The geoglyphs of Palpa, Peru: documentation, analysis and interpretation

The Geoglyphs of Palpa, Peru is a revision of the author´s Ph.D. thesis. In this study, the famed geoglyphs of the Paracas and Nasca cultures on the south coast of Peru are investigated in order to better understand their function and meaning. Combining aerial photogrammetry, archaeological fieldwork, and GIS-based analysis, more than 600 geoglyphs in the vicinity of the modern town of Palpa were recorded and analyzed. This interdisciplinary approach enabled the establishment of the first digital archive of these prehispanic monuments. It also led to important new insights into the origin, development, and spatial context of the geoglyphs. The Palpa dataset was furthermore used to test a recent model that explains the function and meaning of the Nasca geoglyphs in terms of Andean social, cultural, and religious traditions. The results of this study indicate that the ancient activities which took place on the geoglyphs revolved around concepts of water and fertility, and were a means of expressing social status and cultural concepts. The geoglyphs integrated the desert into the cultural landscape of the valley-based Paracas and Nasca societies, and were thus a valuable cultural resource that can still be appreciated today.Digital Archaeolog

Texture segmentation as first step towards archaeological object detection in high-resolution satellite images of the Silvretta Alps

Since 2007, the Silvretta Archaeological Project in the high Alps on the Swiss-Austrian border has been investigating the prehistoric origins of alpine pasture economy. In an area of about 540 km2 more than 20 well-preserved archaeological sites associated with alpine pastoralism have been recorded, the earliest of them dating to the Iron Age (Reitmaier (ed.), 2012; Walser and Lambers, 2012). All of the ruined huts, cellars and livestock enclosures at these sites are visible on the surface and show a limited range of shapes and proportions. According to their function, all of them are located in open grassland. Based on this sample, we are currently developing methods to detect archaeological objects of the kind described above in high-resolution satellite images of our study area (Lambers and Zingman, in press). These methods are intended to assist archaeological survey in vast and/or difficult to access areas by screening large amounts of remotely sensed images in order to detect possible archaeological sites prior to fieldwork (Cowley, 2012). Our general approach aims at assessing the probability of the presence of objects of our interest based on geometric cues that can be automatically detected in the satellite and aerial images that we use. We here describe our general methodology and the first integral step constituting a new approach to texture segmentation.Digital Archaeolog

Detection of incomplete enclosures of rectangular shape in remotely sensed images

We develop an approach for detection of ruins of livestock enclosures in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a new rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfect enclosures, but also for broken ones with distorted angles, fragmented walls, or even a completely missing wall. However, it has zero value for spurious structures with less than three sides of a perceivable rectangle. Performance analysis using large imagery of an alpine environment is provided. We show how the detection performance can be improved by learning from only a few representative examples and a large number of negatives.Computer SciencesEuropean Prehistor

Settlement and landscape history of the Northern Franconian Jura during the Bronze and Iron Ages

This paper describes the results of initial archaeological and geoarchaeological fieldwork in the Northern Franconian Jura between the cities of Bayreuth and Bamberg. Our research aims at the reconstruction of settlement patterns and strategies of land use during the Metal Ages (Bronze Age and Iron Age) in the catchment area of the river Weismain. The project is designed as a case study for research into the settlement and landscape history of a rural region of the Central German Uplands during the last two millennia before our era.European Prehistor

Learning to Look at LiDAR: The Use of R-CNN in the Automated Detection of Archaeological Objects in LiDAR Data from the Netherlands

Digital Archaeolog

User Requirement Solicitation for an Information Retrieval System Applied to Dutch Grey Literature in the Archaeology Domain

In this paper, we present the results of user requirement solicitation for a search system of grey literature in archaeology, specifically Dutch excavation reports. This search system uses Named Entity Recognition and Information Retrieval techniques to create an effective and effortless search experience. Specifically, we used Conditional Random Fields to identify entities, with an average accuracy of 56%. This is a baseline result, and we identified many possibilities for improvement. These entities were indexed in ElasticSearch and a user interface was developed on top of the index. This proof of concept was used in user requirement solicitation and evaluation with a group of end users. Feedback from this group indicated that there is a dire need for such a system, and that the first results are promising.Algorithms and the Foundations of Software technolog

Detection of fragmented rectangular enclosures in very high resolution remote sensing images

We develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfectly regular enclosures but also for ruined ones with distorted angles, fragmented walls, or even a completely missing wall. Furthermore, it has a zero value for spurious structures with less than three sides of a perceivable rectangle. We show how the detection performance can be improved by learning a linear combination of the rectangularity and size features from just a few available representative examples and a large number of negatives. Our approach allowed detection of enclosures in the Silvretta Alps that were previously unknown. A comparative performance analysis is provided. Among other features, our comparison includes the state-of-the-art features that were generated by pretrained deep convolutional neural networks (CNNs). The deep CNN features, although learned from a very different type of images, provided the basic ability to capture the visual concept of the LEs. However, our handcrafted rectangularity-size features showed considerably higher performance.European Prehistor

Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands

This paper presents WODAN2.0, a workflow using Deep Learning for the automated detection of multiple archaeological object classes in LiDAR data from the Netherlands. WODAN2.0 is developed to rapidly and systematically map archaeology in large and complex datasets. To investigate its practical value, a large, random test dataset—next to a small, non-random dataset—was developed, which better represents the real-world situation of scarce archaeological objects in different types of complex terrain. To reduce the number of false positives caused by specific regions in the research area, a novel approach has been developed and implemented called Location-Based Ranking. Experiments show that WODAN2.0 has a performance of circa 70% for barrows and Celtic fields on the small, non-random testing dataset, while the performance on the large, random testing dataset is lower: circa 50% for barrows, circa 46% for Celtic fields, and circa 18% for charcoal kilns. The results show that the introduction of Location-Based Ranking and bagging leads to an improvement in performance varying between 17% and 35%. However, WODAN2.0 does not reach or exceed general human performance, when compared to the results of a citizen science project conducted in the same research area.Archaeology of EuropeArchaeological science

Creating a Dataset for Named Entity Recognition in the Archaeology Domain

In this paper, we present the development of a training dataset for Dutch Named Entity Recognition (NER) in the archaeology domain. This dataset was created as there is a dire need for semantic search within archaeology, in order to allow archaeologists to find structured information in collections of Dutch excavation reports, currently totalling around 60,000 (658 million words) and growing rapidly. To guide this search task, NER is needed. We created rigorous annotation guidelines in an iterative process, then instructed five archaeology students to annotate a number of documents. The resulting dataset contains ~31k annotations between six entity types (artefact, time period, place, context, species & material). The inter-annotator agreement is 0.95, and when we used this data for machine learning, we observed an increase in F1 score from 0.51 to 0.70 in comparison to a machine learning model trained on a dataset created in prior work. This indicates that the data is of high quality, and can confidently be used to train NER classifiersDigital ArchaeologyComputer Science

Usability evaluation for online professional search in the Dutch archaeology domain

Digital Archaeolog