CoChat
Sign Up
Cited by 67

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

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

Verschoof-van der Vaart, Wouter B., Lambers, Karsten, Kowalczyk, Wojtek, Bourgeois, Quentin P.J.·2020·ISPRS International Journal of Geo-Information
DOI 10.3390/ijgi9050293
Open this paper in CoChat — free account

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/ijgi9050293

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Recent work on machine learning in archaeological remote sensing has shown that limited labelled training data, class imbalance and the accuracy paradox are common problems when rare archaeological targets are detected within large and complex datasets.

    “Recent work on machine learning in archaeological remote sensing has shown that limited labelled training data, class imbalance and the accuracy paradox are common problems when rare archaeological targets are detected within large and complex datasets40–42.”

    From Multiscale anthropogenic feature detection in the argentinian andes: satellite machine learning, UAV, and pedestrian survey (2026) · cites this paper as [40]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 72

  1. Cowley, 2012, In with the new, out with the old? Auto-extraction for remote sensing archaeology, Proc. SPIE, 8532, 853206, 10.1117/12.981758 · DOI
  2. Bennett, 2014, The data explosion: Tackling the taboo of automatic feature recognition in airborne survey data, Antiquity, 88, 896, 10.1017/S0003598X00050766 · DOI
  3. Bevan, 2015, The data deluge, Antiquity, 89, 1473, 10.15184/aqy.2015.102 · DOI
  4. Devereux, 2005, The potential of airborne lidar for detection of archaeological features under woodland canopies, Antiquity, 79, 648, 10.1017/S0003598X00114589 · DOI
  5. Opitz, R.S., and Cowley, D.C. (2013). An overview of airborne and terrestial laser scanning in archaeology. Interpreting Archaeological Topography: Airborne Laser Scanning, 3D Data and Ground Observation, Oxbow Books. Chapter 2. · DOI
  6. Opitz, 2018, Recent trends and long-standing problems in archaeological remote sensing, J. Comput. Appl. Archaeol., 1, 19
  7. Blaschke, T., Lang, S., and Hay, G. (2008). Geographic object based image analysis (GEOBIA): A new name for a new discipline. Object Based Image Analysis, Springer. · DOI
  8. Lambers, K., Verschoof-van der Vaart, W.B., and Bourgeois, Q.P. (2019). Integrating remote sensing, machine learning, and citizen science in Dutch archaeological prospection. Remote. Sens., 11. · DOI
  9. Soroush, M., Mehrtash, A., Khazraee, E., and Ur, J.A. (2020). Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq. Remote Sens., 12. · DOI
  10. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, The MIT Press.
  11. Guo, 2016, Deep learning for visual understanding: A review, Neurocomputing, 187, 27, 10.1016/j.neucom.2015.09.116 · DOI · Paper page
  12. Fiorucci, 2020, Machine Learning for Cultural Heritage: A Survey, Pattern Recognit. Lett., 133, 102, 10.1016/j.patrec.2020.02.017 · DOI
  13. Ball, 2017, Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community, J. Appl. Remote. Sens., 11, 042609, 10.1117/1.JRS.11.042609 · DOI · Paper page
  14. Razavian, A.S., Azizpour, H., Sullivan, J., and Carlsson, S. (2014, January 23–28). CNN features off-the-shelf: An astounding baseline for recognition. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshop, Columbus, OH, USA. · DOI · Paper page
  15. Zingman, I. (2016). Semi-Automated Detection of Fragmented Rectangular Structures in High Resolution Remote Sensing Images with Application in Archaeology. [Ph.D. Thesis, University of Konstanz]. · DOI
  16. Zingman, 2016, Detection of fragmented rectangular enclosures in very-high-resolution remote sensing images, IEEE Trans. Geosci. Remote. Sens., 54, 4580, 10.1109/TGRS.2016.2545919 · DOI
  17. Gallwey, J., Eyre, M., Tonkins, M., and Coggan, J. (2019). Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning. Remote. Sens., 11. · DOI
  18. Matsumoto, M., and Uleberg, E. (2018). Semi automatic mapping of charcoal kilns from airborne laser scanning data using deep learning. CAA 2016: Oceans of Data, Proceedings of the 44th Conference on Computer Applications and Quantitative Methods in Archaeology, Archaeopress.
  19. Trier, 2019, Using deep neural networks on airborne laser scanning data: Results from a case study of semi-automatic mapping of archaeological topography on Arran, Scotland, Archaeol. Prospect., 26, 165, 10.1002/arp.1731 · DOI
  20. Lambers, 2019, Learning to look at LiDAR: The use of R-CNN in the automated detection of archaeological objects in LiDAR data from the Netherlands, J. Comput. Appl. Archaeol., 2, 31
