JunchuanYu
JunChuan Yu, male, Ph.D., mainly engaged in hyperspectral remote sensing, deep learning, and geological applications.
AGRSBeijing
Pinned Repositories
3DCAE_for_lithological_mapping
This is the offical implementation for our paper "3D autoencoder algorithm for lithological mapping using ZY-1 02D hyperspectral imagery: a case study of Liuyuan region"
Adobe_Illustrator_for_Scientific_Graphic_Design
A free tutorial on using Adobe Illustrator for scientific graphic design.
Building_Interactive_Web_APP_with_Gradio
This open-source Python tutorial will guide you in quickly building demos or web applications with Gradio
Deep-learning-teaching
Deep learning courses for postgraduates
HSI-Validation
MFGNet_Cloud_Detection_Remote_Sensing
This is the offical implementation for our paper "An effective cloud detection method for GaoFen-5 images via deep learning"
OUNet_for_multi-class_land_cover_classification
This is the official repository for paper “Research on the optimization of multi-class land cover classification using deep learning with multispectral imagery”
SAM-CFFNet
This is the official repository for paper “SAM-CDFFNet:SAM-based Cross-Dept Feature Fusion Net-work for Intelligent Identification of Landslides”
Segmentanything-RS
TransLandSeg
This is the official repository for paper “TransLandSeg :Transfer Learning Approach for Landslide Semantic Segmentation Based on Vision Foundation Model”
JunchuanYu's Repositories
JunchuanYu/Deep-learning-teaching
Deep learning courses for postgraduates
JunchuanYu/Segmentanything-RS
JunchuanYu/SAM-CFFNet
This is the official repository for paper “SAM-CDFFNet:SAM-based Cross-Dept Feature Fusion Net-work for Intelligent Identification of Landslides”
JunchuanYu/Work-with-Remote-Sensing-Data-in-Python
Learn How to Work With Landsat Multispectral Remote Sensing Data in Python, Data Science Course, ModelWhale Workshop
JunchuanYu/3DCAE_Hyper
Repo for the paper "An Attention-based 3D Convolutional Autoencoder for Few-shot Hyperspectral Unmixing" and Classification
JunchuanYu/Adobe_Illustrator_for_Scientific_Graphic_Design
A free tutorial on using Adobe Illustrator for scientific graphic design.
JunchuanYu/Building_Interactive_Web_APP_with_Gradio
This open-source Python tutorial will guide you in quickly building demos or web applications with Gradio
JunchuanYu/OUNet_for_multi-class_land_cover_classification
This is the official repository for paper “Research on the optimization of multi-class land cover classification using deep learning with multispectral imagery”
JunchuanYu/TransLandSeg
This is the official repository for paper “TransLandSeg :Transfer Learning Approach for Landslide Semantic Segmentation Based on Vision Foundation Model”
JunchuanYu/AI-Translator
JunchuanYu/anylabeling
Effortless AI-assisted data labeling with AI support from Segment Anything and YOLO!
JunchuanYu/AutoMJ
none
JunchuanYu/awesome-demos
links and status of cool gradio demos
JunchuanYu/blog-en
repo for my blog
JunchuanYu/chatgpt_academic
科研工作专用ChatGPT拓展,特别优化学术Paper润色体验,支持自定义快捷按钮,支持markdown表格显示,Tex公式双显示,代码显示功能完善,新增本地Python工程剖析功能/自我剖析功能
JunchuanYu/CLIP-SAM
Experiment on combining CLIP with SAM to do open-vocabulary image segmentation.
JunchuanYu/earthlib
Spectral endmembers and unmixing tools for satellite land cover mapping.
JunchuanYu/geospatial-wheels
Geospatial library wheels for Python on Windows
JunchuanYu/Hyperspectral-Image-Classification-Models
收录及复现的高光谱遥感图像分类模型
JunchuanYu/Interactive-Machine-Learning-Experiments
🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
JunchuanYu/JunchuanYu
Config files for my GitHub profile.
JunchuanYu/NOAA-quarto-simple-python
Simple Quarto website with Python code. GitHub Action renders when there are changes
JunchuanYu/PyHAT
JunchuanYu/quarto-rladies-theme
R-Ladies template for Quarto presentations with reveal.js
JunchuanYu/RS-APP-Store
JunchuanYu/Sydney
JunchuanYu/Sydney-AI-plus
JunchuanYu/tuning_playbook_zh_cn
一本系统地教你将深度学习模型的性能最大化的战术手册。
JunchuanYu/video-downloader-with-subtitle-generater
JunchuanYu/water_gee
This repository provides a way to extract water bodies using deep learning methods in GEE.