land-cover-classification

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.

[ISPRS P&RS 2025] TripleS: Mitigating multi-task learning conflicts for semantic change detection in high-resolution remote sensing imagery

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

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

Land Cover Prediction from Satellite Imagery Using Machine Learning Techniques

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.

Crop classification from Sentinel-2 imagery using Graph Convolutional Networks (PyTorch Geometric) | 99.9%% accuracy | Interactive Streamlit demo

Fundamentals of Remote Sensing and Earth Observation Course

Pixel-based and object-oriented land cover classifications of historical panchromatic Corona Spy satellite images

Pipelines for BigEarthNet-Sen1 creation.

Developed a and Cover Classification system using Satellite Image Processing with the help of Remote Sensing images. The system can classify between Forest land, Agricultural of Paddy fields nd Urban areas from a given Dataset. All the step by step procedure has been done and executed in the Jupyter notebook. The system can also clasify the Land…

A repository to showcase environmental projects implemented with Google Earth Engine platform, Javascript and machine learning algorithms.