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Open AccessArticle

Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography

1
Centro de Informática y Comunicaciones, E.T.S.I. de Sistemas Informáticos, Universidad Politécnica de Madrid, 28031 Madrid, Spain
2
Departamento de Inteligencia Artificial, E.T.S.I. de Sistemas Informáticos, Universidad Politécnica de Madrid, 28031 Madrid, Spain
3
Departamento de Ingeniería Topográfica y Cartografía, E.T.S.I. Topografía, Geodesia y Cartografía, Universidad Politécnica de Madrid, 28031 Madrid, Spain
*
Author to whom correspondence should be addressed.
Current address: Universidad Politécnica de Madrid (Campus Sur), Calle Alan Turing s/n (Ctra. de Valencia, Km. 7), C.P. 28031 Madrid, Spain.
Appl. Sci. 2020, 10(11), 3953; https://doi.org/10.3390/app10113953
Received: 1 May 2020 / Revised: 28 May 2020 / Accepted: 3 June 2020 / Published: 6 June 2020
(This article belongs to the Section Computing and Artificial Intelligence)
Photogrammetry involves aerial photography of the Earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It is used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our main goal is the automatic design of deep neural network architectures with grammar-guided genetic programming. In this kind of evolutive algorithm, all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g., Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state-of-the-art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan. View Full-Text
Keywords: grammar evolution; deep learning; network architecture search; grammar-guided genetic programming grammar evolution; deep learning; network architecture search; grammar-guided genetic programming
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MDPI and ACS Style

de la Fuente Castillo, V.; Díaz-Álvarez, A.; Manso-Callejo, M.-Á.; Serradilla García, F. Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography. Appl. Sci. 2020, 10, 3953.

AMA Style

de la Fuente Castillo V, Díaz-Álvarez A, Manso-Callejo M-Á, Serradilla García F. Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography. Applied Sciences. 2020; 10(11):3953.

Chicago/Turabian Style

de la Fuente Castillo, Víctor; Díaz-Álvarez, Alberto; Manso-Callejo, Miguel-Ángel; Serradilla García, Francisco. 2020. "Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography" Appl. Sci. 10, no. 11: 3953.

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