{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:27:38Z","timestamp":1771518458412,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1650547"],"award-info":[{"award-number":["1650547"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unsupervised machine learning algorithms (clustering, genetic, and principal component analysis) automate Unmanned Aerial Vehicle (UAV) missions as well as the creation and refinement of iterative 3D photogrammetric models with a next best view (NBV) approach. The novel approach uses Structure-from-Motion (SfM) to achieve convergence to a specified orthomosaic resolution by identifying edges in the point cloud and planning cameras that \u201cview\u201d the holes identified by edges without requiring an initial model. This iterative UAV photogrammetric method successfully runs in various Microsoft AirSim environments. Simulated ground sampling distance (GSD) of models reaches as low as     3.4     cm per pixel, and generally, successive iterations improve resolution. Besides analogous application in simulated environments, a field study of a retired municipal water tank illustrates the practical application and advantages of automated UAV iterative inspection of infrastructure using     63 %     fewer photographs than a comparable manual flight with analogous density point clouds obtaining a GSD of less than 3 cm per pixel. Each iteration qualitatively increases resolution according to a logarithmic regression, reduces holes in models, and adds details to model edges.<\/jats:p>","DOI":"10.3390\/rs12132169","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T10:41:09Z","timestamp":1594118469000},"page":"2169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Automated 3D Reconstruction Using Optimized View-Planning Algorithms for Iterative Development of Structure-from-Motion Models"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2180-9744","authenticated-orcid":false,"given":"Samuel","family":"Arce","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering, Ira A. Fulton College of Engineering and Technology, Brigham Young University, 350 Clyde Building, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9041-2946","authenticated-orcid":false,"given":"Cory A.","family":"Vernon","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, Ira A. Fulton College of Engineering and Technology, Brigham Young University, 350 Clyde Building, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7025-0150","authenticated-orcid":false,"given":"Joshua","family":"Hammond","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, Ira A. Fulton College of Engineering and Technology, Brigham Young University, 350 Clyde Building, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valerie","family":"Newell","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, Ira A. Fulton College of Engineering and Technology, Brigham Young University, 350 Clyde Building, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph","family":"Janson","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, Ira A. Fulton College of Engineering and Technology, Brigham Young University, 350 Clyde Building, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6804-8199","authenticated-orcid":false,"given":"Kevin W.","family":"Franke","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Ira A. Fulton College of Engineering and Technology, Brigham Young University, 368 Clyde Building, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5535-5277","authenticated-orcid":false,"given":"John D.","family":"Hedengren","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, Ira A. Fulton College of Engineering and Technology, Brigham Young University, 350 Clyde Building, Provo, UT 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wallace, L., Lucieer, A., Malenovsk\u00fd, Z., Turner, D., and Vop\u011bnka, P. (2016). Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests, 7.","DOI":"10.3390\/f7030062"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nikolic, J., Burri, M., Rehder, J., Leutenegger, S., Huerzeler, C., and Siegwart, R. (2013, January 2\u20139). A UAV system for inspection of industrial facilities. Proceedings of the IEEE Aerospace Conference Proceedings, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2013.6496959"},{"key":"ref_3","unstructured":"Yamazaki, F., Liu, W., and Yamazaki, F. (2016, January 22\u201324). Remote Sensing Technologies For Post-Earthquake Damage Assessment: A Case Study On The 2016 Kumamoto Earthquake Satreps_Peru View Project Remote Sensing Technologies For Post-Earthquake Damage Assessment: A Case Study On The 2016 Kumamoto Earthquake. Proceedings of the ASIA Conference on Earthquake Engineering (6ACEE), Cebu City, Phillines."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1108\/00022660510617077","article-title":"Monitoring of gas pipelines\u2014A civil UAV application","volume":"77","author":"Hausamann","year":"2005","journal-title":"Aircr. Eng. Aerosp. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.procs.2015.06.058","article-title":"Health Monitoring of Civil Structures with Integrated UAV and Image Processing System","volume":"54","author":"Sankarasrinivasan","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u2018Structure-from-Motion\u2019 photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1364\/JOSAA.8.000377","article-title":"Affine structure from motion","volume":"8","author":"Koenderink","year":"1991","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s10846-015-0318-8","article-title":"Real-time Autonomous UAV Formation Flight with Collision and Obstacle Avoidance in Unknown Environment","volume":"84","author":"Cetin","year":"2016","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/S0967-0661(02)00186-7","article-title":"A survey of industrial model predictive control technology","volume":"11","author":"Qin","year":"2003","journal-title":"Control Eng. Pract."