Popular GitHub Repositories for Satellite Imagery

:satellite: Simple and ready-to-use tutorials for TensorFlow

Techniques for deep learning with satellite & aerial imagery

:satellite: All You Need to Know About Deep Learning - A kick-starter

?? List of satellite image training datasets with annotations for computer vision and deep learning

TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data

An open source library and framework for deep learning on satellite and aerial imagery.

Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds

A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS. Also, including other vision transformers and CNNs for satellite, aerial image and UAV image segmentation.

A curated list of resources focused on Machine Learning in Geospatial Data Science.

real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)

TernausNetV2: Fully Convolutional Network for Instance Segmentation

Pytorch implementation of ResUnet and ResUnet ++

[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.

Data Preparation for Satellite Machine Learning

Official code of the paper "ReDet: A Rotation-Equivariant Detector for Aerial Object Detection" (CVPR 2021)

Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

Helper package with multiple U-Net implementations in Keras as well as useful utility tools helpful when working with image semantic segmentation tasks. This library and underlying tools come from multiple projects I performed working on semantic segmentation tasks

Satellite Imagery Feature Detection with SpaceNet dataset using deep UNet