Data and datastandards
Remote sensing and geophysical research produce different types of data, and the quality of this data directly impacts the potential for further research and the strength of its conclusions. Throughout the entire research process—from data collection to analysis to storage—data can undergo transformations. Using proper standards and file formats can improve the reproducibility and traceability of the research.
Spatial Data
The data produced through remote sensing and geophysical research always has a spatial component. To position this data in space, geographical coordinates are used. The national coordinate system in the Netherlands is the Rijksdriehoeksmeting (RD), with the Onze Lieve Vrouwentoren in Amersfoort as its central point (https://www.kadaster.nl/zakelijk/registraties/basisregistraties/rijksdriehoeksmeting/rijksdriehoeksstelsel). The establishment of international standards for coordinate systems and projections follows the ISO 19111:2019 standard and is documented by the EPSG (https://epsg.org). The Dutch RD system corresponds to EPSG:28992 (https://epsg.org/crs_28992/Amersfoort-RD-New.html).
Data Models
Two types of spatial data are distinguished: vector and raster data (figure 1). Vector data consists of points, lines, or polygons (areas), where the exact coordinates of each point are known. For lines or polygons, points are connected by lines. Each object in a vector can have one or more properties (attributes) attached to it. Raster data, on the other hand, consists of data arranged in a grid. A raster is comparable to a chessboard, with each square cell containing a numerical value. A raster file is suitable for displaying gradual spatial changes, while a vector file is more suited for representing clearly defined elements.
The quality of vector and raster files depends on several factors. First, scale and resolution play a role. Resolution refers to raster files. The higher the resolution, the smaller the individual cells in the raster file. For example, if a cell is 1 cm², very precise numerical changes in space can be recorded. At a resolution of 1 km², large-scale landscape changes can be studied, but smaller features, such as burial mounds, will not be represented. For vector files, scale is often used to indicate accuracy. The scale at which a map and GIS file is created tells a lot about its usefulness. The Dutch topographic map Top10NL (https://www.pdok.nl/introductie/-/article/basisregistratie-topografie-brt-topnl) is made at a scale of 1:10,000, meaning that 1 cm on the printed map represents 100 m in reality. A burial mound with a diameter of 10 m would have a diameter of 1 mm on the map. The relationship between scale and resolution is described by Tobler (1987).
vector scale = raster resolution (in meters) × 2 × 1000
or alternatively:
raster resolution = vector scale / (2 * 1000)
This is shown in Table 1 below:
In-depth information X
There is an established standard for the calibration and validation of remote sensing data: “ISO/TS 19124-1:2023 Geographic information — Calibration and validation of remote sensing data and derived products” (https://www.iso.org/standard/79352.html). So far, however, this standard has seen little to no deliberate application within the international or national archaeological field.
During the analysis of geophysical or remote sensing data, it’s common for data to be converted from raster to vector format or vice versa. For instance, point measurements collected using LiDAR are often converted into raster files through interpolation, where the density of measurements strongly influences the resolution of the resulting raster. When interpreting raster data, vector files are sometimes created to outline observed features or structures. raster datasets can also be merged; in such cases, the file with the lowest resolution determines the minimum resolution of the combined dataset (see, for example, Conolly and Lake 2006; Lillesand et al. 2015 for further information on data processing).
Datastandards
Due to the differing nature of raster and vector data, they require different file formats. DANS recommends a number of preferred formats that are sustainable, accessible, and robust over the long term: https://dans.knaw.nl/nl/bestandsformaten/. For vector data, it is advisable to use the GeoPackage format (https://www.geopackage.org/). Raster data is best stored as GeoTIFF files (https://trac.osgeo.org/geotiff/). Both formats are open standards and can be accessed and edited using nearly all common GIS software.
According to the KNA (https://www.sikb.nl/archeologie/richtlijnen/brl-sikb-4000), Protocol 4010 on Depot Management (version 4.2) requires that documentation for field evaluations and excavations be submitted with a packing slip in accordance with OS17 (for water or land contexts) and DS05. Some excavation data may also be submitted following protocol SIKB0102 (see also Boasson & Visser 2017). For other files, the guideline states: “Files must be submitted in the native format of a computer program that is commonly used in Dutch archaeology at the time of submission.” Additionally, files must include metadata.
