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.
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
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.
Land Cover Prediction from Satellite Imagery Using Machine Learning Techniques
A repository to showcase environmental projects implemented with Google Earth Engine platform, Javascript and machine learning algorithms.
Crop type mapping solution for MAGO Project (NTUA)
Pixel-based and object-oriented land cover classifications of historical panchromatic Corona Spy satellite images
The source code of the Sentinel-2 Land Cover Explorer has been moved to https://github.com/Esri/imagery-explorer-apps
Land Cover Classification System Database Model
Add a description, image, and links to the land-cover-classification topic page so that developers can more easily learn about it.
To associate your repository with the land-cover-classification topic, visit your repo's landing page and select "manage topics."