PyTorch实现高分遥感语义分割(地物分类)
-
Updated
Nov 11, 2020 - Python
PyTorch实现高分遥感语义分割(地物分类)
Land Cover Classification System Database Model
Land Cover Classification System Web Service
LINDER (Land use INDexER) is an open-source machine-learning based land use/land cover (LULC) classifier using Sentinel 2 satellite imagery
codes for RS paper: High-Rankness Regularized Semi-supervised Deep Metric Learning for Remote Sensing Imagery
Detecting Land Cover Changes Between Satellite Image Time Series By Exploiting Self-Supervised Representation Learning Capabilities
Pipelines for BigEarthNet-Sen1 creation.
ANN to SNN conversion on land cover and land use classification problem for increased energy efficiency.
Harmonize classification raster files using Latent Dirichlet Allocation
Code for the paper "Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification".
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.
Land cover classification in Tanzania using ensemble labels and high resolution Planet NICFI basemaps and Sentinel-1 time series.
A simple example of a machine learning library for land-cover classification
Implementation for "Global heterogeneous graph convolutional network: from coarse to refined land cover and land use segmentation"
Code for our JSTARS paper "Semi-MCNN: A semisupervised multi-CNN ensemble learning method for urban land cover classification using submeter HRRS images"
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."