EV Charging Station Site Suitability Analysis Using GEE

This lecture covers the foundational concepts of remote sensing, focusing on how satellites and aerial platforms collect data about the Earth’s surface. Students will learn about different types of sensors, including optical, radar, and thermal, and understand how electromagnetic waves interact with various land features. Key topics include image acquisition, resolution types (spatial, spectral, temporal), and preprocessing steps like atmospheric correction. The lecture also highlights remote sensing applications in environmental monitoring, urban development, agriculture, and disaster management. By the end, learners will have a solid grasp of how raw satellite data is transformed into actionable geographic information.

This lecture introduces the concept of site suitability analysis using Geographic Information Systems (GIS) and remote sensing data. Students will explore the principles of multi-criteria decision analysis (MCDA), learning how to weigh and combine factors such as population density, proximity to roads, land use, and terrain slope to identify optimal locations for infrastructure or development projects. The lecture will include case studies and hands-on examples demonstrating suitability mapping for applications such as urban planning, environmental conservation, and facility siting. By the end, learners will understand how to design, implement, and interpret suitability maps effectively.

This lecture provides a comprehensive overview of GIS technology, covering the basics of spatial data types, data models (vector and raster), coordinate systems, and spatial analysis techniques. Students will learn how GIS integrates various data layers to visualize, analyze, and solve real-world problems related to geography and environment. Key software tools and platforms will be introduced, alongside practical examples of GIS applications in natural resource management, transportation planning, and disaster response. The lecture emphasizes hands-on skills for working with GIS software and preparing spatial data for analysis.

This lecture introduces Google Earth Engine, a powerful cloud-based platform for large-scale geospatial data analysis. Students will learn about the GEE environment, including how to access its extensive satellite imagery and geospatial datasets, perform complex image processing, and execute spatial analyses using JavaScript or Python APIs. The lecture covers key concepts such as collections, image filtering, visualization, and exporting results. Through practical examples, learners will gain skills in harnessing GEE’s computing power to analyze environmental changes, land use patterns, and urban growth, preparing them to leverage GEE for real-world geospatial challenges.

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.

This lecture focuses on applying Google Earth Engine to perform site suitability analysis specifically for electric vehicle (EV) charging stations. Students will learn how to integrate multiple spatial datasets—such as road networks, population density, terrain slope, and urban land cover—and apply weighting schemes to generate a suitability map. The lecture includes step-by-step coding demonstrations for data preprocessing, normalization, combination, and visualization within the GEE environment. By the end, learners will be able to implement end-to-end suitability analysis workflows in GEE, gaining hands-on experience relevant to urban planning, smart infrastructure, and sustainable development.