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dc.contributor.authorGonzález-Quiñones, Juan J.
dc.contributor.authorReinoso Gordo, Juan Francisco 
dc.contributor.authorLeón-Robles, Carlos A.
dc.contributor.authorGarcía-Balboa, José L.
dc.contributor.authorAriza-López, Francisco J.
dc.date.accessioned2019-03-27T13:28:42Z
dc.date.available2019-03-27T13:28:42Z
dc.date.issued2018-11-11
dc.identifier.citationGonzález-Quiñones, J.J. [et al.]. Variables Influencing the Accuracy of 3D Modeling of Existing Roads Using Consumer Cameras in Aerial Photogrammetry. Sensors 2018, 18, 3880.es_ES
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10481/55234
dc.description.abstractPoint cloud (PC) generation from photogrammetry–remotely piloted aircraft systems (RPAS) at high spatial and temporal resolution and accuracy is of increasing importance for many applications. For several years, photogrammetry–RPAS has been used to recover civil engineering works such as digital elevation models (DEMs), triangle irregular networks (TINs), contour levels, orthophotographs, etc. This study analyzes the influence of variables involved in the accuracy of PC generation over asphalt shapes and determines the most influential variable based on the development of an artificial neural network (ANN) with patterns identified in the test flights. The input variables were those involved, and output was the three-dimension root mean square error (3D-RMSE) of the PC in each ground control point (GCP). The result of the study shows that the most influential variable over PC accuracy is the modulation transfer function 50 (MTF50). In addition, the study obtained an average 3D-RMSE of 1 cm. The results can be used by the scientific and civil engineering communities to consider MTF50 variables in obtaining images from RPAS cameras and to predict the accuracy of a PC over asphalt based on the ANN developed. Also, this ANN could be the beginning of a large database containing patterns from several cameras and lenses in the world market.es_ES
dc.description.sponsorshipThe article processing charge (APC) was funded by the Research Group “Ingeniería Cartográfica” (Grant No. PAIDI-TEP-164 from the Regional Government of Andalucía) from the University of Jaén.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectPhotogrammetry es_ES
dc.subjectCameraes_ES
dc.subjectAccuracy modeles_ES
dc.subjectModulation transfer functiones_ES
dc.subjectNeural networkes_ES
dc.titleVariables Influencing the Accuracy of 3D Modeling of Existing Roads Using Consumer Cameras in Aerial Photogrammetryes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Atribución 3.0 España
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