  21. Brothwell, D., and Pollard, A. (2005). The Role and Practice of Archaeological Prospection. Handbook of Archaeological Sciences, John Wiley & Sons, LTD.
  22. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24–27). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE CVPR, Columbus, OH, USA. · DOI · Paper page
  23. Bourgeois, Q.P.J. (2013). Monuments on the Horizon. The Formation of the Barrow Landscape throughout the 3rd and 2nd Millennium BC, Sidestone Press.
  24. Arnoldussen, 2018, The fields that outlived the Celts: The use-histories of later prehistoric field systems (Celtic Fields or Raatakkers) in The Netherlands, Proc. Prehist. Soc., 84, 303, 10.1017/ppr.2018.5 · DOI
  25. Groenewoudt, 2007, Charcoal Burning and Landscape Dynamics in the Early Medieval Netherlands, Ruralia, 6, 327, 10.1484/M.RURALIA-EB.3.1150 · DOI
  26. Groenewoudt, 2018, Route persistence. Modelling and quantifying historical route-network stability from the Roman period to early-modern times (AD 100–1600): A case study from the Netherlands, Archaeol. Anthropol. Sci., 10, 1037, 10.1007/s12520-016-0431-z · DOI · Paper page
  27. Beex, 2017, The application of LiDAR-based DEMs on WWII conflict sites in the Netherlands, J. Confl. Archaeol., 12, 94, 10.1080/15740773.2017.1440960 · DOI
  28. Lohof, 1994, Tradition and change: Burial practices in the Late Neolithic and Bronze Age in the north-eastern Netherlands, Archaeol. Dialogues, 1, 98, 10.1017/S1380203800000167 · DOI
  29. Louwen, A., and Fontijn, D.R. (2019). Death Revisited. The Excavation of Three Bronze Age Barrows and Surrounding Landscape at Apeldoorn-Wieselseweg, Sidestone Press.
  30. Bourgeois, 2015, The Tempo of Bronze Age Barrow Use: Modeling the Ebb and Flow in Monumental Funerary Landscapes, Radiocarbon, 57, 47, 10.2458/azu_rc.57.17925 · DOI
  31. Kooistra, 2008, The widespread occurrence of Celtic field systems in the central part of the Netherlands, J. Archaeol. Sci., 35, 2318, 10.1016/j.jas.2008.03.007 · DOI
  32. Raab, 2015, Pre-industrial charcoal production in Lower Lusatia (Brandenburg, Germany): Detection and evaluation of a large charcoal-burning field by combining archaeological studies, GIS-based analyses of shaded-relief maps and dendrochronological age determination, Quat. Int., 367, 111, 10.1016/j.quaint.2014.09.041 · DOI
  33. Deforce, 2013, Selective woodland exploitation for charcoal production. A detailed analysis of charcoal kiln remains (ca. 1300–1900 AD) from Zoersel (northern Belgium), J. Archaeol. Sci., 40, 681, 10.1016/j.jas.2012.07.009 · DOI · Paper page
  34. Giligny, F., Djindjian, F., Costa, L., Moscati, P., and Robert, S. (2015). Challenges and perspectives of woodland archaeology across Europe. Concepts, Methods and Tools. Proceedings of the 42nd Annual Conference on Computer Applications and Quantitative Methods in Archaeology, Archaeopress.
  35. Hesse, 2010, LiDAR-derived Local Relief Models—A new tool for archaeological prospection, Archaeol. Prospect., 17, 67, 10.1002/arp.374 · DOI
  36. Kokalj, Ž., and Hesse, R. (2017). Airborne Laser Scanning Raster Data Visualisation: A Guide to Good Practice, Založba ZRC. · DOI
  37. Nyffeler, J. (2018). Kulturlandschaft in neuem Licht: Eine Einführung zu LiDAR in der Archäologie, University of Bamberg Press.
  38. Manning, C.D., Raghavan, P., and Schütze, H. (2009). Introduction to Information Retrieval, Cambridge University Press. · DOI · Paper page
  39. Sadr, 2016, The impact of coder reliability on reconstructing archaeological settlement patterns from satellite imagery: A case study from South Africa, Archaeol. Prospect., 23, 45, 10.1002/arp.1515 · DOI · Paper page
  40. Eitzel, 2017, Citizen science terminology matters: Exploring key terms, Citiz. Sci. Theory Pract., 2, 1, 10.5334/cstp.96 · DOI
  41. 2014, Community Archaeology in the Netherlands, J. Community Archaeol. Heritage, 1, 68
  42. Lyman, 2009, Metric data in archaeology: A study of intra-analyst and inter-analyst variation, Am. Antiq., 74, 485, 10.1017/S0002731600048721 · DOI
  43. Kosmala, 2016, Assessing data quality in citizen science, Front. Ecol. Environ., 14, 551, 10.1002/fee.1436 · DOI
  44. Swanson, 2016, A generalized approach for producing, quantifying, and validating citizen science data from wildlife images, Conserv. Biol., 30, 520, 10.1111/cobi.12695 · DOI
  45. Casana, 2020, Global-Scale Archaeological Prospection using CORONA Satellite Imagery: Automated, Crowd-Sourced, and Expert-led Approaches, J. Field Archaeol., 45, S89, 10.1080/00934690.2020.1713285 · DOI
