{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T12:51:37Z","timestamp":1747227097723,"version":"3.40.5"},"reference-count":0,"publisher":"Copernicus GmbH","license":[{"start":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T00:00:00Z","timestamp":1654905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AGILE GIScience Ser."],"abstract":"<jats:p>Abstract. Recent progress on geospatial and sensory, artificial intelligence technologies defines the necessity to revisit conventional geodetic techniques for surveying and mapping. In the present study, an alternative novel surveying method is implemented, which enables the precise localization of characteristic points in any area, including unknown and GNSS-denied environments, by simply using low-cost cameras. The methodology is based on novel algorithms that combine simultaneous localization and mapping (SLAM), deep learning, point-cloud processing, along with coordinate systems\u2019 transformations. The camera system subsequently detects and localizes target markers and reconstructs a 3D environment with relative coordinate estimations under a few centimeters-level of accuracy.\n                    <\/jats:p>","DOI":"10.5194\/agile-giss-3-52-2022","type":"journal-article","created":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T14:58:27Z","timestamp":1654959507000},"page":"1-3","source":"Crossref","is-referenced-by-count":0,"title":["Precision mapping through an RGB-Depth camera and deep learning"],"prefix":"10.5194","volume":"3","author":[{"given":"Georgios","family":"Petrakis","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3792-953X","authenticated-orcid":false,"given":"Panagiotis","family":"Partsinevelos","sequence":"additional","affiliation":[]}],"member":"3145","published-online":{"date-parts":[[2022,6,11]]},"container-title":["AGILE: GIScience Series"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/agile-giss.copernicus.org\/articles\/3\/52\/2022\/agile-giss-3-52-2022.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T14:58:27Z","timestamp":1654959507000},"score":1,"resource":{"primary":{"URL":"https:\/\/agile-giss.copernicus.org\/articles\/3\/52\/2022\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,11]]},"references-count":0,"URL":"https:\/\/doi.org\/10.5194\/agile-giss-3-52-2022","relation":{},"ISSN":["2700-8150"],"issn-type":[{"type":"electronic","value":"2700-8150"}],"subject":[],"published":{"date-parts":[[2022,6,11]]}}}