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Archaeological LiDAR has evolved into an indispensable component of archaeological prospection and landscape archaeology. However, it is frequently employed as a black-box digital method, which confines it to the realm of a specialized field. Making scientific publications more accessible, transparent, and reproducible is one of the steps required to turn LiDAR into a background method for all archaeologists. This is possible, according to our proposal, through the Executable Map Paper concept. This concept can be understood as a type of executable paper that pursues Open Science’s goals. The proposed technical solution consists of a PDF frontend, a persistence layer, and a hyperlinked interactive map. Executable Map Paper is applicable to all map-dependent sciences, including geography, geology, and any geoscience. In this paper, we outline the theoretical context, propose technical solutions, and provide a practical illustration.

Kamnolom v naselju Podpeč pri Ljubljani velja za glavni kamnolom rimske kolonije Emone (Ljubljana). Obstoj antičnega kamnoloma v Podpeči je bil do sedaj utemeljen le na podlagi makroskopske podobnosti med Členom litiotidnega apnenca, ki izdanja v Podpeči, in z njim povezanimi rimskimi spomeniki, odkritimi v Ljubljani. V okviru pričujočega dela smo skušali poiskati oprijemljive arheološke in geoarheološke dokaze za njegov obstoj. V skrajno severnem delu kamnoloma smo izvedli arheološko sondiranje, hkrati pa natančno določili sestavo apnenca na tem območju in v 288 izdelkih iz antične Emone. Sondiranje severno od modernega kamnoloma je odkrilo dobro ohranjene sledove rimskodobne kamnoseške dejavnosti. Hkrati je litološka analiza spomenikov iz Emone razkrila, da je kar 182 (ali 64 odstotkov) analiziranih spomenikov izdelanih iz apnenca, litološko identičnega različkom, ki izdanjajo v Podpeči. Ti različki se prostorsko grupirajo severno od modernega kamnoloma. Pri tem sicer ostaja odprto vprašanje pripadnosti nekaterih različkov apnenca v izdelkih, ki so brez diagnostičnih komponent in bi lahko pripadali tudi nekoliko starejšemu Členu krkinega apnenca. Rekonstrukcijo obsega starega kamnoloma omogoča primerjava faciesov izvornih litostratigrafskih enot in izdelkov, pa tudi historična analiza zgodnjih kartografskih in katastrskih dokumentov. Analiza najzgodnejših izdelkov je omogočila hipotezo o prihodu kamnosekov iz kamnoloma v Nabrežini.

Historically, mastery of writing was deemed essential to human progress. However, recent advances in generative AI have marked an inflection point in this narrative, including for scientific writing. This article provides a comprehensive analysis of the capabilities and limitations of six AI chatbots in scholarly writing in the humanities and archaeology. The methodology was based on tagging AI-generated content for quantitative accuracy and qualitative precision by human experts. Quantitative accuracy assessed the factual correctness in a manner similar to grading students, while qualitative precision gauged the scientific contribution similar to reviewing a scientific article. In the quantitative test, ChatGPT-4 scored near the passing grade (−5) whereas ChatGPT-3.5 (−18), Bing (−21) and Bard (−31) were not far behind. Claude 2 (−75) and Aria (−80) scored much lower. In the qualitative test, all AI chatbots, but especially ChatGPT-4, demonstrated proficiency in recombining existing knowledge, but all failed to generate original scientific content. As a side note, our results suggest that with ChatGPT-4, the size of large language models has reached a plateau. Furthermore, this paper underscores the intricate and recursive nature of human research. This process of transforming raw data into refined knowledge is computationally irreducible, highlighting the challenges AI chatbots face in emulating human originality in scientific writing. Our results apply to the state of affairs in the third quarter of 2023. In conclusion, while large language models have revolutionised content generation, their ability to produce original scientific contributions in the humanities remains limited. We expect this to change in the near future as current large language model-based AI chatbots evolve into large language model-powered software.

The rapid expansion of the Slavic speakers in the second half of the first millennium CE remains a controversial topic in archaeology, and academic passions on the issue have long run high. Currently, there are three main hypotheses for this expansion. The aim of this paper was to test the so-called “hybrid hypothesis,” which states that the movement of people, cultural diffusion and language diffusion all occurred simultaneously. For this purpose, we examined an archaeological Deep Data set with a machine learning method termed time series clustering and with emerging hot spot analysis. The latter required two archaeology-specific modifications: The archaeological trend map and the multiscale emerging hot spot analysis. As a result, we were able to detect two migrations in the Eastern Alps between c. 500 and c. 700 CE. Based on the convergence of evidence from archaeology, linguistics, and population genetics, we have identified the migrants as Alpine Slavs, i.e., people who spoke Slavic and shared specific common ancestry.

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection. However, as a step towards theoretically aware, impactful, and reproducible research, a more rigorous and transparent method of data processing is required. To this end, we set out to create a processing pipeline for archaeology-specific point cloud processing and derivation of products that are optimized for general-purpose data. The proposed pipeline improves on ground and building point cloud classification. The main area of innovation in the proposed pipeline is raster grid interpolation. We have improved the state-of-the-art by introducing a hybrid interpolation technique that combines inverse distance weighting with a triangulated irregular network with linear interpolation. State-of-the-art solutions for enhanced visualizations are included and essential metadata and paradata are also generated. In addition, we have introduced a QGIS plug-in that implements the pipeline as a one-step process. It reduces the manual workload by 75 to 90 percent and requires no special skills other than a general familiarity with the QGIS environment. It is intended that the pipeline and tool will contribute to the white-boxing of archaeology-specific airborne LiDAR data processing. In discussion, the role of data processing in the knowledge production process is explored.

