MSc Archaeological Computing (GIS and Survey), University of Southampton. “An archaeological reaction to the remote sensing data explosion. Reviewing the research on semi-automated pattern recognition and assessing the potential to integrate artificial intelligence”

1. An archaeological reaction to the
remote sensing data explosion.
Reviewing the research on semi-automated
pattern recognition and assessing the potential
to integrate artificial intelligence.
Iris Kramer
MSc Archaeological Computing (GIS and Survey)
External supervisor: David Holland
14 December 2015

2. 2
Introduction
• Aerial survey in Archaeology
• Using AI to imitate the archaeologist
• Case study: barrow detection using TRIMBLE eCognition
• Discussion and future scope
• Conclusion
• Next steps

3. Aerial survey in
Archaeology

4. 4
after Lasaponara and Masini (2012)
Aerial photography
• First features recorded at large scale by O.G.S. Crawford
– From 1920’s
Possible cause to the
presence of crop marks

5. 5
Challis et al. (2011)
Light Detection And Ranging
• First demonstrated in a collaboration of the UK
Environment Agency and English Heritage around 2000
• Revolutionary for forested areas since 2006
Interaction of laser pulse
with forest canopy resulting
multi returns over increasing
time

6. 6
Automated methods
• Shape detection
– e.g. lines, corners, circles
• Template matching
Rectangularity heath
map derived from
Hough transform line
detections
after Zingman et al. (2015)
(a) The ground plan
and cross-section
geometry of a
charcoal kiln site.
(b) LiDAR derivatives for
template matching
Schneider et al. (2015)

7. 7
Reacting to the data explosion
• “…there will never be any automated mapping for
archaeology…” – Parcak 2009
• “…focus should be on predictable shapes and sizes as these
work best within the presented template matching and
shape detection algorithms…” – Bennett et al. 2014
• Limited research

8. Using AI to imitate
the archaeologist

9. 9
• Key concepts for reconstructing stories - Barceló (2008)
• Deduction (argumentation)
• Induction (learned from examples)
• Analogy (information recalled from previous case studies)
Archaeological discovery: incomplete data

10. 10
• Geomorphic fingerprint
– Define rules
Human argument: cognitive computing
after van den Eeckhaut et al. (2012)
Process of visual
interpretation of
archaeological features

11. 11
Barceló (2008)
Human experience: machine learning
• Artificial Neural Network
• Some examples The basic, three-layer
neural network topology,
with a hidden layer
A neural network to recognize
visual textures as use-wear
patterns in lithic tools

12. 12
(top) Barceló 2008, (bottom) Krizhevsky et al. 2012
Human experience: machine learning
• Artificial Neural Network
• ImageNet contest 2012
– Deep convolutional neural network
The CNN architecture,
explicitly showing the
delineation of tasks
between two GPUs.
The basic, three-layer
neural network topology,
with a hidden layer
Results of test images
and labels found most
probable by the model

13. Case study: barrow
detection using
TRIMBLE eCognition

14. 14
Reinvention of eCognition
• Not useful for archaeology?
• Very useful for landslide detection!
de Laet et al. (2007)
Result of classifying
shadows of walls
Overview of processing
steps for the Random
Forest algorithm
Stumpf and Kerle (2011)

15. 15
Avebury, Wiltshire
• Prehistoric landscape
• LiDAR data from the
Environment Agency
– Slope derivative
• Aerial photography from OS

16. 16
Feature detection
1. Defined by rules
2. Template matching
3. Towards automation
•Most attempted feature detection
– Round barrows
Various types of
barrows

17. 17
Defined by rules
Three barrow types; (left) Bell (middle) Saucer (right) BowlImage segmentation into objects with range of brightness
Open test
image
Define
features
Generate
threshold
Classify features
Review
classification
Add threshold
Open
verification
image
Apply ruleset
Evaluate
result
Export
classification
Image
segmentation
Define
segmentation
threshold
Iterate process
Iterate process
Iterate process

18. 18
Template matching
Barrow classification based on correspondence thresholdFive template barrows created from training locations
Open test
image
Sample
selection
Generate
template
Test template
Define
threshold
Review
targets
Update
template
Open
verification
image
Create
correlation map
Evaluate
correlation
Execute
classification
Iterate process
Export
classification
Iterate process
Iterate process

