aj1365/3DGAN-ViT
Here is the code developed for the paper "A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples" puplished in International Journal of Applied Earth Observation and Geoinformation.
This project helps environmental scientists and geographers accurately map and classify complex wetlands using satellite imagery, even when very few labeled examples are available. It takes in remote sensing data, like multispectral satellite images, and outputs detailed classification maps of different wetland types. Wetland ecologists, land-use planners, and conservation managers would use this to get precise wetland inventories.
No commits in the last 6 months.
Use this if you need to classify intricate wetland areas from satellite images but struggle with limited ground truth data for training.
Not ideal if you have abundant labeled training data or are working with non-remote sensing image classification tasks.
Stars
17
Forks
1
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Nov 22, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aj1365/3DGAN-ViT"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
WHU-Sigma/HyperSIGMA
The official repo for [TPAMI'25] "HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model"
manideep2510/eye-in-the-sky
Satellite Image Classification using semantic segmentation methods in deep learning
motokimura/spacenet_building_detection
Project to train/test convolutional neural networks to extract buildings from SpaceNet satellite...
rogerxujiang/dstl_unet
Dstl Satellite Imagery Feature Detection
Paulymorphous/skeyenet
Road and Building Segmentation in Satellite Imagery