4.1. Evaluating Our Approach

61,63,64,65, Automatic feature detection approaches for archaeology are a rapidly and fast developing field in archaeological prospection [ 60 62 ]. Recent studies have demonstrated machine learning methods for detecting a wide variety of features, including burial mounds, charcoal kilns, buildings, and field systems [ 10 66 ].

The approach considered above differs from these studies in that it seeks to test a specific hypothesis: Are there any further fortresses of the Trelleborg-type in Denmark? Searching for such specific, rare features across the landmass of a whole country presents unique challenges, especially given the paucity of training data and large size of the features. The combined ring detection and classification approach addresses this by searching for anything that fits the morphological characteristics of the fortresses and then classifying these features based on their context.

Our approach detected >2800 features, the majority of which are false positives that need to be manually checked. This is time consuming, and even though we feel it is worthwhile given the scarcity and significance of the features of interest, should be addressed in further work. More stringent noise removal is essential, and could be achieved by careful reprocessing of the raw data. Consensus of classifications between the 2007, 2014, and forthcoming 2019 ALS datasets could mitigate the effects of sensor and platform noise [ 67 68 ], and reduce the influence of artificial textures introduced by vegetation and cultivation. The large number of modern features could be identified and removed by classification of orthophotography. Furthermore, areas where modern development has led to gaps in our ALS dataset can be addressed by using structure from motion techniques to derive DTMs from archival aerial photographic surveys [ 3 69 ], in Denmark such as those with national coverage including the Basic Cover series from 1954 and RAF series from 1965 [ 70 ].

voldsteder (fortifications) and vold (ramparts) in the Danish state heritage database [ A potential criticism of this work is that it could be construed as machine learning for machine learning’s sake. Manually searching for fortresses across the whole landmass of the country would have been untenable, but if we had undertaken a systematic review of the 1395 known(fortifications) and(ramparts) in the Danish state heritage database [ 71 ] using the ALS data, we could have arrived at the same conclusions as this work with much less effort. However, we contend that this is a circular argument. We would not know this if we had not looked for undiscovered fortresses, which could have remained undetected and unidentified given their poor condition, and we should never rely on our existing understanding being complete. This is especially important given the nature of the problem. An indicated absence of further fortresses is just as important for our understanding of the geography of power and dynastic politics in Northern Europe during this period as finding new examples.