| File Name: | Urban Expansion Suitability Mapping Using Remote Sensing |
| Content Source: | https://www.udemy.com/course/urban-expansion-suitability-mapping-using-remote-sensing |
| Genre / Category: | Other Tutorials |
| File Size : | 381.3 MB |
| Publisher: | Earth’s AI |
| Updated and Published: | August 5, 2025 |
As cities grow, planning future development in a sustainable and data-informed manner is crucial. This course guides you through the process of urban expansion suitability mapping using remote sensing data and the powerful cloud-based platform Google Earth Engine (GEE).
You’ll begin by learning the basics of remote sensing—how satellites collect earth observation data and what types of sensors and bands are used in land monitoring. From there, you’ll explore essential datasets such as digital elevation models (DEM) and ESA WorldCover land use classifications.
Moving from theory to application, you will learn to identify areas unsuitable for expansion, like water bodies and steep slopes, and analyze proximity to existing infrastructure. Using multi-criteria analysis, you’ll assign weights to key factors such as slope, urban proximity, and land cover to create a suitability model.
The core of this course is hands-on implementation using Google Earth Engine (GEE). You’ll write and modify code to visualize terrain, calculate slope suitability, mask out restricted land cover types, and produce a final suitability map. You’ll also learn how to export your results in GIS-ready formats like GeoTIFF.
By the end of the course, you’ll be equipped to:
- Conduct spatial modeling for urban planning
- Automate satellite data processing in GEE
- Make informed decisions using remote sensing analytics
Whether you’re a student, researcher, planner, or developer, this course empowers you with practical geospatial tools to support smart and sustainable urban growth.
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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