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

The use of Light Detection and Ranging (LiDAR) is becoming more common in different landscape exploration domains such as archaeology or geomorphology. In order to allow the detection features interest, visualization filters have be applied raw Digital Elevation Model (DEM), enhance small relief variations. Several been proposed for this purpose, Sky View Factor, Slope, negative positive Openness, Local Relief (LRM). efficiency each these methods strongly dependent on input parameters chosen regard topography investigated area. LRM has proved one most efficient, but it parameterized adapted natural slopes characterizing Generally, setting a single value, best compromise between optimal values configuration. As LiDAR mainly used wide areas, large distribution often encountered. aim paper propose Self AdaptIve LOcal Enhancer (SAILORE) based approach. filtering effect local slope, allowing at same time low-frequency variation flat well identification high-frequency presence steep slopes. First, interest self-adaptive approach presented, principle method, compared classical described. This new tool then dataset characterized by various terrain configurations test its performance compare with LRM. results show that SAILORE significantly increases capability while simplifying it.