  46. Freitag, 2016, Strategies Employed by Citizen Science Programs to Increase the Credibility of Their Data, Citiz. Sci. Theory Practice., 1, 2, 10.5334/cstp.6 · DOI
  47. Herfort, 2018, 3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis, ISPRS J. Photogramm. Remote. Sens., 137, 73, 10.1016/j.isprsjprs.2018.01.009 · DOI
  48. Salk, C., Sturn, T., See, L., and Fritz, S. (2016). Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task. Remote. Sens., 8. · DOI
  49. Uijlings, 2013, Selective search for object recognition, Int. J. Comput. Vis., 104, 154, 10.1007/s11263-013-0620-5 · DOI · Paper page
  50. Girshick, R. (2015, January 7–13). Fast R-CNN. Proceedings of the IEEE ICCV, Santiago, Chile. · DOI · Paper page
  51. Tang, T., Zhou, S., Deng, Z., Zou, H., and Lei, L. (2017). Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining. Sensors, 17. · DOI
  52. Mohamed, 2020, Application of transfer learning for object detection on manually collected data, Advances in Intelligent Systems and Computing, Volume 1037, 919, 10.1007/978-3-030-29516-5_69 · DOI
  53. Simonyan, K., and Zisserman, A. (2015, January 7–9). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA.
  54. Lai, S., Lepetit, V., Nishino, K., and Sato, Y. (2017). R-CNN for Small Object Detection. Computer Vision—ACCV 2016. Lecture Notes in Computer Science, vol 10115, Springer. · DOI
  55. Ren, Y., Zhu, C., and Xiao, S. (2018). Small object detection in optical remote sensing images via modified Faster R-CNN. Appl. Sci., 8. · DOI
  56. Breiman, 1996, Bagging Predictors, Mach. Learn., 24, 123, 10.1007/BF00058655 · DOI · Paper page
  57. Efron, B., and Tibshirani, R. (1993). An Introduction to the Bootstrap, Chapman & Hall/CRC. · DOI · Paper page
  58. Gupta, 2017, Geographic Visualization in Archaeology, J. Archaeol. Method Theory, 24, 852, 10.1007/s10816-016-9298-7 · DOI
  59. Gao, L., He, Y., Sun, X., Jia, X., and Zhang, B. (2019). Incorporating negative sample training for ship detection based on deep learning. Sensors, 19. · DOI
  60. Gillings, M., Hacigüzeller, P., and Lock, G. (2020). Predictive spatial modelling. Archaeological Spatial Analysis: A Methodological Guide, Routledge. Chapter 13. · DOI
  61. Casarotto, 2018, Assessing visibility and geomorphological biases in regional field surveys: The case of Roman Aesernia, Geoarchaeology, 33, 177, 10.1002/gea.21627 · DOI
  62. Gerritsen, F. (2003). Local Identities: Landscape and Community in the Late Prehistoric Meuse-Demer-Scheldt Region, Amsterdam University Press. · DOI
  63. Cowley, D.C. (2011). Remote sensing for archaeology and heritage management-site discovery, interpretation and registration. Remote Sensing for Archaeological Heritage Management, Proceedings of the 11th EAC Heritage Management Symposium, Reykjavîk, Iceland, 25–27 March 2010, Europae Archaeologia Consilium. Chapter 4.
  64. Koster, 2009, The “European Aeolian Sand Belt”: Geoconservation of Drift Sand Landscapes, Geoheritage, 1, 93, 10.1007/s12371-009-0007-8 · DOI
  65. Blume, 2004, Plaggen soils: Landscape history, properties, and classification, J. Plant Nutr. Soil Sci., 167, 319, 10.1002/jpln.200420905 · DOI
  66. Russakovsky, 2015, ImageNet Large Scale Visual Recognition Challenge, Int. J. Comput. Vis., 115, 211, 10.1007/s11263-015-0816-y · DOI · Paper page
  67. Sammut, C., and Webb, G.I. (2010). Encyclopaedia of Machine Learning, Springer. · DOI
  68. Zou, 2016, Finding the Best Classification Threshold in Imbalanced Classification, Big Data Res., 5, 2, 10.1016/j.bdr.2015.12.001 · DOI
  69. Nesbakken, 2013, Interpreting cultural remains in airborne laser scanning generated digital terrain models: Effects of size and shape on detection success rates, J. Archaeol. Sci., 40, 4688, 10.1016/j.jas.2013.07.002 · DOI
  70. Torney, 2019, A comparison of deep learning and citizen science techniques for counting wildlife in aerial survey images, Methods Ecol. Evol., 10, 779, 10.1111/2041-210X.13165 · DOI · Paper page
  71. Herfort, B., Li, H., Fendrich, S., Lautenbach, S., and Zipf, A. (2019). Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning. Remote. Sens., 11. · DOI
  72. Traviglia, 2016, Finding Common Ground: Human and Computer Vision in Archaeological Prospection, AARGnews—Newsl. Aerial Archaeol. Res. Group, 53, 11

Cited by

Sorted by citations

Metadata

Authors
Verschoof-van der Vaart, W. B., Lambers, K., Kowalczyk, W., & Bourgeois, Q. P. J.
Venue
ISPRS International Journal of Geo-Information
Publisher
MDPI AG
Published
2020-05-01
Volume
9
Issue
5
Pages
293
ISSN
2220-9964
DOI
10.3390/ijgi9050293
Cited by
67
References
5
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review