Bus Station Site Suitability Analysis using GEE

In this lecture, students will explore the essential principles of remote sensing, a technology that allows us to observe and measure the Earth’s surface without physical contact. The session will cover the types of remote sensors, including passive and active sensors, and explain how they capture data across different parts of the electromagnetic spectrum. Students will learn about spatial, spectral, temporal, and radiometric resolution, which influence the quality and usability of remote sensing data. The lecture will also discuss the major sources of satellite imagery, such as Landsat, Sentinel, and MODIS, and their applications in environmental monitoring, urban development, agriculture, and disaster management. By understanding remote sensing fundamentals, students will be prepared to interpret satellite images and apply this knowledge in GIS and spatial analysis projects.

This lecture introduces students to the methodology of site suitability mapping, a vital spatial analysis process used to determine the best locations for various developments or activities. Students will learn how to select and evaluate different spatial criteria that affect site suitability—such as distance to roads, terrain slope, land cover type, and population density. The lecture emphasizes multi-criteria decision analysis (MCDA) techniques, including assigning weights to each criterion based on their relative importance. Practical case studies will demonstrate how suitability mapping supports urban planning, environmental conservation, agriculture, and infrastructure siting. By the end of the lecture, students will understand how to integrate multiple data layers into a composite suitability index to guide informed decision-making.

In this foundational lecture, students will gain a comprehensive overview of Geographic Information Systems (GIS) technology. The session covers the basics of spatial data models, including vector (points, lines, polygons) and raster (grid cells) formats. Students will learn about coordinate reference systems, map projections, and how GIS manages and visualizes spatial data to reveal patterns and relationships. Key GIS operations such as buffering, overlay, spatial querying, and thematic mapping will be introduced. The lecture also highlights common GIS software and platforms used for analysis and decision support across fields like transportation planning, public health, natural resource management, and disaster response. Students will leave with a solid understanding of how GIS integrates diverse datasets to solve real-world spatial problems.

This lecture introduces students to Google Earth Engine (GEE), a powerful cloud-based platform for processing and analyzing large-scale geospatial data. Students will learn about GEE’s capabilities to access petabytes of satellite imagery and global datasets, and how it simplifies complex geospatial computations using JavaScript and Python APIs. The lecture covers the GEE interface, data catalog, and basic scripting workflows for image visualization, filtering, and exporting results. Emphasis will be placed on the benefits of using GEE for rapid environmental monitoring, land use change detection, and disaster assessment without the need for high-performance local computing resources. By the end, students will be comfortable navigating GEE and performing simple remote sensing tasks within the platform.

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.

Building on previous lectures, this session focuses on applying site suitability mapping within Google Earth Engine for locating optimal bus station sites. Students will learn how to combine multiple spatial datasets such as population density, road networks, slope, and land cover to create a composite suitability map using GEE’s JavaScript API. The lecture will walk through coding practices for data filtering, normalization, raster calculations, and visualization. Students will also explore methods to apply buffers and masking for refining the suitability analysis. Practical examples will illustrate exporting results for further use. This hands-on lecture empowers students to translate theoretical knowledge into real-world spatial decision support using GEE’s scalable platform.