PyTorch实现高分遥感语义分割（地物分类）

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".

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

Pipelines for BigEarthNet-Sen1 creation.

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 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"

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

Land Cover Classification System Database Model

A simple example of a machine learning library for land-cover classification

Harmonize classification raster files using Latent Dirichlet Allocation

Implementation for "Global heterogeneous graph convolutional network: from coarse to refined land cover and land use segmentation"

Classification of Sentinel-2 land cover multiband images through an ensamble of DNN

Project to practice and learn TorchGeo. Land cover classification using the Esri 2020 Land Cover dataset and Sentinel-2 imagery