Effortless data labeling with AI support from Segment Anything and other awesome models.

Python library for morphological cleaning of multiclass 2D numpy arrays (edge smoothing and island removal

Texture and Boundary Encoding-based Segmentation

Web App to run the core FTW inference and polygonization

HarvestStat-Africa: Open Access Harmonized Subnational Crop Statistics

A final sanity checklist to help your CS paper get accepted, not desk rejected.

Detecting and segmenting sand mining river vessels on the Vietnam Mekong Delta, using PlanetScope imagery and Mask R-CNN.

A collection of geospatial programs with commandline interface

An open-source tool for reading OvertureMaps data with multiprocessing and additional Quality-of-Life features

check code for common misspellings

Python library for cleaning and regularising building footprints in geospatial data, aligning edges to principal directions and simplifying polygons for more accurate analysis.

Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery. Foundation Model for Field Boundary Delineation.

Download and parse data from Garmin Connect or a Garmin watch, FitBit CSV, and MS Health CSV files into and analyze data in Sqlite serverless databases with Jupyter notebooks.

fAIr - AI Assisted Mapping Tool

A Content Editor for GitHub.

A toolkit that enables building damage assessments from remotely sensed imagery.

Speedtest by Ookla Global Fixed and Mobile Network Performance Map Tiles

Hydro -- A Foundation Model for Water in Satellite Imagery

Field Boundaries for Agriculture (fiboa) - a specification that describes important properties of field boundaries

ruptures: change point detection in Python

This repository contains code for experiments using the winning mode for xView2 "xView2: Assess Building Damage" challenge and a simple implementation of our strong baseline.

Segmentation models with pretrained backbones. PyTorch.

PyGMTSAR (Python InSAR): Powerful and Accessible Satellite Interferometry

Chesapeake RSC dataset introduced in "Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery"

A little Python script to collect LaTeX sources for upload to the arXiv.

Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning