models) based on Week 4 content of GEOL0069 (AI4EO) at University College London (UCL) 25/26.
Single Image Super Resolution models (SRCNN, FSRCNN, VDSR, SRResNet, EDSR, RCAN, Real ESRGAN, HAT) adapted for remote sensing imagery using NWPU-RESISC45 dataset | PyTorch
This repository contains resources and examples on Unsupervised Learning Methods. It aims to introduce and apply unsupervised learning techniques to Earth observation data.
A Deep Learning Approach to Image Resolution Enhancement: Super-Resolution of Multispectral MODIS Imagery Utilizing Sentinel-2 Products
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