Park Site Selection Using Remote Sensing, GIS & Google Earth Engine
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.
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
- Introduction –> 6 lectures • 1hr.
Requirements
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.