19. 19
Towards automation
Image segmentation into objects trained on
brightness
Open test
image
Assign class to
test features
Train RF
classifier
Apply RF
classifier
Open
verification
image
Apply ruleset
Review
classification
Export
classification
Image
segmentation
Define
segmentation
threshold
Iterate process
Iterate process
Open test
features

20. 20
Evaluation
• Best results through
defined rules
• Most potential for
self-learning algorithm
Other saucer bell bowl
True positive 10 3 14 23
False negative 76 7 6 74
Percentage p/n 12% 30% 70% 24%

21. Discussion and
future scope

22. 22
AI in reaction to the data explosion
• Ever increasing data from various sources
– “Is satellite technology advancing faster than
archaeologists’ ability to learn, apply, and analyse the
data and programs, and all the inherent implications?”
- Parcak (2009)
– Limited research in overall methods
• Heritage monitoring
• Small scale

23. 23
• Consistency in large mapping programmes
– Exchange of common feature detection
• (e.g. ditch, mound)
– Web-based data repository
Future scope
Round barrow
Mound
Round
has shape
is defined by
…
(varied sizes)
has size
Ditch
possibly
surrounded by
Bank
possibly
surrounded by
Flora
Agriculture
possibly
(partly)
levelled
Fauna
possibly
(partly)
destroyed
has landcover
Barrow Earthworkis type of is type of
is type of
Semantic description
of a round barrow

24. Conclusion

25. 25
Research in automated feature recognition
• Limited in-depth research
– Short
– On-the-side
– No knowledge exchange
– Settled for less
• Lot of potential
– Emerging research in Geosciences and Computer Vision
– Reaction to hazards, long term changes, building projects

26. Next steps

27. 27
PhD in machine learning?
• Creation of a reference database such as ImageNet
– 14,197,122 images?
– Connecting objects to words
• Application for archaeology
– Connecting parts of features to words (e.g. ditch, mound)
– Deep learning
• Multi-scalar
• Parts of features related to types

28. Bibliography
Barceló, J. A. 2008. Computational Intelligence in Archaeology, Hershey, New York, IGI.
Bennett, R., Cowley, D., and De Laet, V. 2014. The data explosion: tackling the taboo of automatic feature recognition in airborne survey
data. Antiquity, 88, 896-905.
van den Eeckhaut, M., Kerle, N., Poesen, J., and Herv‡s, J. 2012. Identification of vegetated landslides using only a Lidar-based terrain
model and derivatives in an object-oriented environment. Proceedings of the 4th GEOBIA, 211.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. Imagenet classification with deep convolutional neural networks. Advances in neural
information processing systems, 2012. 1097-1105.
Lasaponara, R., and Masini, N. 2012. Image Enhancement, Feature Extraction and Geospatial Analysis in an Archaeological
Perspective. In: Lasaponara, R., and Masini, N. (eds.) Satellite Remote Sensing: a New Tool for Archaeology. New York: Springer.
de Laet, V., Paulissen, E., and Waelkens, M. 2007. Methods for the extraction of archaeological features from very high-resolution
Ikonos-2 remote sensing imagery, Hisar (southwest Turkey). Journal of Archaeological Science, 34, 830-841.
Niemeyer, I., Marpu, P. R., and Nussbaum, S. 2008. Change detection using object features. In: Blaschke, T., Lang, S., and Hay, G. J.
(eds.) Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Verlag: Springer.
Parcak, S. 2009. Satellite Remote Sensing for Archaeology, New York, Taylor & Francis.
Schneider, A., Takla, M., Nicolay, A., Raab, A., and Raab, T. 2015. A Template-matching Approach Combining Morphometric Variables
for Automated Mapping of Charcoal Kiln Sites. Archaeological Prospection, 22, 45-62.
Stumpf, A., and Kerle, N. 2011. Object-oriented mapping of landslides using Random Forests. Remote Sensing of Environment, 115,
2564-2577.
Zingman, I., Saupe, D., and Lambers, K. 2015. Detection of incomplete rectangular contours with application in archaeology. Technical
Report, University of Konstanz.
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