Show simple item record

A Structure-from-Motion Pipeline for Generating Digital Elevation Models for Surface-Runoff Analysis

dc.contributor.editorPerez-Taborda J.A.
dc.contributor.editorAvila Bernal A.G.
dc.creatorMeza J.
dc.creatorMarrugo A.G.
dc.creatorOspina G.
dc.creatorGuerrero M.
dc.creatorRomero L.A.
dc.identifier.citationMeza J., Marrugo A.G., Ospina G., Guerrero M. y Romero L.A. (2019) A Structure-from-Motion Pipeline for Generating Digital Elevation Models for Surface-Runoff Analysis. Journal of Physics: Conference Series; Vol. 1247, Núm. 1
dc.description.abstractDigital Elevation Models (DEMs) are used to derive information from the morphology of a land. The topographic attributes obtained from the DEM data allow the construction of watershed delineation useful for predicting the behavior of systems and for studying hydrological processes. Imagery acquired from Unmanned Aerial Vehicles (UAVs) and 3D photogrammetry techniques offer cost-effective advantages over other remote sensing methods such as LIDAR or RADAR. In particular, a high spatial resolution for measuring the terrain microtopography. In this work, we propose a Structure from Motion (SfM) pipeline using UAVs for generating high-resolution, high-quality DEMs for developing a rainfall-runoff model to study flood areas. SfM is a computer vision technique that simultaneously estimates the 3D coordinates of a scene and the pose of a camera that moves around it. The result is a 3D point cloud which we process to obtain a georeference model from the GPS information of the camera and ground control points. The pipeline is based on open source software OpenSfM and OpenDroneMap. Encouraging experimental results on a test land show that the produced DEMs meet the metrological requirements for developing a surface-runoff model. © Published under licence by IOP Publishing Ltd.eng
dc.format.mediumRecurso electrónico
dc.publisherInstitute of Physics Publishing
dc.titleA Structure-from-Motion Pipeline for Generating Digital Elevation Models for Surface-Runoff Analysis
dcterms.bibliographicCitationSmith, M., Carrivick, J., Quincey, D., (2016) Progress in Physical Geography, 40 (2), pp. 247-275
dcterms.bibliographicCitationMarcus, W.A., Fonstad, M.A., (2008) Earth Surface Processes and Landforms, 33 (1), pp. 4-24
dcterms.bibliographicCitationWestoby, M., Brasington, J., Glasser, N., Hambrey, M., Reynolds, J., (2012) Geomorphology, 179, pp. 300-314
dcterms.bibliographicCitationEnciso, J., Jung, J., Chang, A., (2018) Journal of Applied Remote Sensing, 12 (1), pp. 1-9
dcterms.bibliographicCitationNobajas, A., Waller, R.I., Robinson, Z.P., Sangonzalo, R., (2017) International Journal of Remote Sensing, 38 (8-10), pp. 2844-2860
dcterms.bibliographicCitationGoesele, M., Curless, B., Seitz, S.M., (2006) Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 2402-2409. , (IEEE)
dcterms.bibliographicCitationAgisoft Photoscan Professional, ,
dcterms.bibliographicCitation, Pix4d
dcterms.bibliographicCitationMeza, J., Marrugo, A.G., Sierra, E., Guerrero, M., Meneses, J., Romero, L.A., (2018) Communications in Computer and Information Science, 885, pp. 213-225
dcterms.bibliographicCitation, Mapillary: Opensfm
dcterms.bibliographicCitation, Opendronemap
dcterms.bibliographicCitationBradski, G., Kaehler, A., (2000) Dr. Dobb's Journal of Software Tools, 3
dcterms.bibliographicCitationTriggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W., (1999) International Workshop on Vision Algorithms, pp. 298-372. , (Springer)
dcterms.bibliographicCitationFurukawa, Y., Ponce, J., (2010) IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (8), pp. 1362-1376
dcterms.bibliographicCitationAdorjan, M., (2016) Ein Kollaboratives Structure-from-Motion System, , (Technischen UniversitDot
dcterms.bibliographicCitationat Wien) Master's thesis
dcterms.bibliographicCitationPDAL Contributors 2018 PDAL: The Point Data Abstraction Library, ,
dcterms.bibliographicCitationChen, Z., Devereux, B., Gao, B., Amable, G., (2012) ISPRS Journal of Photogrammetry and Remote Sensing, 72, pp. 121-130
dcterms.bibliographicCitationPingel, T.J., Clarke, K.C., McBride, W.A., (2013) ISPRS Journal of Photogrammetry and Remote Sensing, 77, pp. 21-30
dc.source.event6th National Conference on Engineering Physics, CNIF 2018 and the 1st International Conference on Applied Physics Engineering and Innovation, APEI 2018
dc.subject.keywordsCost effectiveness
dc.subject.keywordsDigital instruments
dc.subject.keywordsEngineering research
dc.subject.keywordsObject recognition
dc.subject.keywordsOpen source software
dc.subject.keywordsOpen systems
dc.subject.keywordsOptical radar
dc.subject.keywordsRemote sensing
dc.subject.keywordsRock mechanics
dc.subject.keywordsComputer vision techniques
dc.subject.keywordsDigital elevation model
dc.subject.keywordsGround control points
dc.subject.keywordsHigh spatial resolution
dc.subject.keywordsRainfall-runoff modeling
dc.subject.keywordsStructure from motion
dc.subject.keywordsSurface runoff modeling
dc.subject.keywordsWatershed delineation
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.description.notesThis work has been partly funded by Universidad Tecnológica de Bolívar project (FI2006T2001). The authors thank Direccion de Investigaciones Universidad Tecnologica de Bolivar for their support.
dc.relation.conferencedate22 October 2018 through 26 October 2018

Files in this item


This item appears in the following Collection(s)

Show simple item record
Except where otherwise noted, this item's license is described as