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

Abstract

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 km2 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.

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

“…Therefore, only archaeological features that have an invariable morphology and are abundant can be targeted. Until now, the focus has been limited to a handful: mound structures (burial mounds [59,[62][63][64][65][66][67][68], charcoal kilns [69,70], and shell-rings [71]), pit structures (hunting system [72]; ore extraction pits [72,73], and bomb craters [74]), and linear sunken structures (paths [75,76], ditches [77], and mining shafts [73]). There is a recent trend of targeting complex features [78] and multiple feature types (multi-class archaeological object detection [77,[79][80][81][82][83][84]), but complex archaeological landscapes imbedded in a complex terrain with ample anthropogenic influence remains challenging [80,81].…”

Lidar visualization techniques for the construction of geoarchaeological deposit models: An overview and evaluation in alluvial environments

“…In geomorphology, these procedures are increasingly critical for providing a quantification and recognition of landforms that possess unique morphometric characteristics (Evans, 2012; Jasiewicz & Stepinski, 2013; Lin et al, 2021; Wang et al, 2010). In archaeology, the focus of these procedures has also been more restricted to the detection of morphologically distinct features such as pits, linear features, mounds and other structures (e.g., Cerrillo‐Cuenca, 2017; Freeland et al, 2016; Niculiță, 2020; Schneider et al, 2015; Trier & Pilø, 2012). However, algorithms for multiple and more complex feature types are increasingly being developed, particularly through the application of deep learning techniques such as convolutional neural networks (Bonhage et al, 2021; Bundzel et al, 2020; Meyer et al, 2019; Trier et al, 2019, 2021; Verschoof‐van der Vaart & Lambers, 2019; Verschoof‐van der Vaart et al, 2020).…”

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“…Для анализа рассчитанных по ЦМР морфометрических моделей применяют не только традиционные методы обработки изображений и распознавания образов Masini et al, 2018], но все чаще -методы машинного обучения [Guyot et al, 2018]. Так, для автоматического выявления погребальных курганов культур бронзового века был применен алгоритм случайного леса [Niculiţă, 2020], а для картографирования пробных шурфов и неглубоких выработок заброшенных оловянных и медных рудников -свёрточная нейронная сеть, ранее обученная выявлять лунные кратеры [Gallwey et al, 2019].…”