Projects sharing features with LightNet

CosmiQ Works Geospatial Machine Learning Analysis Toolkit

Data Preparation for Satellite Machine Learning

Pythonic Framework for AI Inference on Geospatial Data

SNIPER / AutoFocus is an efficient multi-scale object detection training / inference algorithm

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

MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testi

Segment Geospatial is a Python toolkit for isolating geographic features in remote sensing imagery using the Segment Anything Model. It functions as a remote sensing image processor that converts map tiles into georeferenced formats to generate segmentation masks from satellite data. The system enables the extraction of geographic objects through automatic mask generation or manual prompts, such as text descriptions, bounding boxes, and interactive markers. It supports timeseries imagery segmentation to track or identify objects across sequences of images over different dates and provides a g

Earth observation processing framework for machine learning in Python

TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch.

Super Resolution for Satellite Imagery Applying super resolution strategies to sattelite imagery

Python Package for Airborne RGB machine learning

The Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) codebase combines some of the leading object detection algorithms into a unified framework designed to detect objects both large and small in overhead imagery. This work seeks to extend the YOLT modification of…