| File Name: | Offshore Wind Turbine Detection with Sentinel-1 and GEE |
| Content Source: | https://www.udemy.com/course/offshore-wind-turbine-detection-with-sentinel-1-and-gee |
| Genre / Category: | Other Tutorials |
| File Size : | 299.1 MB |
| Publisher: | Earth’s AI |
| Updated and Published: | August 3, 2025 |
Offshore wind energy is rapidly expanding as a sustainable solution to global energy needs. Monitoring and mapping these wind farms from space is essential for environmental management, maritime safety, and infrastructure planning. This course provides a comprehensive, practical introduction to detecting offshore wind turbines using Sentinel-1 Synthetic Aperture Radar (SAR) data in Google Earth Engine (GEE).
You’ll start by understanding the fundamentals of SAR remote sensing, including the benefits of radar imagery for all-weather, day-and-night observation. The course guides you through accessing and filtering Sentinel-1 data over your region of interest, focusing on the VV polarization channel ideal for detecting man-made structures like turbines.
A key part of the workflow is reducing speckle noise inherent in radar data. You will implement the Lee filter to smooth the imagery while preserving important features. Using thresholding techniques, you will isolate turbine candidates based on their radar backscatter intensity relative to surrounding water.
Beyond detection, you’ll learn to visualize your results within GEE and export the turbine candidate maps as GeoTIFF files for integration with other GIS tools. The course emphasizes scalable, cloud-based analysis, enabling you to apply these techniques to different regions and time periods without heavy computing resources.
This course is ideal for environmental scientists, geospatial analysts, and renewable energy professionals looking to harness satellite data for offshore wind farm monitoring and maritime spatial planning.
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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