PyTorch实现高分遥感语义分割(地物分类)
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Updated
Nov 11, 2020 - Python
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".
LINDER (Land use INDexER) is an open-source machine-learning based land use/land cover (LULC) classifier using Sentinel 2 satellite imagery
ANN to SNN conversion on land cover and land use classification problem for increased energy efficiency.
Land Cover Prediction from Satellite Imagery Using Machine Learning Techniques
[ISPRS P&RS 2025] TripleS: Mitigating multi-task learning conflicts for semantic change detection in high-resolution remote sensing 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.
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.
Detecting Land Cover Changes Between Satellite Image Time Series By Exploiting Self-Supervised Representation Learning Capabilities
Land Cover Classification System Web Service
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