Objective classification of settlement deposits is a prerequisite for understanding human‐environment interactions at habitation sites. This paper presents a novel approach combining a relatively fine‐scale sampling strategy, a multimethod geoarchaeological investigation of cores and multivariate statistics to aid in the classification and interpretation of complex and intricately stratified archaeological deposits. Heterogeneous settlement deposits, buried soils, colluvial, fluvial, and fluvioglacial sediments from cores retrieved in the Viking settlement Hedeby were investigated using six cost‐effectively measurable geoecological parameters: loss on ignition at 550°C, magnetic susceptibility, contents of stones, artifacts, bones, and charcoal with wood. Principal component analysis allowed identifying variables that would sufficiently describe data and cluster analysis enabled the classification of the materials. As a result, 13 classes were distinguished with a detailed and reliable differentiation of materials of natural and cultural genesis. Based on spatial distribution patterns of the classes, hypotheses regarding land use in the adjacent areas were made: Waste disposal in the valley of Hedeby‐brook and metallurgic activities north of it. This approach is valuable for coring‐based research at settlements, in particular at tightly managed heritage sites, and for surveys to identify potential excavation sites, whereas the set of variables must be adjusted according to local conditions.
This paper presents a workflow for UXO detection based on multibeam data in combination with AUVbased ground truth. An artificial neuronal network (ANN) is trained on manually annotated multibeam data and aims for making UXO detection and the generation of target lists faster and more objective. Prior to annotation the data is checked according to several quality factors to ensure that it fits for the purpose of object detection. The quality and accuracy of annotations has an influence on the predicted probabilities of the ANN, as the probabilities of the annotations determined by the experts are considered during training.To make this whole workflow even more effective in terms of survey time, the quality check and ANN analysis can run automated on the survey vessel. While MBES mapping continues, autonomous underwater vehicles (AUVs) can be used to ground truth possible targets with additional sensors, such as geomagnetic and under water cameras. The more precise the training data, the more reliable the ANN outcome will be.
Within the Marispace-X project, a digital maritime data space based on data sovereignty, security, interoperability, and modularity according to the Gaia-X concept should be created. The article provides an overview of the envisioned Marispace-X digital infrastructure, a decentralized maritime data ecosystem, followed by exemplary applications for hydrographic surveys and some of the use cases defined in the Marispace-X project. These exemplary applications are related to data management, sharing and processing services that can be performed on a cloud infrastructure. This digital infrastructure should simplify data handling of all the different data types and formats that exist in the maritime domain.
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