Udemy – Bike Site Suitability Mapping with Remote Sensing and GEE

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.

Who this course is for:

• Students, researchers and professionals in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world scenarios.

DOWNLOAD LINK: Bike Site Suitability Mapping with Remote Sensing and GEE

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