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.

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.

In this paper, we present the web-based, open source software OpenAtlas, which uses the International Council of Museums’ Conceptual Reference Model (CIDOC CRM), and its possible future potential for the acquisition, analysis and dissemination of a wide range of archaeological and historical data on a landscape basis. To this end, we will first introduce the ongoing research project The Anthropological and Archaeological Database of Sepultures (THANADOS), built upon OpenAtlas, as well as its data model and interactive web interface/presentation frontend. Subsequently, the article will then discuss the possible extension of this database of early medieval cemeteries with regard to the integration of further archaeological structures (e.g., medieval settlements, fortifications, field systems and traffic routes) and other data, such as historical maps, aerial photographs and airborne laser scanning data. Finally, the paper will conclude with the general added value for future research projects by such a collaborative and web-based approach.

It has become almost standard practice that archaeological research on cemeteries is published in a similar fashion, specifically when primary sources supplement the data presented. Aside from the interpretative part, a catalog of all graves, buried individuals, and finds is published along with a map of the site and graphical depictions of the various entities. This is mostly structured within a four-level hierarchy beginning with the cemetery, the contained graves, the burials from each grave, and the finds associated with the burial. Today, even though many publications and their catalogs are based on or derived from digital data and published as open access, the outcome is often printed text such as a pdf file. Digital data that is properly structured and can be used out of the box for further analyses is rarely available. The presented article discusses how to digitize data on burials and how to provide them to the public in sustainable and comprehensible ways. Within previous and ongoing projects, the author and his team have developed a database system (OpenAtlas) that is used for the data acquisition of archaeological and anthropological research data that also maps information directly to the CIDOC CRM. Temporal and spatial fuzziness are dealt with following various concepts such as GeoJSON-T. For providing the data as Linked Open Data, the “linked places” format is used and an API provides a JSON-LD representation of each entity. Due to the “standard” approach implemented when publishing cemeteries, the data acquisition is mostly achieved by manually recording the published information in the database. In the following projects, data from several hundred Early Medieval Austrian and Czech burial sites with several thousand graves and finds have been digitized. To publicize the information, an online web application (https://thanados.net) has been developed to present and disseminate this data.

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