Optimal Car Parking Site Selection Using Google Earth Engine
Optimal Car Parking Site Selection Using Google Earth Engine
Last updated 8/2025
Duration: 1h 6m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 547.22 MB
Genre: eLearning | Language: English
Last updated 8/2025
Duration: 1h 6m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 547.22 MB
Genre: eLearning | Language: English
Map Optimal Car Parking Locations Using GIS, Remote Sensing & Google Earth Engine
What you'll learn
- Understand the fundamentals of remote sensing and GIS concepts essential for urban planning and spatial analysis.
- Analyze slope, landcover, and road proximity data to identify suitable areas for car parking using real-world geospatial datasets.
- Build and implement a parking suitability model in Google Earth Engine, combining multiple spatial criteria for scoring and classification.
- Visualize and export suitability maps for urban decision-making using GEE tools and techniques.
Requirements
- No prior experience with Google Earth Engine is required — the course will guide you step-by-step.
Description
Parking is a major challenge in urban environments, and smart solutions are needed to optimize space and accessibility. This course offers a hands-on guide to identifyingoptimal car parking sitesusing modern geospatial tools. You'll learn how to applyRemote Sensing, GIS, and Google Earth Engine (GEE)to solve real-world urban planning problems through spatial data analysis.
The course begins with thefundamentals of remote sensing, giving you an understanding of how satellite imagery and elevation data are used to map the physical features of the Earth. You'll then move into the core concept ofsite suitability mapping, where you'll learn how to select and evaluate spatial factors such asland cover, slope, and road proximity.
You’ll be introduced toGeographic Information Systems (GIS)and how they help integrate and analyze multiple layers of spatial data. Following this, you’ll dive intoGoogle Earth Engine, a powerful cloud-based platform for analyzing geospatial data at scale. Using GEE, you’ll write code to combine real-world datasets and score urban land areas based on suitability for parking infrastructure.
In the final part of the course, you will implement a completeparking suitability model in GEEusing San Francisco as a case study. You'll classify land based on accessibility and terrain, visualize suitability zones on the map, and export your results for use in planning or presentations.
This course is ideal forurban planners, GIS students, smart city developers, and anyone interested in real-world geospatial applications. No prior coding experience is required—just a curiosity for spatial thinking and urban problem-solving.
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.
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