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Park Site Suitability Mapping in Google Earth Engine (GEE)
Rating: 4.2 out of 5(2 ratings)
10 students

Park Site Suitability Mapping in Google Earth Engine (GEE)

Park Site Selection Using Remote Sensing, GIS & Google Earth Engine
Created byEarth's AI
Last updated 8/2025
English

What you'll learn

  • Learn how to use satellite data and GIS tools to identify suitable land for urban parks using environmental and demographic criteria.
  • Gain hands-on experience with Google Earth Engine for processing and analyzing spatial datasets at scale.
  • Understand how to combine raster layers like roads, slope, land cover, and population into a weighted suitability index.
  • Develop spatial decision-making skills using remote sensing and GEE to support real-world urban planning and green space development.

Course content

1 section • 6 lectures • 1h 0m total length

  • Lecture 1: Fundamentals of Remote Sensing13:19

    Explore the core principles of remote sensing, including electromagnetic spectrum interaction, spatial resolution, and sensor types. Understand how satellite imagery captures land surface features critical for analyzing potential park locations. This lecture lays the groundwork for understanding the data used throughout the course.

  • Lecture 2: Site Suitability Mapping8:03

    Learn how to evaluate and combine multiple spatial layers—such as proximity to roads, population density, slope, and land cover—to identify suitable park locations. This lecture introduces multi-criteria decision-making (MCDM) techniques for urban planning using geospatial analysis.

  • Lecture 3: GIS Introduction7:39

    Get introduced to GIS concepts such as vector and raster data, spatial operations, and cartographic principles. Learn how GIS enables you to visualize and manipulate geospatial data to inform park site decisions and prepare datasets for further analysis in GEE.

  • Lecture 4: Introduction to GEE9:25

    Dive into the powerful cloud-based GEE platform. Learn the basics of JavaScript coding in the GEE Code Editor, how to access global datasets, and build simple visualizations. This lecture bridges your GIS knowledge to scalable cloud processing.

  • 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 5: Implementation in GEE — Park Suitability Mapping18:19

    Apply your knowledge by building a complete park site suitability model in GEE. You'll integrate population, slope, roads, land cover, and green space exclusion zones into a weighted index, visualize outputs, and export your results for decision-making.

Requirements

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

Description

Urban parks provide essential ecological, social, and health benefits. However, placing new parks in the right locations requires informed spatial planning. This course teaches students how to use remote sensing, GIS, and the Google Earth Engine (GEE) platform to perform park site suitability analysis based on a variety of environmental and urban criteria.

Students will begin by learning the fundamentals of geospatial data and tools, including remote sensing imagery and digital elevation models. Core lectures cover thematic data such as population density, proximity to roads, urban land cover, terrain slope, and existing green spaces. Using these inputs, students will apply normalization, weighting, and multi-criteria analysis techniques to determine the most suitable areas for new parks.

A major strength of this course is its use of Google Earth Engine, a powerful cloud-based platform that eliminates the need for large downloads or complex desktop software. Students will gain hands-on experience in scripting with JavaScript to preprocess imagery, analyze spatial relationships, and generate interactive suitability maps. Final outputs can be exported as GeoTIFFs or shared for stakeholder decision-making.

By the end of the course, learners will be able to:

  • Integrate diverse geospatial datasets

  • Apply MCDA principles in land suitability analysis

  • Build scalable, cloud-based spatial models

  • Support green infrastructure planning in urban environments

This course is ideal for GIS analysts, urban planners, environmental consultants, students, and anyone interested in sustainable development. No prior coding experience is needed—only curiosity and a passion for building smarter, greener cities.

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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