These protocols do not explicitly address data from remote sensing or geophysical surveys. Therefore, it is best to deposit the preferred file formats mentioned above along with appropriate metadata. Just as important is the inclusion of paradata—documentation that explains how the research was carried out—which is essential for ensuring the transparency and reproducibility of research (Huvila et al. 2024). To support this, it is important to maintain a detailed research log throughout the project.
Metadata and paradata
Both metadata and paradata describe aspects of a dataset, but they serve different purposes. Metadata focuses on describing the files and their contents, while paradata documents the processes and decisions involved in generating the data. The Dutch government has included several standards in this area on its list of mandatory standards (https://www.forumstandaardisatie.nl/open-standaarden/verplicht).
Metadata provides information about files and datasets, allowing users to assess their relevance or usability without opening them. The most widely used metadata standard is the Dublin Core (https://www.dublincore.org/), which is often used to describe metadata at the project level. For geographic data, there is a Dutch metadata profile based on ISO 19115 (https://docs.geostandaarden.nl/md/mdprofiel-iso19115/). Guidelines for depositing data, including metadata requirements, are provided by DANS: https://dans.knaw.nl/nl/handleiding-data-deponeren/tijdens-het-deponeren_ds/. These focus on ensuring that both datasets and individual files are described in a way that supports easy reuse.
Paradata, on the other hand, documents the research process itself. Its purpose is to make future users aware of the decisions and actions that shaped the dataset. This includes many factors relevant to geophysical and remote sensing research, such as weather conditions, environmental context, flight altitude, or the positioning of sensors. It can also include the use of specific software tools or algorithms. Recording this information helps ensure the transparency, traceability, and reproducibility of the data.
Referenties/verder lezen
Boasson, W., & Visser, R. M. (2017). SIKB0102: Synchronizing Excavation Data for Preservation and Re-Use. Studies in Digital Heritage, 1(2), 206–224. https://doi.org/10.14434/sdh.v1i2.23262
Conolly, J., & Lake, M. (2006). Geographical information systems in archaeology. Cambridge University Press.
Gillings, M., Hacigüzeller, P., & Lock, G. R. (Eds.). (2020). Archaeological spatial analysis: A methodological guide. Routledge.
Huvila, I., Andersson, L., & Sköld, O. (Eds.). (2024). Perspectives on Paradata: Research and Practice of Documenting Process Knowledge (Vol. 13). Springer International Publishing. https://doi.org/10.1007/978-3-031-53946-6.
Kraak, M.-J., & Ormeling, F. (2020). Cartography: Visualization of Geospatial Data, Fourth Edition (4th ed.). CRC Press. https://doi.org/10.1201/9780429464195.
Lillesand, T., Kiefer, R. W., & Chipman, J. W. (2015). Remote Sensing and Image Interpretation, 7th Edition (7th ed.). Wiley.
Lozić, E., & Štular, B. (2021). Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing. Geosciences, 11(1), Article 1. https://doi.org/10.3390/geosciences11010026.
Schmidt, A., Linford, P., Linford, N., David, A., Gaffney, C., Sarris, A., & Fassbinder, J. (2015). EAC Guidelines for the Use of Geophysics in Archaeology Questions to Ask and Points to Consider. Europae Archaeologia Consilium (EAC), Association Internationale sans But Lucratif (AISBL).
Thomson, G. H. (1994). A Practical Method of Determining the Ground Sampled Distance in Small Scale Aerospace Photography. The Journal of Photographic Science, 42(4), 129–132. https://doi.org/10.1080/00223638.1994.11738589.
Tobler, W. (1987). Measuring spatial resolution. Proceedings, Land Resources Information Systems Conference, Beijing, 1987, 12–16.
Waagen, J. (2025). Documenting drone remote sensing: A reality-based modelling approach for applications in cultural heritage and archaeology. Drone Systems and Applications, 13, 1–14. https://doi.org/10.1139/dsa-2023-0138.
Worldbank (2021) Open Night Lights. https://github.com/worldbank/OpenNightLights.