HSG-AIML/SSLTransformerRS
Code repository for "Self-supervised Vision Transformers for Land-cover Segmentation and Classification", CVPR EarthVision Workshop 2022 - Best Student Paper Award
This project helps environmental scientists, urban planners, and agricultural specialists analyze satellite images to identify different land cover types like forests, water bodies, or urban areas. You input raw Sentinel-1 and/or Sentinel-2 satellite imagery, and it outputs detailed maps showing distinct land classifications or segmented regions. This is ideal for professionals who need to accurately map and monitor geographical features over time.
No commits in the last 6 months.
Use this if you need to perform highly accurate land-cover classification or segmentation on Sentinel-1 and Sentinel-2 satellite data, even with limited labeled training examples.
Not ideal if your primary need is real-time processing of other image types (like aerial drone footage or street-level photos) or if you prefer off-the-shelf software with no programming involved.
Stars
92
Forks
17
Language
Python
License
—
Category
Last pushed
Jan 23, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/HSG-AIML/SSLTransformerRS"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
deepinv/deepinv
DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning
fidler-lab/polyrnn-pp
Inference Code for Polygon-RNN++ (CVPR 2018)
mhamilton723/STEGO
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
yjxiong/tsn-pytorch
Temporal Segment Networks (TSN) in PyTorch
pyxu-org/pyxu
Modular and scalable computational imaging in Python with GPU/out-of-core computing.