Cloud Removal for High-resolution Remote Sensing Imagery based on Generative Adversarial Networks.
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Updated
Jul 14, 2023 - Python
Cloud Removal for High-resolution Remote Sensing Imagery based on Generative Adversarial Networks.
DSen2-CR: A network for removing clouds from Sentinel-2 images. This repo contains the model code, written in Python/Keras, as well as links to pre-trained checkpoints and the SEN12MS-CR dataset.
Code for paper:Memory Augment is All Your Need for image restoration. TCE 2025
[CVPR 2025] The official implementation of EMRDM, which is a novel diffusion model for cloud removal of remote sensing images.
CloudGAN: Detecting and removing clouds from satellite RGB-images
[Pattern Recognit. Lett.] This is the official code of the paper "Cloud removal using SAR and optical images via attention mechanism-based GAN"
Seamless Flood Mapping Using Harmonized Landsat and Sentinel-2 Data
Code from the paper Generative Networks for Spatio-Temporal Gap Filling of Sentinel-2 Reflectances
Developed an AI System based on Generative Adversarial Networks (GANs) to predict and remove the Clouds and Fog from an Image captured from Satellite. Gets input of a Satellite Image with Clouds and outputs a predicted landscape without clouds. Demo prototype for my internship at ISRO Hyderabad campus National Remote Sensing Centre. Actual proje…
Multi-modal remote sensing image restoration and fusion foundation model with language prompting.
A small python package for fast and scalable detection, removal and/or filling of anomalous pixels in satellite images.
Generative-AI cloud removal & reconstruction for ISRO LISS-IV imagery (BAH 2026 · PS2): a unified diffusion / GAN / transformer / SAR–optical-fusion framework with synthetic-cloud training, multi-satellite cross-verification, spectral-fidelity (SAM/ERGAS) evaluation, and an O(1) COG/STAC/FastAPI serving stack.
generative AI framework for cloud removal and reconstruction in LISS-IV satellite imagery using PyTorch.
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