gicait/DL-for-satellite-image-analysis
This includes short and minimalistic few examples covering fundamentals of Deep Learning for Satellite Image Analysis (Remote Sensing).
This collection of tutorials helps geospatial analysts and remote sensing specialists learn how to apply deep learning to satellite imagery. It takes satellite images and other geospatial data as input, guiding you through the process to produce maps for building identification and land cover classification. The examples are designed for individuals who work with satellite data and want to enhance their image analysis capabilities using advanced machine learning techniques.
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Use this if you are a remote sensing specialist or geospatial analyst with basic Python skills and want to learn how to apply deep learning models like U-Net for tasks such as identifying buildings or classifying land cover in satellite images.
Not ideal if you are looking for a theoretical deep dive into the mathematics of deep learning, or if you need an out-of-the-box software solution rather than guided programming examples.
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MIT
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Aug 12, 2021
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