{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T21:04:27Z","timestamp":1773695067709,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T00:00:00Z","timestamp":1708732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"VLAIO COOCK","award":["HBC.2021.0553"],"award-info":[{"award-number":["HBC.2021.0553"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Drone-based photogrammetry typically requires the task of georeferencing aerial images by detecting the center of Ground Control Points (GCPs) placed in the field. Since this is a very labor-intensive task, it could benefit greatly from automation. In this study, we explore the extent to which traditional computer vision approaches can be generalized to deal with variability in real-world drone data sets and focus on training different residual neural networks (ResNet) to improve generalization. The models were trained to detect single keypoints of fixed-sized image tiles with a historic collection of drone-based Red\u2013Green\u2013Blue (RGB) images with black and white GCP markers in which the center was manually labeled by experienced photogrammetry operators. Different depths of ResNets and various hyperparameters (learning rate, batch size) were tested. The best results reached sub-pixel accuracy with a mean absolute error of 0.586. The paper demonstrates that this approach to drone-based mapping is a promising and effective way to reduce the human workload required for georeferencing aerial images.<\/jats:p>","DOI":"10.3390\/rs16050794","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T10:40:17Z","timestamp":1708944017000},"page":"794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automating Ground Control Point Detection in Drone Imagery: From Computer Vision to Deep Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2251-2915","authenticated-orcid":false,"given":"Gonzalo","family":"Murad\u00e1s Odriozola","sequence":"first","affiliation":[{"name":"Image and Speech Processing (PSI), Department of Electrical Engineering (ESAT), KU Leuven, B-3000 Leuven, Belgium"},{"name":"Laboratory for Experimental Psychology, Faculty of Psychology and Educational Sciences, KU Leuven, B-3000 Leuven, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2183-1917","authenticated-orcid":false,"given":"Klaas","family":"Pauly","sequence":"additional","affiliation":[{"name":"Remote Sensing Unit, Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium"}]},{"given":"Samuel","family":"Oswald","sequence":"additional","affiliation":[{"name":"Remote Sensing Unit, Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium"}]},{"given":"Dries","family":"Raymaekers","sequence":"additional","affiliation":[{"name":"Remote Sensing Unit, Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,24]]},"reference":[{"key":"ref_1","first-page":"16","article-title":"Unmanned aerial vehicle and geospatial technology pushing the limits of development","volume":"4","author":"Tiwari","year":"2015","journal-title":"Am. 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