#Satellite-imagery
Showing 53 of 53 repositories tagged #satellite-imagery, ranked by stars
Techniques for deep learning with satellite & aerial imagery
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
🛰️ List of satellite image training datasets with annotations for computer vision and deep learning
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
Satellite imagery for dummies.
A curated list of resources focused on Machine Learning in Geospatial Data Science.
interactive notebooks from Planet Engineering
[CVPR26] TESSERA is a foundation model that can process time-series satellite imagery for applications such as land classification and canopy height prediction. Developed at the University of Cambridge, it enables efficient extraction of temporal patterns from Earth observation
[IGARSS'22]: A Transformer-Based Siamese Network for Change Detection
This repository provides a comprehensive list of radar and optical satellite datasets curated for ship detection, classification, semantic segmentation, and instance segmentation tasks. These datasets are ideal for applications in computer vision, machine learning, remote sensing, and maritime analysis.
TernausNetV2: Fully Convolutional Network for Instance Segmentation
Data Preparation for Satellite Machine Learning
A python package that extends Google Earth Engine.
Open solution to the Mapping Challenge :earth_americas:
dynamic tile server for visualizing rasters in Jupyter with ipyleaflet or folium
A Curated List of Python Resources for Earth Sciences
Global Forest Watch: An online, global, near-real time forest monitoring tool
Create a digital-twin style traffic visualization using only mp4 CCTV footage and its Google Maps location.
Pytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agency’s Kelvin competition. This is a ServiceNow Research project that was started at Element AI.
Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.
Awesome Spectral Indices in Python.
[ESSD 2025 & IEEE DFC 2025 & CVPRW 2026] Bright: A globally distributed multimodal VHR dataset for all-weather disaster response
The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.
🛣 Building an end-to-end Promptable Semantic Segmentation (Computer Vision) project from training to inferencing a model on LandCover.ai data (Satellite Imagery).
Satellite-powered agricultural and land analytics platform that combines Sentinel-2 satellite imagery, Google Earth Engine processing, real-time weather data, soil science databases, and AI-driven crop planning into a single web dashboard.
Application of deep learning for earth observation.
Open-source ML platform for detecting deforestation, ice melt, and flooding from Sentinel-2 / Landsat imagery.
Python-based extractor of vegetation metrics from satellite-based vegetation time-series imagery.
Open Satellite Image Cloud Detection Resources (Datasets and Tools)
Official implementation of our ICRA'22 paper: SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving
An introduction to script-based satellite image processing using Python
An open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO)
GeoTile is the python library for tiling the geographic raster data (eg. Tiff etc)
Satellite image processing pipeline to classify land-cover and land-use
Multi-Class Semantic Segmentation on Dubai's Satellite Images.
[IGARSS 2024 ORAL] Official implementation of "Composed Image Retrieval for Remote Sensing".
Detecting ships from the satellite images using the YOLO algorithm
[RA-L 2024] Official repository for "STHN: Deep Homography Estimation for UAV Thermal Geo-localization with Satellite Imagery"
Using U-Net Model to Detect Wildfire from Satellite Imagery
A tool for running deep learning algorithms for semantic segmentation with satellite imagery
Label-Pixels is the tool for semantic segmentation of remote sensing images using Fully Convolutional Networks. Initially, it is designed for extracting the road network from remote sensing imagery and now, it can be used to extract different features from remote sensing imagery.
A ninja python package that unifies the Google Earth Engine ecosystem.
Dockerized Repo for "3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D" based on Applied Energy publication.
PyTorch implementation of SPNv2
Search Satellite Imagery Archive on aggregators like SkyFi, UP42, Skywatch, EOS via their respective APIs
A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python
Train your own neural networks to segment orthophotos.
Pre-trained VGG-Net Model for image classification using tensorflow
A convolutional neural network that estimates the wind speed of a hurricane based on its satellite image
Official repo for 'Generating Physically-Consistent Satellite Imagery for Climate Visualizations'
Jupyterlab extension for EODAG search
WAV to MMV converter. You can then use the MMV file in input of MSSTV to decode Slow Scan Television (SSTV) sound signals.