School Site Selection Analysis using Google Earth Engine

Optimizing school site selection using advanced spatial analysis and Google Earth Engine for informed decision-making.

Created byEarth's AI

Last updated 8/2025

English

What you'll learn

Understand how to define an Area of Interest (AOI) and integrate spatial datasets using Google Earth Engine for suitability analysis.
Learn to preprocess and normalize key geographic layers such as roads, slope, land cover, and population density in GEE.
Develop skills to combine multiple criteria using weighted overlay methods for site suitability mapping in a real-world context.
Gain hands-on experience exporting geospatial results and visualizing suitability maps with customized symbology and parameters in GEE.

Course content

1 section • 6 lectures • 1h 3m total length

Lecture 1: Fundamentals of Remote Sensing13:19
This lecture introduces the core principles of remote sensing technology, including the types of sensors, platforms, and data acquisition methods. Students will explore electromagnetic spectrum basics, image resolution types, and data formats. The lecture emphasizes the role of remote sensing in environmental monitoring, urban planning, and disaster management. Hands-on examples demonstrate how satellite and aerial imagery provide valuable spatial information. By the end, learners will understand how remote sensing data is captured and its potential applications in geospatial analysis.
Lecture 2: Site Suitability Mapping8:03
Students learn how to perform site suitability analysis by integrating multiple spatial datasets. The lecture covers criteria selection, data preprocessing, normalization, and the application of weighted overlay techniques. Through practical examples, learners will understand how to evaluate land characteristics, accessibility, and environmental constraints to determine optimal locations for various uses. This session highlights how geographic and socio-economic factors influence suitability and introduces tools for visualizing and interpreting suitability results.
Lecture 3: GIS Introduction7:39
This lecture provides a comprehensive introduction to Geographic Information Systems (GIS). Students will learn GIS concepts such as spatial data types, coordinate systems, and database management. The lecture also covers common GIS operations like buffering, clipping, and spatial joins. Emphasis is placed on the use of GIS for spatial analysis, mapping, and decision support. Practical demonstrations introduce popular GIS software, preparing learners to handle real-world geospatial problems effectively.
Lecture 4: Introduction to Google Earth Engine (GEE)9:25
In this lecture, students are introduced to Google Earth Engine, a cloud-based platform for large-scale geospatial data analysis. The session covers the GEE interface, scripting environment, and key datasets available. Learners will gain familiarity with JavaScript API basics and how to access satellite imagery and environmental data. The lecture also demonstrates how GEE enables rapid processing of vast spatial datasets for environmental monitoring, urban planning, and resource management.
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 — Suitability Mapping for School Site Selection21:17
This practical lecture focuses on applying Google Earth Engine to perform suitability analysis for school site selection. Students will learn how to integrate multiple spatial criteria such as roads, population density, slope, and green spaces. The session guides learners through data normalization, weighted overlay techniques, and visualization of results within GEE. By the end, participants will have hands-on experience creating suitability maps and exporting results, equipping them with skills to conduct real-world geospatial decision-making projects using cloud computing.

Requirements

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

Description

Selecting the best locations for schools is crucial for community development, accessibility, and environmental sustainability. This course introduces the foundational tools and methodologies needed to conduct site suitability analysis specifically for school site selection using remote sensing, GIS, and Google Earth Engine (GEE). Beginning with the fundamentals of remote sensing, students will understand how satellite imagery and spatial data can reveal critical environmental and social information.

The course then delves into Geographic Information Systems (GIS) basics and their powerful role in mapping and spatial decision-making. Learners will explore key factors influencing school site suitability such as road proximity for easy access, terrain slope for safety and construction feasibility, population density to serve maximum students, and land cover to preserve green areas and avoid unsuitable zones.

Google Earth Engine is introduced as a powerful cloud-based platform enabling the processing of large geospatial datasets efficiently. Students will learn to implement site suitability models by integrating and normalizing diverse datasets, applying weighted overlays, and generating maps that visually communicate suitability scores.

Practical exercises culminate in applying these concepts to real-world scenarios, empowering learners to conduct school site selection analyses. By the course’s end, participants will be equipped with the technical skills and spatial reasoning necessary to support urban planners, educators, and policymakers in making informed decisions for sustainable school infrastructure development.

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.