Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands
by
Wouter B. Verschoof-van der Vaart 1,2,*,†
, Karsten Lambers 1, Wojtek Kowalczyk 3 and Quentin P.J. Bourgeois 1
Wouter B. Verschoof-van der Vaart 1,2,*,†, Karsten Lambers 1, Wojtek Kowalczyk 3 and Quentin P.J. Bourgeois 1
1
Faculty of Archaeology, Leiden University, P.O. Box 9514, 2300 RA Leiden, The Netherlands
2
Data Science Research Programme (Leiden Centre of Data Science), Leiden University, P.O. Box 9505, 2300 RA Leiden, The Netherlands
3
Leiden Institute of Advanced Computer Science, Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands
*
Author to whom correspondence should be addressed.
†
Current address: Faculty of Archaeology, Leiden University, P.O. Box 9514, 2300 RA Leiden, The Netherlands.
ISPRS Int. J. Geo-Inf. 2020, 9(5), 293; https://doi.org/10.3390/ijgi9050293
Received: 30 March 2020 / Revised: 8 April 2020 / Accepted: 22 April 2020 / Published: 1 May 2020
(This article belongs to the Special Issue Machine Learning and Deep Learning in Cultural Heritage)
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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Keywords:
citizen science; Deep Learning; LiDAR; The Netherlands; Faster R-CNN
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MDPI and ACS Style
Verschoof-van der Vaart, W.B.; Lambers, K.; Kowalczyk, W.; Bourgeois, Q.P. Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands. ISPRS Int. J. Geo-Inf. 2020, 9, 293.
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