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<front>
<journal-meta>
<journal-id journal-id-type="publisher">AGILE-GISS</journal-id>
<journal-title-group>
<journal-title>AGILE: GIScience Series</journal-title>
<abbrev-journal-title abbrev-type="publisher">AGILE-GISS</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">AGILE GIScience Ser.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2700-8150</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/agile-giss-3-52-2022</article-id>
<title-group>
<article-title>Precision mapping through an RGB-Depth camera and deep learning</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Petrakis</surname>
<given-names>Georgios</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Partsinevelos</surname>
<given-names>Panagiotis</given-names>
<ext-link>https://orcid.org/0000-0002-3792-953X</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>School of Mineral Resources Engineering, Technical University of Crete, Chania, Greece</addr-line>
</aff>
<pub-date pub-type="epub">
<day>11</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>3</volume>
<elocation-id>52</elocation-id>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2022 Georgios Petrakis</copyright-statement>
<copyright-year>2022</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://agile-giss.copernicus.org/articles/3/52/2022/agile-giss-3-52-2022.html">This article is available from https://agile-giss.copernicus.org/articles/3/52/2022/agile-giss-3-52-2022.html</self-uri>
<self-uri xlink:href="https://agile-giss.copernicus.org/articles/3/52/2022/agile-giss-3-52-2022.pdf">The full text article is available as a PDF file from https://agile-giss.copernicus.org/articles/3/52/2022/agile-giss-3-52-2022.pdf</self-uri>
<abstract>
<p>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’ 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.</p>
</abstract>
<counts><page-count count="3"/></counts>
</article-meta>
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