Long list of geospatial tools and resources

Geo Spatial Data Analytics on Spark

GRASS GIS - free and open source Geographic Information System (GIS)

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

MicaSense RedEdge and Altum image processing tutorials

A collection of Python packages for geospatial analysis with binder-ready notebook examples

Over 60 recipes to work with topology, overlays and indoor routing

🌐 Interactive Workshop on GeoAnalysis using PySpark

A lightweight and efficient Python Morton encoder with support for geo-hashing

a package to create and plot Voronoi regions within geographic boundaries

Extract and Visualize location from any file

Pipeline for Observational Data Processing Analysis and Collaboration

OUTDATED, use https://github.com/OSGeo/grass/ ! Continuous integration for GRASS GIS

Actinia Core is an open source REST API for scalable, distributed, high performance processing of geographical data that uses mainly GRASS GIS for computational tasks.

Short course on interactive analysis of Big Earth Data with Jupyter Notebooks

A Los Angeles Times analysis of street racing fatalities in L.A. County

GRASS GIS Addons Repository (Manuals: https://grass.osgeo.org/grass7/manuals/addons/ | Linux-logs: https://grass.osgeo.org/addons/grass7/logs/ | Windows logs: http://wingrass.fsv.cvut.cz/grass78/x86_64/addons/latest/logs/)

AI bootcamp 2019 materials.

A Julia port of Turf.js http://turfjs.org/

A Los Angeles Times analysis of California buildings within fire hazard zones

This repo is used to organize and collaborate in sprints for code projects, hackathons, STAC and metadata specs development

Geospatial Land Availability for Energy Systems

Code and resources to serve as a starting point for data science projects with a particular focus on data visualization and statistical modeling.

Introduction to simple mapping in R: georeferencing lat/lon and choropleths. using simple features, leaflet, and tmap, ggmap

A tool for de-noising raster digital elevation models

Tutorials for geodesign with Blender

Global FloodPLAIN mapping using a geomorphic algorithm

Hands-on tutorial on OGC web services and big climate data analysis.

This project gives an overview of crime time analysis in New York City . We have created Python Jupyter notebooks for spatial analysis of different crime types in the city using Pandas, Numpy, Plotly and Leaflet packages. As a second part to this analysis, we worked on ARIMA model on R for predicting the crime counts across various localities in the city based on correlations of various demographics correlation in each locality.