Geomorphometric Methods for Burial Mound Recognition and Extraction from High-Resolution LiDAR DEMs

被引:14
|
作者
Niculita, Mihai [1 ]
机构
[1] Alexandru Ioan Cuza Univ, Dept Geog, Iasi 700505, Romania
关键词
archaeological topography; tumulus; burial mound; geomorphometry; high-resolution; DEM; LiDAR; random forest; SKY-VIEW FACTOR; ARCHAEOLOGICAL FEATURES; AIRBORNE LIDAR; AUTOMATIC DETECTION; VISUALIZATION; TOPOGRAPHY; LANDSCAPE; MODELS; RECONSTRUCTION; ALGORITHM;
D O I
10.3390/s20041192
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Archaeological topography identification from high-resolution DEMs (Digital Elevation Models) is a current method that is used with high success in archaeological prospecting of wide areas. I present a methodology through which burial mounds (tumuli) from LiDAR (Light Detection And Ranging) DEMS can be identified. This methodology uses geomorphometric and statistical methods to identify with high accuracy burial mound candidates. Peaks, defined as local elevation maxima are found as a first step. In the second step, local convexity watershed segments and their seeds are compared with positions of local peaks and the peaks that correspond or have in vicinity local convexity segments seeds are selected. The local convexity segments that correspond to these selected peaks are further fed to a Random Forest algorithm together with shape descriptors and descriptive statistics of geomorphometric variables in order to build a model for the classification. Multiple approaches to tune and select the proper training dataset, settings, and variables were tested. The validation of the model was performed on the full dataset where the training was performed and on an external dataset in order to test the usability of the method for other areas in a similar geomorphological and archaeological setting. The validation was performed against manually mapped, and field checked burial mounds from two neighbor study areas of 100 km(2) each. The results show that by training the Random Forest on a dataset composed of between 75% and 100% of the segments corresponding to burial mounds and ten times more non-burial mounds segments selected using Latin hypercube sampling, 93% of the burial mound segments from the external dataset are identified. There are 42 false positive cases that need to be checked, and there are two burial mound segments missed. The method shows great promise to be used for burial mound detection on wider areas by delineating a certain number of tumuli mounds for model training.
引用
收藏
页数:30
相关论文
共 50 条
  • [1] High-Resolution LiDAR-Derived DEMs in Hydrografic Network Extraction and Short-Time Landscape Changes
    Lazzari, Maurizio
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT II, 2020, 12250 : 723 - 737
  • [2] High-resolution topography and anthropogenic feature extraction: testing geomorphometric parameters in floodplains
    Sofia, Giulia
    Dalla Fontana, Giancarlo
    Tarolli, Paolo
    HYDROLOGICAL PROCESSES, 2014, 28 (04) : 2046 - 2061
  • [3] Gully boundary extraction based on multidirectional hill-shading from high-resolution DEMs
    Yang, Xin
    Li, Min
    Na, Jiaming
    Liu, Kai
    TRANSACTIONS IN GIS, 2017, 21 (06) : 1204 - 1216
  • [5] High-resolution DEMs for urban applications from NAPP photography
    Davis, CH
    Wang, XY
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2001, 67 (05): : 585 - 592
  • [6] The influence of roads on depressional storage capacity estimates from high-resolution LiDAR DEMs in a Canadian Prairie agricultural basin
    Annand, Holly J. J.
    Wheater, Howard S. S.
    Pomeroy, John W. W.
    CANADIAN WATER RESOURCES JOURNAL, 2024, 49 (01) : 117 - 136
  • [7] Recognition of crevasses with high-resolution digital elevation models: Application of geomorphometric modeling and texture analysis
    Ishalina, Olga T.
    Bliakharskii, Dmitrii P.
    Florinsky, Igor, V
    TRANSACTIONS IN GIS, 2021, 25 (05) : 2529 - 2552
  • [8] Enhanced Minutiae Extraction for High-Resolution Palmprint Recognition
    Fei L.
    Teng S.
    Wu J.
    Rida I.
    Fei, Lunke (flksxm@126.com), 1600, World Scientific (17):
  • [9] Automatic Delineation of Seacliff Limits using Lidar-derived High-resolution DEMs in Southern California
    Palaseanu-Lovejoy, Monica
    Danielson, Jeff
    Thatcher, Cindy
    Foxgrover, Amy
    Barnard, Patrick
    Brock, John
    Young, Adam
    JOURNAL OF COASTAL RESEARCH, 2016, : 162 - 173
  • [10] Effect of point density and interpolation of LiDAR-derived high-resolution DEMs on landscape scarp identification
    Chu, Hone-Jay
    Wang, Chi-Kuei
    Huang, Min-Lang
    Lee, Chung-Cheng
    Liu, Chun-Yu
    Lin, Chih-Chiao
    GISCIENCE & REMOTE SENSING, 2014, 51 (06) : 731 - 747