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Open AccessFeature PaperArticle

A Deep Learning-Based Solution for Large-Scale Extraction of the Secondary Road Network from High-Resolution Aerial Orthoimagery

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Departamento de Ingeniería Topográfica y Cartografía, E.T.S.I. en Geodesia, Topografía, Geodesia y Cartografía, Universidad Politécnica de Madrid, 28031 Madrid, Spain
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Departamento de Inteligencia Artificial, E.T.S.I. de Sistemas Informáticos, Universidad Politécnica de Madrid, 28031 Madrid, Spain
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Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(20), 7272; https://doi.org/10.3390/app10207272
Received: 21 September 2020 / Revised: 12 October 2020 / Accepted: 14 October 2020 / Published: 17 October 2020
Secondary roads represent the largest part of the road network. However, due to the absence of clearly defined edges, presence of occlusions, and differences in widths, monitoring and mapping them represents a great effort for public administration. We believe that recent advancements in machine vision allow the extraction of these types of roads from high-resolution remotely sensed imagery and can enable the automation of the mapping operation. In this work, we leverage these advances and propose a deep learning-based solution capable of efficiently extracting the surface area of secondary roads at a large scale. The solution is based on hybrid segmentation models trained with high-resolution remote sensing imagery divided in tiles of 256 × 256 pixels and their correspondent segmentation masks, resulting in increases in performance metrics of 2.7–3.5% when compared to the original architectures. The best performing model achieved Intersection over Union and F1 scores of maximum 0.5790 and 0.7120, respectively, with a minimum loss of 0.4985 and was integrated on a web platform which handles the evaluation of large areas, the association of the semantic predictions with geographical coordinates, the conversion of the tiles’ format and the generation of geotiff results compatible with geospatial databases. View Full-Text
Keywords: aerial orthoimagery; deep learning; remote sensing; road extraction; semantic segmentation; web-based segmentation solution aerial orthoimagery; deep learning; remote sensing; road extraction; semantic segmentation; web-based segmentation solution
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Cira, C.-I.; Alcarria, R.; Manso-Callejo, M.-Á.; Serradilla, F. A Deep Learning-Based Solution for Large-Scale Extraction of the Secondary Road Network from High-Resolution Aerial Orthoimagery. Appl. Sci. 2020, 10, 7272.

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