Benyaminhosseiny/NSDL4EO
A database of over 500 published papers on Earth Observation with Remote Sensing data using Non-Supervised Deep Learning techniques, classified by their learning methods (Un-, Semi-, Self-, Transfer-, and Weakly-Supervised), Applications, Data, etc.
This is a curated database of over 500 published research papers focused on using advanced machine learning techniques, specifically non-supervised deep learning, for Earth Observation with remote sensing data. It provides insights into various learning methods like unsupervised, semi-supervised, and self-supervised approaches, along with their applications, data types used, and resolution. Remote sensing researchers, geospatial analysts, and environmental scientists can use this to quickly find relevant studies and understand the landscape of deep learning applications in their field without needing extensive labeled data.
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Use this if you are a researcher or practitioner in remote sensing looking for specific papers on non-supervised deep learning methods for tasks like forest disturbance detection, crop mapping, flood mapping, image denoising, or super-resolution.
Not ideal if you are looking for introductory material on remote sensing, supervised deep learning methods, or a general overview of Earth Observation techniques without a focus on advanced machine learning.
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Mar 06, 2024
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