Learn how to use population data, land cover, road networks, and satellite imagery in GEE to identify optimal bike site
In the age of smart cities and sustainable transportation, planning efficient and accessible bike infrastructure is crucial. This course offers a hands-on approach to Bike Site Suitability Mapping using Remote Sensing and Google Earth Engine (GEE). Designed for urban planners, GIS professionals, and students, this course guides you through the complete workflow of creating a suitability map for placing bike-sharing stations in urban areas.
What you’ll learn
- Learn site suitability concepts and how remote sensing aids smart urban planning for bike infrastructure..
- Work with real-world spatial data like population, roads, and parks using Google Earth Engine..
- Write GEE scripts to analyze spatial layers, compute distances, and build bike site suitability maps..
- Visualize, style, and export geospatial results as GeoTIFFs for use in GIS tools like QGIS or ArcGIS..
Course Content
- Introduction –> 5 lectures • 51min.
Requirements
In the age of smart cities and sustainable transportation, planning efficient and accessible bike infrastructure is crucial. This course offers a hands-on approach to Bike Site Suitability Mapping using Remote Sensing and Google Earth Engine (GEE). Designed for urban planners, GIS professionals, and students, this course guides you through the complete workflow of creating a suitability map for placing bike-sharing stations in urban areas.
You’ll begin by understanding the basic principles of site suitability analysis and how to define a study area. Then, you’ll explore how to integrate multiple data sources — including population density, road networks from TIGER datasets, and tree cover from ESA WorldCover — to evaluate site suitability. You’ll calculate distance-to-road and distance-to-park metrics using raster analysis and apply weighted overlays to generate a final suitability score.
Through a case study of New York City, you’ll gain practical skills in preprocessing, visualization, and map export. You’ll also learn how to normalize datasets, apply spatial logic using GEE’s JavaScript API, and produce meaningful geospatial outputs for real-world urban planning applications.
Whether you’re designing sustainable bike infrastructure or expanding your remote sensing skills, this course empowers you with modern, cloud-based tools and a clear analytical framework to support data-driven decisions in urban mobility planning.