Satellite image time series in R

PyTorch实现高分遥感语义分割（地物分类）

A curated list of Earth Science's Artificial Intelligence (AI) tutorials, notebooks, software, datasets, courses, books, video lectures and papers. Contributions most welcome.

Application of deep learning on Satellite Imagery of Sentinel-2 satellite that move around the earth from June, 2015. This image patches can be trained and classified using transfer learning techniques.

Minerva project includes the minerva package that aids in the fitting and testing of neural network models. Includes pre and post-processing of land cover data. Designed for use with torchgeo datasets.

LINDER (Land use INDexER) is an open-source machine-learning based land use/land cover (LULC) classifier using Sentinel 2 satellite imagery

Code for the paper "Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification".

A deep learning (neural network) land cover classification project using satellite images (remote sensing).

ANN to SNN conversion on land cover and land use classification problem for increased energy efficiency.

Fundamentals of Remote Sensing and Earth Observation Course

Pipelines for BigEarthNet-Sen1 creation.

Classification of land based on land cover data.

A repository to showcase environmental projects implemented with Google Earth Engine platform, Javascript and machine learning algorithms.

The source code of the Sentinel-2 Land Cover Explorer has been moved to https://github.com/Esri/imagery-explorer-apps

ASI-304: Applying AI and Machine Learning to Satellite Data

My implementation of simple Land cover classification using Keras. This was part of one of my internships

Land Cover Classification System Web Service

Land cover classification in Tanzania using ensemble labels and high resolution Planet NICFI basemaps and Sentinel-1 time series.

Pixel-based and object-oriented land cover classifications of historical panchromatic Corona Spy satellite images

codes for RS paper: High-Rankness Regularized Semi-supervised Deep Metric Learning for Remote Sensing Imagery