There are 6 repositories under sentinel-2 topic.
A ready-to-use curated list of Spectral Indices for Remote Sensing applications.
Repository for Digital Earth Australia Jupyter Notebooks: tools and workflows for geospatial analysis with Open Data Cube and Xarray
Sentinel Hub Cloud Detector for Sentinel-2 images in Python
🌱 Deep Learning for Instance Segmentation of Agricultural Fields - Master thesis
The Clay Foundation Model (in development)
A collection of all earth related space Images in one script to set as your Desktop background.
On-Demand Earth System Data Cubes (ESDCs) in Python
Expandable Datasets for Earth Observation
a deep model that segments water on multispectral images
This repository contains the code used in the paper: A high-resolution canopy height model of the Earth. Here, we developed a model to estimate canopy top height anywhere on Earth. The model estimates canopy top height for every Sentinel-2 image pixel and was trained using sparse GEDI LIDAR data as a reference.
Tool to download Sentinel images from PEPS sentinel mirror site : https://peps.cnes.fr
A deep learning model for surface water mapping based on satellite optical image.
Sen4AgriNet: A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning
Search, composite, and download Google Earth Engine imagery.
An open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO)
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.
Satellite cloud removal with Deep Image Prior.
Urban change model designed to identify changes across 2 timestamps
Google Earth Engine application that finds Sentinel-2 images that are cloud-free in a particular area of interest.
Python scripts for creating time lapse videos and gifs from Sentinel-2 images
Download and process satellite imagery in JavaScript or TypeScript using Sentinel Hub services.
To process a Sentinel-2 time series with MAJA cloud detection and atmospheric correction processor