},{"key":"ref_10","unstructured":"Hudzietz, B.P., and Saripalli, S. (2011, January 14\u201316). An experimental evaluation of 3d terrain mapping with an autonomous helicopter. Proceedings of the International Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g), Zurich, Switzerland."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Borra-Serrano, I., De Swaef, T., Quataert, P., Aper, J., Saleem, A., Saeys, W., Somers, B., Rold\u00e1n-Ruiz, I., and Lootens, P. (2020). Closing the Phenotyping Gap: High Resolution UAV Time Series for Soybean Growth Analysis Provides Objective Data from Field Trials. Remote Sens., 12.","DOI":"10.3390\/rs12101644"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yang, M.D., Tseng, H.H., Hsu, Y.C., and Tsai, H.P. (2020). Semantic segmentation using deep learning with vegetation indices for rice lodging identification in multi-date UAV visible images. Remote Sens., 12.","DOI":"10.3390\/rs12040633"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ashapure, A., Jung, J., Chang, A., Oh, S., Maeda, M., and Landivar, J. (2019). A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data. Remote Sens., 11.","DOI":"10.3390\/rs11232757"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Martin, R., Rojas, I., Franke, K., Hedengren, J., Martin, R.A., Rojas, I., Franke, K., and Hedengren, J.D. (2015). Evolutionary View Planning for Optimized UAV Terrain Modeling in a Simulated Environment. Remote Sens., 8.","DOI":"10.3390\/rs8010026"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Okeson, T.J., Barrett, B.J., Arce, S., Vernon, C.A., Franke, K.W., and Hedengren, J.D. (2019). Achieving Tiered Model Quality in 3D Structure from Motion Models Using a Multi-Scale View-Planning Algorithm for Automated Targeted Inspection. Sensors, 19.","DOI":"10.3390\/s19122703"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s10846-011-9576-2","article-title":"View planning for multi-view stereo 3D Reconstruction using an autonomous multicopter","volume":"65","author":"Schmid","year":"2012","journal-title":"J. Intell. Robot. Syst. Theory Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Martin, R.A., Blackburn, L., Pulsipher, J., Franke, K., and Hedengren, J.D. (2017). Potential benefits of combining anomaly detection with view planning for UAV infrastructure modeling. Remote Sens., 9.","DOI":"10.3390\/rs9050434"},{"key":"ref_18","unstructured":"Hoppe, C., Wendel, A., Zollmann, S., Paar, A., Pirker, K., Irschara, A., Bischof, H., and Kluckner, S. (2012). Photogrammetric Camera Network Design for Micro Aerial Vehicles, Institute for Computer Graphics and Vision\u2014Graz University of Technology. Technical Report."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.autcon.2018.10.006","article-title":"Framework for automated UAS-based structural condition assessment of bridges","volume":"97","author":"Morgenthal","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pan, Y., Dong, Y., Wang, D., Chen, A., and Ye, Z. (2019). Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds. Remote Sens., 11.","DOI":"10.3390\/rs11101204"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/3468.823482","article-title":"Isolated 3-D object recognition through next view planning","volume":"30","author":"Roy","year":"2000","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Trummer, M., Munkelt, C., and Denzler, J. (2010, January 23\u201326). Online next-best-view planning for accuracy optimization using an extended E-criterion. Proceedings of the International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.406"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hollinger, G.A., Englot, B., Hover, F., Mitra, U., and Sukhatme, G.S. (2012, January 14\u201318). Uncertainty-driven view planning for underwater inspection. Proceedings of the IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6224726"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1109\/LRA.2017.2655144","article-title":"A Two-Stage Optimized Next-View Planning Framework for 3-D Unknown Environment Exploration, and Structural Reconstruction","volume":"2","author":"Meng","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1109\/LRA.2019.2900507","article-title":"Fast Heuristics for the 3-D Multi-Goal Path Planning Based on the Generalized Traveling Salesman Problem With Neighborhoods","volume":"4","author":"Faigl","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Palmer, L.M., Franke, K.W., Abraham Martin, R., Sines, B.E., Rollins, K.M., and Hedengren, J.D. (2015). Application and Accuracy of Structure from Motion Computer Vision Models with Full-Scale Geotechnical Field Tests, American Society of Civil Engineers. IFCEE 2015.","DOI":"10.1061\/9780784479087.225"},{"key":"ref_27","unstructured":"Palaniappan, K., Seetharaman, G., and Doucette, P.J. (2018). Targeted 3D modeling from UAV imagery. Geospatial Informatics, Motion Imagery, and Network Analytics VIII, SPIE."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Martin, R.A., Hall, A., Brinton, C., Franke, K., and Hedengren, J.D. (2016, January 4\u20138). Privacy aware mission planning and video masking for UAV systems. Proceedings of the AIAA Infotech @ Aerospace Conference, San Diego, CA, USA.","DOI":"10.2514\/6.2016-0250"},{"key":"ref_29","unstructured":"Okeson, T.J. (2018). Camera View Planning for Structure from Motion: Achieving Targeted Inspection through More Intelligent View Planning Methods. [Ph.D. Thesis, Brigham Young University]."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine Learning: Trends, Perspectives, and Prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_31","unstructured":"Gr\u00fcn, A. (1978). Progress in photogrammetric point determination by compensation of systematic errors and detection of gross errors. Informations Relative to Cartography and Geodesy, Series 2: Translations, Verlag des Institut f\u00fcr Angewandte Geod\u00e4sie. SEE N 80-12455 03-43."