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Article

Self-AdaptIve LOcal Relief Enhancer (SAILORE): A New Filter to Improve Local Relief Model Performances According to Local Topography

1
GEOLAB, Université Clermont Auvergne, CNRS, 63000 Clermont-Ferrand, France
2
Inrap, DST, 75000 Paris, France
3
Chrono-Environment, UMR 6249, UBFC, CNRS, 25000 Besançon, France
4
Landscape Archaeology Research Group, Catalan Institute of Classical Archaeology-ICAC, 43003 Tarragona, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Francesca Cigna
Received: 2 October 2021 / Revised: 5 November 2021 / Accepted: 6 November 2021 / Published: 18 November 2021
The use of Light Detection and Ranging (LiDAR) is becoming more and more common in different landscape exploration domains such as archaeology or geomorphology. In order to allow the detection of features of interest, visualization filters have to be applied to the raw Digital Elevation Model (DEM), to enhance small relief variations. Several filters have been proposed for this purpose, such as Sky View Factor, Slope, negative and positive Openness, or Local Relief Model (LRM). The efficiency of each of these methods is strongly dependent on the input parameters chosen in regard of the topography of the investigated area. The LRM has proved to be one of the most efficient, but it has to be parameterized in order to be adapted to the natural slopes characterizing the investigated area. Generally, this setting has a single value, chosen as the best compromise between optimal values for each relief configuration. As LiDAR is mainly used in wide areas, a large distribution of natural slopes is often encountered. The aim of this paper is to propose a Self AdaptIve LOcal Relief Enhancer (SAILORE) based on the Local Relief Model approach. The filtering effect is adapted to the local slope, allowing the detection at the same time of low-frequency relief variation on flat areas, as well as the identification of high-frequency relief variation in the presence of steep slopes. First, the interest of this self-adaptive approach is presented, and the principle of the method, compared to the classical LRM method, is described. This new tool is then applied to a LiDAR dataset characterized by various terrain configurations in order to test its performance and compare it with the classical LRM. The results of this test show that SAILORE significantly increases the detection capability while simplifying it. View Full-Text
Keywords: LiDAR; ALS; Digital Elevation Model; Local Relief Model; visualization tools; data processing; filtering; archaeology; geomorphology LiDAR; ALS; Digital Elevation Model; Local Relief Model; visualization tools; data processing; filtering; archaeology; geomorphology
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MDPI and ACS Style

Toumazet, J.-P.; Simon, F.-X.; Mayoral, A. Self-AdaptIve LOcal Relief Enhancer (SAILORE): A New Filter to Improve Local Relief Model Performances According to Local Topography. Geomatics 2021, 1, 450-463. https://0-doi-org.brum.beds.ac.uk/10.3390/geomatics1040026

AMA Style

Toumazet J-P, Simon F-X, Mayoral A. Self-AdaptIve LOcal Relief Enhancer (SAILORE): A New Filter to Improve Local Relief Model Performances According to Local Topography. Geomatics. 2021; 1(4):450-463. https://0-doi-org.brum.beds.ac.uk/10.3390/geomatics1040026

Chicago/Turabian Style

Toumazet, Jean-Pierre, François-Xavier Simon, and Alfredo Mayoral. 2021. "Self-AdaptIve LOcal Relief Enhancer (SAILORE): A New Filter to Improve Local Relief Model Performances According to Local Topography" Geomatics 1, no. 4: 450-463. https://0-doi-org.brum.beds.ac.uk/10.3390/geomatics1040026

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