mariagonzc/GANFilling
Code from the paper Generative Networks for Spatio-Temporal Gap Filling of Sentinel-2 Reflectances
This project helps environmental scientists, land-use managers, and agricultural specialists get a complete picture of Earth's surface from satellite imagery, even when clouds or other issues obscure the view. It takes incomplete Sentinel-2 satellite reflectance data (visible and near-infrared bands) and fills in the missing information, producing clear, gap-free images ready for analysis. This allows for more reliable monitoring of natural ecosystems and vegetation health over time.
No commits in the last 6 months.
Use this if you need to analyze Sentinel-2 satellite data but frequently encounter missing information due to clouds or sensor issues, and you require clean, continuous time-series imagery for accurate environmental monitoring or forecasting.
Not ideal if you are working with satellite data from sensors other than Sentinel-2 or if your primary need is not filling spatio-temporal gaps in reflectance data.
Stars
13
Forks
3
Language
Python
License
BSD-2-Clause
Category
Last pushed
Jan 23, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mariagonzc/GANFilling"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
TencentARC/GFPGAN
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
bamos/dcgan-completion.tensorflow
Image Completion with Deep Learning in TensorFlow
MathiasGruber/PConv-Keras
Unofficial implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions"....
leehomyc/Faster-High-Res-Neural-Inpainting
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
yu4u/cutout-random-erasing
Cutout / Random Erasing implementation, especially for ImageDataGenerator in Keras