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Nocerino, E., Menna, F., and Remondino, F. (2014). Accuracy of typical photogrammetric networks in cultural heritage 3D modeling projects. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 45.","DOI":"10.5194\/isprsarchives-XL-5-465-2014"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1002\/esp.3609","article-title":"Mitigating systematic error in topographic models derived from UAV and ground-based image networks","volume":"39","author":"James","year":"2014","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1145\/235815.235821","article-title":"The quickhull algorithm for convex hulls","volume":"22","author":"Barber","year":"1996","journal-title":"ACM Trans. Math. Softw."},{"key":"ref_35","unstructured":"Akkiraju, N., Edelsbrunner, H., Facello, M., Fu, P., Mucke, E.P., and Varela, C. (1995, January 19\u201322). Alpha shapes: Definition and software. Proceedings of the 1st International Computational Geometry Software Workshop, Minneapolis, MN, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1111\/j.1467-8659.2009.01388.x","article-title":"Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression","volume":"28","author":"Guennebaud","year":"2009","journal-title":"Comput. Graph. Forum"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bazazian, D., Casas, J.R., and Ruiz-Hidalgo, J. (2015, January 23\u201325). Fast and Robust Edge Extraction in Unorganized Point Clouds. Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Adelaide, SA, Australia.","DOI":"10.1109\/DICTA.2015.7371262"},{"key":"ref_38","first-page":"81","article-title":"Novel algorithms for 3D surface point cloud boundary detection and edge reconstruction","volume":"6","author":"Mineo","year":"2019","journal-title":"J. Comput. Des. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.isprsjprs.2015.01.016","article-title":"Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers","volume":"105","author":"Weinmann","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"Volume 96","author":"Ester","year":"1996","journal-title":"Kdd"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3068335","article-title":"DBSCAN Revisited, Revisited","volume":"42","author":"Schubert","year":"2017","journal-title":"ACM Trans. Database Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Spoorthy, D., Manne, S.R., Dhyani, V., Swain, S., Shahulhameed, S., Mishra, S., Kaur, I., Giri, L., and Jana, S. (2019, January 23\u201327). Automatic Identification of Mixed Retinal Cells in Time-Lapse Fluorescent Microscopy Images using High-Dimensional DBSCAN. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857375"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Baselice, F., Coppolino, L., D\u2019Antonio, S., Ferraioli, G., and Sgaglione, L. (2015, January 25\u201329). A DBSCAN based approach for jointly segment and classify brain MR images. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319021"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4549","DOI":"10.1109\/JSEN.2019.2897989","article-title":"Vision Sensor-Based Shoe Detection for Human Tracking in a Human-Robot Coexisting Environment: A Photometric Invariant Approach Using DBSCAN Algorithm","volume":"19","author":"Paral","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/TSM.2019.2916835","article-title":"A Novel DBSCAN-Based Defect Pattern Detection and Classification Framework for Wafer Bin Map","volume":"32","author":"Jin","year":"2019","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tipping, M.E., and Bishop, C.M. (1999). Mixtures of Probabilistic Principal Component Analysers, MIT Press. Technical Report 2.","DOI":"10.1162\/089976699300016728"},{"key":"ref_48","first-page":"1","article-title":"AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles","volume":"2","author":"Shah","year":"2017","journal-title":"Field Serv. Robot."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.procs.2018.10.290","article-title":"Space-based collision avoidance framework for Autonomous Vehicles","volume":"140","author":"Yu","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zuluaga, J.G.C. (2018). Deep Reinforcement Learning for Autonomous Search and Rescue. [Ph.D. Thesis, Grand Valley State University].","DOI":"10.1109\/NAECON.2018.8556642"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1016\/j.procs.2019.09.361","article-title":"Drone Forensic Investigation: DJI Spark Drone as A Case Study","volume":"159","author":"Kao","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.compag.2018.08.039","article-title":"Soil sampling with drones and augmented reality in precision agriculture","volume":"154","author":"Huuskonen","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_53","unstructured":"(2020, July 03). AgiSoft Metashape Professional (Version 1.5.5). Available online: https:\/\/www.agisoft.com\/downloads\/installer\/."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.isprsjprs.2019.11.020","article-title":"A two-step approach for the correction of rolling shutter distortion in UAV photogrammetry","volume":"160","author":"Zhou","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"McCarthy, J.K., Benjamin, J., Winton, T., and van Duivenvoorde, W. (2019). Camera Calibration Techniques for Accurate Measurement Underwater. 3D Recording and Interpretation for Maritime Archaeology, Springer.","DOI":"10.1007\/978-3-030-03635-5"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2169\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:48:28Z","timestamp":1760176108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/13\/2169"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,7]]},"references-count":55,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["rs12132169"],"URL":"https:\/\/doi.org\/10.3390\/rs12132169","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,7]]}}}