1huang's repositories
1huang
Config files for my GitHub profile.
co2-data
Data on CO2 and greenhouse gas emissions by Our World in Data
blockchain-carbon-accounting
This project implements blockchain applications for climate action and accounting, including emissions calculations, carbon trading, and validation of climate claims. It is part of the Linux Foundation's Hyperledger Climate Action and Accounting SIG.
Carbonalyser
The add-on "Carbonalyser" allows to visualize the electricity consumption and greenhouse gases (GHG) emissions that your Internet browsing leads to.
copilot-docs
Documentation for GitHub Copilot
GDN
Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021)
carbon-footprint
Calculate your carbon footprint 🏭👣 from food, transport, purchases, fashion, electricity and digital activities like streaming, NFT or blockchain.
pytorch-tutorial
PyTorch深度学习快速入门教程（绝对通俗易懂！）
carbontracker
Track and predict the energy consumption and carbon footprint of training deep learning models.
Compressive-Sensing-and-Deep-Learning-Framework
We propose Compressive Sensing and Deep Learning framework (CS-DL) for multiple satellite sensor based data fusion. It’s aims to improve spatial and temporal resolution for long term analysis. Compressive Sensing is used as an initial guess to combine data from multiple sources. Deep Learning model, using Long Short Term Memory Neural Network (LSTM/RNN) refines and further improves the resulting data fusion output from CS. Our CS-DL framework has been tested to fuse CO2 from the NASA Orbiting Carbon Observatory-2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT). It achieves lower errors and high correlation compared with the original data. This work demonstrates the use of CS-DL for fusing CO2 from NASA Orbiting Carbon Observatory-3 and GOSAT2 at higher resolution.
bloom-contrib
Making carbon footprint data available to everyone.
IEEE_TGRS_MDL-RS
Danfeng Hong, Lianru Gao, Naoto Yokoya, Jing Yao, Jocelyn Chanussot, Qian Du, Bing Zhang. More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification, IEEE TGRS, 2020.
Data_Assimilation
MSc Research project (6 months). Data Assimilation using Deep Learning (AEs). Imperial College Machine Learning MSc 2018-19
IEEE_TGRS_GCN
Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2020.
PyMICAPS
气象数据可视化，用matplotlib和basemap绘制micaps数据
GELM-AE-AL
The following demo comes for two papers "Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification" and "Multi-layer Extreme Learning Machine-based Autoencoder for Hyperspectral Image Classification".
DCGAN-tensorflow
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
dimensionality_reduction_alo_codes
PCA、LDA、MDS、LLE、TSNE等降维算法的python实现
Hyperspectral_Classification_RMG
Matlab code for our JARS18 paper "Spectral and spatial classification of hyperspectral image based on random multi-graphs"
Hyperspectral-KNN-Classification
This work contains KNN classification of Hyperspectral Satellite Images using the given groundtruth and finding success rate of the method. You can download the hypersectral images using the link below :http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes&redirect=no#Pavia_University_scene
SuperPCA
Dimensionality reduction and classification of hyperspectral image based on SuperPCA (IEEE TGRS, 2018)
ML-DL_book
机器学习、深度学习一些个人认为不错的书籍。
SF-CNN
Code for the paper of Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification, which is accepted by IEEE Transactions on Geoscience and Remote Sensing
Demo_spectral_spatial_hyperspectral_classification
This is a code set for spectral-spatial hyperpsectral classifcation, including the EMAP, Gabor, LORSAL, LibSVM, MRF, and LBP methods.
generative-models
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
BASS-Net
Band-Adaptive Spectral-Spatial Feature Learning Deep Neural Network for Hyperspectral Image Classification
Hyperspectral
Deep Learning for Land-cover Classification in Hyperspectral Images.
DEEP-TENSOR-FACTORIZATION-FOR-HYPERSPECTRAL-IMAGE-CLASSIFICATION
Hyperspectral image classification by exploring deep tensor facorization, published in IGARSS 2018.
JSaCR
Matlab code for hyperspectral image classification based on JSaCR (IEEE GRSL, 2017)