
Free Download Mall Site Suitability Mapping Using Google Earth Engine
Published 8/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 6m | Size: 555 MB
Learn to analyze, map, and visualize the best locations for shopping malls using Remote Sensing, GIS, and GEE
What you'll learn
Understand remote sensing and GIS fundamentals to build a strong foundation for spatial data analysis and location-based decision-making.
Identify key criteria like roads, population, slope, and land cover for mall site selection through spatial data integration and evaluation.
Process and analyze geospatial datasets using Google Earth Engine, including raster operations, masking, and visualization techniques.
Generate and export mall suitability maps in GEE, combining multiple layers and applying weighted overlays for spatial decision support.
Requirements
No prior experience with Google Earth Engine is required - the course will guide you step-by-step.
Description
Urban development decisions like mall placement can have a major impact on accessibility, economic growth, and environmental balance. This course empowers you to make such decisions using modern geospatial tools. Through this hands-on learning journey, you will develop the skills to carry out site suitability mapping for shopping malls using Remote Sensing, GIS, and the cloud-based platform Google Earth Engine (GEE).The course begins with a solid foundation in Remote Sensing-what it is, how satellite imagery is captured, and how it applies to urban analysis. You'll then explore GIS fundamentals, including spatial layers, vector and raster data, and how they combine to model real-world locations.Next, you'll dive into the principles of site suitability mapping, learning how to translate real-world criteria into spatial rules. After learning how to use GEE, you'll implement a complete mall suitability analysis using real datasets: roads (for accessibility), population density (for customer base), slope (for buildability), and land cover (urban zones).By the end of this course, you'll know how to
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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