| File Name: | Detecting Offshore Wind Farms with Sentinel-2 and GEE |
| Content Source: | https://www.udemy.com/course/detecting-offshore-wind-farms-with-sentinel-2-and-gee |
| Genre / Category: | Other Tutorials |
| File Size : | 290.6 MB |
| Publisher: | Earth’s AI |
| Updated and Published: | August 2, 2025 |
This course offers an in-depth exploration of remote sensing technologies and their practical applications using Sentinel-2 satellite data and Google Earth Engine (GEE). Starting with the fundamentals of remote sensing, learners will understand key concepts such as electromagnetic spectrum, spatial and spectral resolution, and image acquisition principles. The course then delves into the specifics of Sentinel-2’s spectral bands, emphasizing their spectral signatures and how these are applied to identify and analyze various land and water features.
Students will gain hands-on experience with GEE, a powerful cloud-based geospatial processing platform, learning to manipulate, analyze, and visualize large satellite datasets efficiently. The course culminates with a focused module on detecting offshore wind farms using Sentinel-2 data within GEE. Here, learners will apply techniques including NDWI (Normalized Difference Water Index) to mask water bodies, brightness thresholding to isolate man-made structures, and difference analysis to identify wind turbines in offshore environments.
Throughout the course, emphasis is placed on understanding the technical workflows, including image filtering, cloud masking, spectral index calculation, and exporting processed results for further use. This course is designed for geospatial professionals, environmental scientists, and data analysts seeking to enhance their remote sensing skills with practical satellite image processing and analysis. By the end, learners will be equipped to implement advanced remote sensing techniques for environmental monitoring and infrastructure detection, leveraging the latest satellite data and cloud computing tools.
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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