PyTorch实现高分遥感语义分割（地物分类）

Satellite image time series in R

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.

Contains implementations of data-centric approaches for improving semantic segmentation on satellite imagery.

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

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.

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

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

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

[ISPRS P&RS 2025] TripleS: Mitigating multi-task learning conflicts for semantic change detection in high-resolution remote sensing imagery

Land Cover Prediction from Satellite Imagery Using Machine Learning Techniques

Study about Urban Green Spaces in Athens GR, using the Google Earth Engine platform, along with Landsat 8 and 9 imagery and Random Forest supervised machine learning algorithms.

Classification of land based on land cover data.

Fundamentals of Remote Sensing and Earth Observation Course

Pipelines for BigEarthNet-Sen1 creation.

Developed a and Cover Classification system using Satellite Image Processing with the help of Remote Sensing images. The system can classify between Forest land, Agricultural of Paddy fields nd Urban areas from a given Dataset. All the step by step procedure has been done and executed in the Jupyter notebook. The system can also clasify the Land cover into various other categories as well as shown in the Sample predicted results.

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

Crop type mapping solution for MAGO Project (NTUA)

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

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

Crop classification from Sentinel-2 imagery using Graph Convolutional Networks (PyTorch Geometric) | 99.9%% accuracy | Interactive Streamlit demo

Detecting Land Cover Changes Between Satellite Image Time Series By Exploiting Self-Supervised Representation Learning Capabilities

Land Cover Classification System Web Service

Land Cover Classification System Database Model

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

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

Code for our JSTARS paper "Semi-MCNN: A semisupervised multi-CNN ensemble learning method for urban land cover classification using submeter HRRS images"

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

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