Satellite image time series in R
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Updated
Sep 16, 2024 - R
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.
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.
A deep learning (neural network) land cover classification project using satellite images (remote sensing).
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.
Fundamentals of Remote Sensing and Earth Observation Course
Classification of land based on land cover data.
Pipelines for BigEarthNet-Sen1 creation.
A repository to showcase environmental projects implemented with Google Earth Engine platform, Javascript and machine learning algorithms.
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.
The source code of the Sentinel-2 Land Cover Explorer has been moved to https://github.com/Esri/imagery-explorer-apps
ASI-304: Applying AI and Machine Learning to Satellite Data
My implementation of simple Land cover classification using Keras. This was part of one of my internships
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.
Crop type mapping solution for MAGO Project (NTUA)
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