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Title: Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation
Author: García Rodríguez, Carlos
Vitrià i Marca, Jordi
Mora Sacristán, Oscar
Keywords: Aprenentatge automàtic
Xarxes neuronals (Informàtica)
Sistemes classificadors (Intel·ligència artificial)
Machine learning
Neural networks (Computer science)
Learning classifier systems
Issue Date: 23-Nov-2020
Publisher: MDPI
Abstract: 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.
Note: Reproducció del document publicat a:
It is part of: Remote Sensing, 2020, vol. 12, num. 22
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ISSN: 2072-4292
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)

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