Projects sharing features with Pixel Decoder

Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

CosmiQ Works Geospatial Machine Learning Analysis Toolkit

Data Preparation for Satellite Machine Learning

Pythonic Framework for AI Inference on Geospatial Data

implementation for paper "ShelfNet for fast semantic segmentation"

Deep Learning for Land-cover Classification in Hyperspectral Images.

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

Deep learning for medical image segmentation in R: 2D and 3D, DICOM and NIfTI, one specification.

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

SegNet-like network implemented in TensorFlow to use for segmenting aerial images

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

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

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.

TernausNetV2: Fully Convolutional Network for Instance Segmentation

tsl: a PyTorch library for processing spatiotemporal data.

A convolutional neural network that identifies water in satellite images.

SamGIS can do machine learning-based (Segment Anything by Meta - Facebook) image segmentation tasks applied to GIS and geo data

A Libtorch implementation of the YOLO v3 object detection algorithm

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

Python Package for Airborne RGB machine learning

LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)