Techniques for deep learning with satellite & aerial imagery

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

Interactive interface for browsing global, full-resolution satellite imagery

Global shoreline mapping tool from satellite imagery

SAS.Planet is a free, open-source Geographic Information System (GIS) software designed for viewing, downloading, and managing high-resolution satellite imagery and conventional maps from various online sources.

Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery

AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.

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

Satellite Image Classification using semantic segmentation methods in deep learning

ROS package containing drivers for NMEA devices that can output satellite navigation data (e.g. GPS or GLONASS).