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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/172335
Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation
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In recent years, different deep learning techniques were applied to segment aerial and satellite images. Nevertheless, state of the art techniques for land cover segmentation does not provide accurate results to be used in real applications. This is a problem faced by institutions and companies that want to replace time-consuming and exhausting human work with AI technology. In this work, we propose a method that combines deep learning with a human-in-the-loop strategy to achieve expert-level results at a low cost. We use a neural network to segment the images. In parallel, another network is used to measure uncertainty for predicted pixels. Finally, we combine these neural networks with a human-in-the-loop approach to produce correct predictions as if developed by human photointerpreters. Applying this methodology shows that we can increase the accuracy of land cover segmentation tasks while decreasing human intervention.
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GARCÍA RODRÍGUEZ, Carlos, VITRIÀ I MARCA, Jordi and MORA SACRISTÁN, Oscar. Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation. Remote Sensing. 2020. Vol. 12, num. 22. ISSN 2072-4292. [consulted: 19 of August of 2026]. Available at: https://hdl.handle.net/2445/172335