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Bike Site Suitability Mapping with Remote Sensing and GEE
Rating: 5.0 out of 5(1 rating)
5 students

Bike Site Suitability Mapping with Remote Sensing and GEE

Learn how to use population data, land cover, road networks, and satellite imagery in GEE to identify optimal bike site
Created byEarth's AI
Last updated 8/2025
English

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

1 section • 5 lectures • 51m total length

  • Lecture 1: Fundamentals of Remote Sensing13:19

    This lecture introduces the core principles of remote sensing, including electromagnetic spectrum concepts, satellite sensors, spatial/temporal resolution, and types of remote sensing data. You’ll learn how remote sensing is used in environmental monitoring and urban planning, setting the foundation for later modules. This session is ideal for beginners and helps students understand how satellite data can be turned into meaningful spatial information for decision-making.

  • Lecture 2: Suitability Mapping with Remote Sensing8:03

    This lecture focuses on how to use remote sensing data for site suitability analysis. You’ll learn how to identify the best locations for specific land uses—such as urban development, green infrastructure, or public facilities—by combining geospatial datasets. The session will cover common suitability criteria like slope, land cover, proximity to infrastructure, and population density. By the end, you’ll understand how to build a multi-criteria suitability model using satellite imagery and remote sensing principles.

  • Lecture 3: Introduction to Google Earth Engine (GEE)9:25

    Google Earth Engine is a cloud-based geospatial analysis platform. This lecture covers the GEE interface, how to access and process satellite imagery, and use of the JavaScript API. You’ll learn how to load datasets, apply filters, visualize data on a map, and export results. GEE’s vast data catalog and processing power make it a perfect tool for remote sensing analysis—even without prior coding experience.

  • Getting Started with the Google Earth Engine Interface3:38

    This section introduces the Google Earth Engine platform, guiding learners through accessing the Code Editor, understanding its interface, and exploring key panels like the script editor, map viewer, inspector, and data catalog. It helps beginners get comfortable navigating GEE before writing any code or performing analysis.

  • Lecture 4: Implementing Bike Site Suitability Mapping in GEE16:46

    In this practical session, you’ll use GEE to create a bike site suitability model. By integrating layers like population density, road access, and green spaces, you’ll write a script that scores locations for bike-friendliness. You'll apply distance functions, raster math, masking, and layer weighting to produce a final suitability map. The lesson ends with visualizing and exporting your results for further analysis in GIS platforms.

Requirements

  • No prior experience with Google Earth Engine is required — the course will guide you step-by-step.

Description

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.

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