{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:26:04Z","timestamp":1760369164015,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2014,5,27]],"date-time":"2014-05-27T00:00:00Z","timestamp":1401148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The current generation of Mobile Mapping Systems (MMSs) capture high density spatial data in a short time-frame. The quantity of data is difficult to predict as there is no concrete understanding of the point density that different scanner configurations and hardware settings will exhibit for objects at specific distances. Obtaining the required point density impacts survey time, processing time, data storage and is also the underlying limit of automated algorithms. This paper details a novel method for calculating point and profile information for terrestrial MMSs which are required for any point density calculation. Through application of algorithms utilising 3D surface normals and 2D geometric formulae, the theoretically optimal profile spacing and point spacing are calculated on targets. Both of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings-all important features in asset management surveys.<\/jats:p>","DOI":"10.3390\/s140609471","type":"journal-article","created":{"date-parts":[[2014,5,27]],"date-time":"2014-05-27T11:34:30Z","timestamp":1401190470000},"page":"9471-9488","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Calculation of Target-Specific Point Distribution for 2D Mobile Laser Scanners"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1657-5688","authenticated-orcid":false,"given":"Conor","family":"Cahalane","sequence":"first","affiliation":[{"name":"National Centre for Geocomputation, Iontas, NUI Maynooth, Maynooth, Co. Kildare, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Conor","family":"McElhinney","sequence":"additional","affiliation":[{"name":"National Centre for Geocomputation, Iontas, NUI Maynooth, Maynooth, Co. Kildare, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Lewis","sequence":"additional","affiliation":[{"name":"National Centre for Geocomputation, Iontas, NUI Maynooth, Maynooth, Co. Kildare, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4865-3644","authenticated-orcid":false,"given":"Tim","family":"McCarthy","sequence":"additional","affiliation":[{"name":"National Centre for Geocomputation, Iontas, NUI Maynooth, Maynooth, Co. Kildare, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,5,27]]},"reference":[{"key":"ref_1","unstructured":"McElhinney, C.P., Kumar, P., Cahalane, C., and McCarthy, T. (2010, January 21\u201324). Initial results from European Road Safety Inspection (EURSI) mobile mapping project. Newcastle, UK."},{"key":"ref_2","first-page":"229","article-title":"Grammar supported facade reconstruction from mobile LiDAR mapping","volume":"38","author":"Becker","year":"2009","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_3","first-page":"65","article-title":"Extracting building footprints from 3D point clouds using terrestrial laser scanning at street level","volume":"38","author":"Hammoudi","year":"2009","journal-title":"Int. Arch. Photogramm.Remote Sens."},{"key":"ref_4","unstructured":"Kumar, P., McCarthy, T., and McElhinney, C.P. (2010, January 30\u201331). 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