Park Site Suitability Mapping in Google Earth Engine (GEE)

About this course

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 datasetsApply MCDA principles in land suitability analysisBuild scalable, cloud-based spatial modelsSupport green infrastructure planning in urban environmentsThis 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.

Course objectives

• teach the fundamentals of geospatial data
• demonstrate the use of cloud-based spatial analysis
• equip students to make informed decisions about urban green spaces