Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W2, 459-464, 2013
https://doi.org/10.5194/isprsarchives-XL-5-W2-459-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
22 Jul 2013
POINT CLOUD METRICS FOR SEPARATING STANDING ARCHAEOLOGICAL REMAINS AND LOW VEGETATION IN ALS DATA
R. Opitz2,1 and L. Nuninger2 1CAST, University of Arkansas, Fayetteville, AR USA
2Chrono-Environnement UMR 6249, LEA ModeLTER, MSHE Ledoux USR 3124, CNRS, Besançon, France
Keywords: ALS, classification, archaeology, built structures, terrain models Abstract. The integration of Airborne Laser Scanning survey into archaeological research and cultural heritage management has substantially added to our knowledge of archaeological remains in forested areas, and is changing our understanding of how these landscapes functioned in the past. While many types of archaeological remains manifest as micro-topography, several important classes of features commonly appear as standing remains. The identification of these remains is important for archaeological prospection surveys based on ALS data, and typically represent structures from the Roman, Medieval and early Modern periods. Standing structures in mixed scenes with vegetation are not well addressed by standard classification approaches developed to identify bare earth (terrain), individual trees or plot characteristics, or buildings (roofed structures). In this paper we propose an approach to the identification of these structures in the point cloud based on multi-scale measures of local density, roughness, and normal orientation. We demonstrate this approach using discrete-return ALS data collected in the Franche-Comte region of France at a nominal point density of 8 pts/m2, a resolution which, in coming years, will become increasingly available to archaeologists through government supported mapping schemes.
Conference paper (PDF, 1562 KB)


Citation: Opitz, R. and Nuninger, L.: POINT CLOUD METRICS FOR SEPARATING STANDING ARCHAEOLOGICAL REMAINS AND LOW VEGETATION IN ALS DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5/W2, 459-464, https://doi.org/10.5194/isprsarchives-XL-5-W2-459-2013, 2013.

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