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

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•Key concepts for reconstructing stories - Barceló (2008)

•Deduction (argumentation)

•Induction (learned from examples)

•Analogy (information recalled from previous case studies)

Archaeological discovery: incomplete data

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•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)

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Avebury, Wiltshire• Prehistoric

landscape

• LiDAR data from the Environment Agency

– Slope derivative

• Aerial photography from OS

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Feature detection1. Defined by rules

2. Template matching

3. Towards automation

•Most attempted feature detection

– Round barrows

Various types of barrows

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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

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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

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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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