LIDAR based semi-automatic pattern recognition within an archaeological landscape

Research output: Book/ReportPh.D. thesis


LIDAR data provides a novel approach for locating and monitoring cultural heritage in the landscape, especially in areas of logistical complications, e.g. forest, rough terrain, and remote areas. Manuel detection and mapping of archaeological information in the landscape is a time-consuming task. To improve and increase the possibilities of cultural heritage detection and management, computational means can offer a solution, and even reveal details that are not possible to detect by human vision and pattern detection. Within an archaeological scope, the motivation for this thesis is to asses archaeological LIDAR for automated and semi-automated procedures by detection of archaeological patterns and monuments in digital LIDAR landscapes. This is done by applying simple and open algorithmic means of segmentation and classification in LIDAR landscapes towards large-scale archaeological monument detection. The thesis gives a thorough account of the archaeological use and potential of LIDAR data; qualitative and quantitatively define the state and development of the field of automatic and semi-automatic archaeological detection for remote sensing; indicate best practice and state of the art; exemplify quality of detection by automated and semi-automated segmentation and classification of data; indicate range of potential application; apply template matching for large-scale cultural heritage investigation; compare human versus computational detection; and lastly discuss and stipulate potentials within the field of LIDAR based pattern recognition within an archaeological landscape.
Translated title of the contributionLIDAR baseret semi-automatisk mønster genkendelse indenfor et arkæologisk landskab
Original languageEnglish
Place of PublicationHeidelberg
Number of pages320
Publication statusPublished - 4 Dec 2019
Externally publishedYes

Note re. dissertation

Document type: Dissertation
Place of Publication: Heidelberg
Date: 2019
Supervisor: Dr. Armin Volkmann
Version: Primary publication
Date Deposited: 04 Dec 2019 06:29

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