Introduction Generally, the data from remote sensing surveys is screened manually in archaeology. However, constant monitoring of the earth's surface-by a multitude of airborne and satellite sensors-causes a huge influx of data of high complexity and high quality. To cope with this ever-growing set of largely digital and easily available data, computer-aided methods for the processing of data and the detection of archaeological objects 1 are needed (Bennett, Cowley & De Laet 2014: 896). Over a decade ago, archaeologists started developing computational methods for the (semi-)automated detection of archaeological objects (De Boer 2007; De Laet, Paulissen & Waelkens 2007). Since then multiple case studies have shown these algorithms to be capable of detecting well-defined archaeological traces, such as barrows (see for example Sevara et al. 2016). However, these (often) handcrafted algorithms are highly specialised on specific, single object categories and data sources, which restricts their use in different contexts and limits their usability in general for archaeological prospection. Furthermore, these approaches are predominantly complex algorithms that can require a high level of expertise to operate, and are regularly dependent on expensive software. All this results in an implementation that is limited in its user-friendliness (see also Ball, Anderson & Seng Chan 2017: 3). To overcome the aforementioned limitations, this research project explores the implementation of advanced computational methods to develop a generic, flexible and robust automated detection method for archaeological objects in remotely sensed data. More specifically, this project aims to develop user-friendly workflows for the detection of multiple classes of archaeological objects in LiDAR (Light Detection And Ranging; Wehr & Lohr 1999) data using Deep Learning (Goodfellow, Bengio & Courville 2016). The research project, a four-year PhD, is part of the Data Science Research Programme (DSRP) at the Faculty of Archaeology and the Leiden Centre of Data Science (LCDS) at Leiden University. The DSRP aims to bring together domain knowledge and associated 'big data' problems (for instance in archaeology) with the technical methods and solutions from data science. This paper presents the results of the first year of the PhD project consisting of the first workflow developed, called WODAN (Workflow for Object Detection of Archaeology in the Netherlands). WODAN has successfully been implemented on LiDAR data from the research area in the Netherlands (Figure 1). The workflow serves as a proof of concept, to demonstrate that by implementing deep learning techniques it is possible to create a multi-class

This paper describes the 3D modelling of Pinchango Alto, Peru, based on a combination of image and range data. Digital photogrammetry and laser scanning allow archaeological sites to be recorded efficiently and in detail even under unfavourable conditions. In 2004 we documented Pinchango Alto, a typical site of the hitherto poorly studied Late Intermediate Period on the south coast of Peru, with the aim of conducting spatial archaeological analyses at different scales. The combined use of a mini helicopter and a terrestrial laser scanner, both equipped with a camera, allowed a fast yet accurate recording of the site and its stone architecture. In this paper we describe the research background, the 3D modelling based on different image and range data sets, and the resulting products that will serve as a basis for archaeological analysis.

Although the history of automated archaeological object detection in remotely sensed data is short, progress and emerging trends are evident. Among them, the shift from rule-based approaches towards machine learning methods is, at the moment, the cause for high expectations, even though basic problems, such as the lack of suitable archaeological training data are only beginning to be addressed. In a case study in the central Netherlands, we are currently developing novel methods for multi-class archaeological object detection in LiDAR data based on convolutional neural networks (CNNs). This research is embedded in a long-term investigation of the prehistoric landscape of our study region. We here present an innovative integrated workflow that combines machine learning approaches to automated object detection in remotely sensed data with a two-tier citizen science project that allows us to generate and validate detections of hitherto unknown archaeological objects, thereby contributing to the creation of reliable, labeled archaeological training datasets. We motivate our methodological choices in the light of current trends in archaeological prospection, remote sensing, machine learning, and citizen science, and present the first results of the implementation of the workflow in our research area.

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.

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