Final presentation for Ordinance Survey sponsored MSc Project
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Transcript of Final presentation for Ordinance Survey sponsored MSc Project
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 KramerMSc Archaeological Computing (GIS and Survey)External supervisor: David Holland14 December 2015
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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
Aerial survey in Archaeology
4after 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
5Challis 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
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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)
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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
Using AI to imitate the
archaeologist
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
•Geomorphic fingerprint
– Define rules
Human argument: cognitive computing
after van den Eeckhaut et al. (2012)
Process of visual interpretation of archaeological features
11Barceló (2008)
Human experience: machine learning • Artificial Neural
Network
• Some examplesThe 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(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
Case study: barrow detection
using TRIMBLE eCognition
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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
Avebury, Wiltshire• Prehistoric
landscape
• LiDAR data from the Environment Agency
– Slope derivative
• Aerial photography from OS
16
Feature detection1. Defined by rules
2. Template matching
3. Towards automation
•Most attempted feature detection
– Round barrows
Various types of barrows
17
Defined by rules
Three barrow types; (left) Bell (middle) Saucer (right) Bowl Image segmentation into objects with range of brightness
Open test image
Define features
Generate threshold Classify features Review
classification Add threshold
Open verification
imageApply ruleset Evaluate
resultExport
classification
Image segmentation
Definesegmentation
threshold
Iterate process
Iterate process
Iterate process
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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
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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
imageApply ruleset Review
classificationExport
classification
Image segmentation
Definesegmentation
threshold
Iterate process
Iterate process
Open test features
20
Evaluation• Best results
through defined rules
• Most potential for self-learning algorithm
Other saucer bell bowlTrue positive 10 3 14 23False negative 76 7 6 74Percentage p/n 12% 30% 70% 24%
Discussion and future scope
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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
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•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
Ditchpossibly surrounded by Bankpossibly
surrounded by
Flora
Agriculturepossibly(partly)levelled
Fauna
possibly (partly)
destroyed
has landcover
Barrow Earthworkis type of is type of
is type of
Semantic description of a
round barrow
Conclusion
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
Next steps
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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
BibliographyBarceló, 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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