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, particularly as a tool for detecting archaeological features in the landscape. However, its use for landscape reconstruction and understanding archaeological sites in their environmental context is still underutilised. To this end, we took an innovative approach to using LiDAR data as a means of discovering, documenting, and interpreting agricultural land use systems by looking for significant environmental variation within a microregion. We combined information from LiDAR-derived DEM derivatives with archaeological, geological, and soil data. We introduced two methodological innovations. The first is the modified wetness index, which combines the LiDAR-derived precision with the accuracy of the effective field capacity of the soil to obtain a very realistic predictor of soil quality. The second is the modified landform classification, a combination of topographic position index and visual geomorphological analysis, which amalgamates two of the most important predictive variables for the distribution of plant species. Our approach is demonstrated by a case study focusing on early medieval settlements in the context of agricultural land use in the subalpine microregion of Bled (Slovenia). It revealed that early medieval settlers were drawn to light soils with high water retention capacity. Such soils were particularly suitable for the cultivation of barley, which is known to have been one of the most important staple crops of the period, especially in colder climate such as subalpine. Soils with lower water retention capacity were not colonized until the eleventh century, which may signify the transition at that time to a higher level of agricultural organisation and wheat as a staple cereal food.

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection, and the need for an archaeology-specific data processing workflow is well known. It is therefore surprising that little attention has been paid to the key element of processing: an archaeology-specific DEM. Accordingly, the aim of this paper is to describe an archaeology-specific DEM in detail, provide a tool for its automatic precision assessment, and determine the appropriate grid resolution. We define an archaeology-specific DEM as a subtype of DEM, which is interpolated from ground points, buildings, and four morphological types of archaeological features. We introduce a confidence map (QGIS plug-in) that assigns a confidence level to each grid cell. This is primarily used to attach a confidence level to each archaeological feature, which is useful for detecting data bias in archaeological interpretation. Confidence mapping is also an effective tool for identifying the optimal grid resolution for specific datasets. Beyond archaeological applications, the confidence map provides clear criteria for segmentation, which is one of the unsolved problems of DEM interpolation. All of these are important steps towards the general methodological maturity of airborne LiDAR in archaeology, which is our ultimate goal.

Airborne LiDAR is a widely accepted tool for archaeological prospection. Over the last decade an archaeology-specific data processing workflow has been evolving, ranging from raw data acquisition and processing, point cloud processing and product derivation to archaeological interpretation, dissemination and archiving. Currently, though, there is no agreement on the specific steps or terminology. This workflow is an interpretative knowledge production process that must be documented as such to ensure the intellectual transparency and accountability required for evidence-based archaeological interpretation. However, this is rarely the case, and there are no accepted schemas, let alone standards, to do so. As a result, there is a risk that the data processing steps of the workflow will be accepted as a black box process and its results as “hard data”. The first step in documenting a scientific process is to define it. Therefore, this paper provides a critical review of existing archaeology-specific workflows for airborne LiDAR-derived topographic data processing, resulting in an 18-step workflow with consistent terminology. Its novelty and significance lies in the fact that the existing comprehensive studies are outdated and the newer ones focus on selected aspects of the workflow. Based on the updated workflow, a good practice example for its documentation is presented.

Identifying bare-earth or ground returns within point cloud data is a crucially important process for archaeologists who use airborne LiDAR data, yet there has thus far been very little comparative assessment of the available archaeology-specific methods and their usefulness for archaeological applications. This article aims to provide an archaeology-specific comparison of filters for ground extraction from airborne LiDAR point clouds. The qualitative and quantitative comparison of the data from four archaeological sites from Austria, Slovenia, and Spain should also be relevant to other disciplines that use visualized airborne LiDAR data. We have compared nine filters implemented in free or low-cost off-the-shelf software, six of which are evaluated in this way for the first time. The results of the qualitative and quantitative comparison are not directly analogous, and no filter is outstanding compared to the others. However, the results are directly transferable to real-world problem-solving: Which filter works best for a given combination of data density, landscape type, and type of archaeological features? In general, progressive TIN (software: lasground_new) and a hybrid (software: Global Mapper) commercial filter are consistently among the best, followed by an open source slope-based one (software: Whitebox GAT). The ability of the free multiscale curvature classification filter (software: MCC-LIDAR) to remove vegetation is also commendable. Notably, our findings show that filters based on an older generation of algorithms consistently outperform newer filtering techniques. This is a reminder of the indirect path from publishing an algorithm to filter implementation in software.

This article presents a hypothesis about three Roman period stonemasonr y workshops that operated in the area of Strahomer, Iška vas, and Ig, all of them located on the southern edge of the Ljubljansko barje, to the south of the Roman Emona. The hypothesis is based on the formal analysis of the grave stelae as well as their spatial distribution