hbinol's starred repositories
terratorch
a Python toolkit for fine-tuning Geospatial Foundation Models (GFMs).
python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
Smoothly-Blend-Image-Patches
Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye.
Awesome-AI-Data-GitHub-Repos
A collection of the most important Github repos for ML, AI & Data science practitioners
IEEE_TPAMI_SpectralGPT
Hong, D., Zhang, B., Li, X., Li, Y., Li, C., Yao, J., Yokoya, N., Li, H., Ghamisi, P., Jia, X., Plaza, A. and Gamba, P., Benediktsson, J., Chanussot, J. (2024). SpectralGPT: Spectral remote sensing foundation model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. DOI:10.1109/TPAMI.2024.3362475.
FAO_crop_boundary
Scalable workflow for crop boundary delineation using pre-trained deep learning model
RemoteCLIP
🛰️ Official repository of paper "RemoteCLIP: A Vision Language Foundation Model for Remote Sensing" (IEEE TGRS)
project-based-learning
Curated list of project-based tutorials
openai-python
The official Python library for the OpenAI API
awesome-vision-language-models-for-earth-observation
A curated list of awesome vision and language resources for earth observation.
segment-geospatial
A Python package for segmenting geospatial data with the Segment Anything Model (SAM)
awesome-segment-anything
Tracking and collecting papers/projects/others related to Segment Anything.
pytorch_forward_forward
Implementation of Hinton's forward-forward (FF) algorithm - an alternative to back-propagation
mlops-course
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Transformers-Recipe
🧠 A study guide to learn about Transformers
awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
Geospatial_Python_CourseV1
This is an collection of blog posts turned into a course format
Random-Forest-Land-Classification
Sentinel-1 and Sentinel-2 land classification over Finland using sklearn Random Forest and SNAP
SoilGrids250m
Global spatial predictions of soil properties and classes at 250 m resolution
sentinel2-cloud-detector
Sentinel Hub Cloud Detector for Sentinel-2 images in Python