The archaeology domain produces large amounts of texts, too much to effectively read or manually search through for research. To alleviate this problem, we created a search system (called AGNES),... Show moreThe archaeology domain produces large amounts of texts, too much to effectively read or manually search through for research. To alleviate this problem, we created a search system (called AGNES), which combines full text search with entity and geographical search. We first created a manually labelled data set to train a Named Entity Recognition model, which is used to extract entities from text. We also did a user requirement study, and usability evaluation on the system, to make sure it is suitable for archaeological research. In a case study on Early Medieval cremations, we show that using AGNES leads to a knowledge increase when compared to the knowledge of experts, gathered using previously available search engines. This shows that this kind of intelligent search system can help with literature research, find more relevant data, and lead to a better understanding of the past. Show less
The manual analysis of remotely-sensed data is a widespread practice in local and regional scale archaeological research, as well as heritage management. However, the amount of available high... Show moreThe manual analysis of remotely-sensed data is a widespread practice in local and regional scale archaeological research, as well as heritage management. However, the amount of available high-quality, remotely-sensed data is continuously growing at a staggering rate, which creates new challenges to effectively and efficiently analyze these data and find and document the seemingly overwhelming number of potential archaeological objects. Therefore, computer-aided methods for the automated detection of archaeological objects are needed. In this thesis, the development and application of automated detection methods, based on Deep Convolutional Neural Networks, for the detection of multiple classes of archaeological objects in LiDAR data is investigated. Furthermore, the implementation of these methods into archaeological practice and the opportunities of knowledge discovery—on both a quantitative and qualitative level—for landscape or spatial archaeology are explored. Show less
