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Navigating the AI minefield:
A step-by-step workflow to apply Deep
Learning to detect Archaeological Sites
An AI minefield?
Interdisciplinary research
• Tutorials don’t work anymore
• Not a benchmark dataset
• Noisy dataset with limited known/labelled examples
• Difficult to generalise approach
• Making the approach robust
• Train on one area test in another
(Google Maps, 2019)
Workflow
1. Find a solvable problem
2. Try the simple approach
3. Start adding complexity
4. Reflect on results
5. Tune
Examples from a case study
on Arran, Scotland
(Google Maps, 2019)
Workflow
1. Find a solvable problem
2. Try the simple approach
3. Start adding complexity
4. Reflect on results
5. Tune
Find a solvable problem
• Consult with domain expert
• What archaeological sites of similar size
are numbersome in your case study area?
Round houses 20x20 meter 203 known
Small cairns 10x10 meter 344 known
Shieling huts 10x10 meter 403 known
Find a solvable problem
• Consult with domain expert
• What is your preferred remote sensor?
(Cowley and López-López, 2017)
© Historic England
Workflow
1. Find a solvable problem
2. Try the simple approach
3. Start adding complexity
4. Reflect on results
5. Tune
Try the simple approach
Image classification
• Image cropped around each object
Try the simple approach
Image classification
• Image cropped around each object
• Set baseline before applying deep learning
• Train a simple network
• Construct a human baseline
Workflow
1. Find a solvable problem
2. Try the simple approach
3. Start adding complexity
4. Reflect on results
5. Tune
Start adding complexity
Image classification
• Deeper network (e.g. ResNet50)
• Data augmentation
• Pretrained network
One at a time
• Get performance boost you expect?
• Visualise results 
Result: 90-92%
Start adding complexity
Object detection
• Use optimised network
• Add object detection (e.g. RetinaNet)
• Expect worse results
• Few foreground to many background objects
Workflow
1. Find a solvable problem
2. Try the simple approach
3. Start adding complexity
4. Reflect on results
5. Tune
Reflect on results
Disappointing results of object detection
• Validation mAP: 0.25
Validation areas Round house Small cairn Shieling
Detections 70 70 66
Average precision 0.50 0.05 0.20
Reflect on results
Disappointing results of object detection
• Validation mAP: 0.25
• Training mAP: 0.54
• TOO LOW!
Validation areas Round house Small cairn Shieling
Detections 70 70 66
Average precision 0.50 0.05 0.20
Training areas Round house Small cairn Shieling
Detections 286 622 514
Average precision 0.73 0.39 0.49
Reflect on results
Disappointing results of object detection
• Training mAP: 0.54
• OOOOH! – new site detections
Training areas Round house Small cairn Shieling
Detections 286 622 514
Average precision 0.73 0.39 0.49
Reflect on results
Include domain knowledge
• Where do false positives occur where they are
not expected?
• 25% in build up areas
• Golf courses, cattle feeders, houses
• 32% of cairn on deep peat
• Peat extraction piles
Workflow
1. Find a solvable problem
2. Try the simple approach
3. Start adding complexity
4. Reflect on results
5. Tune
Tune
Where do false positives occur where
they are not expected?
• 25% in build up areas
• Golf courses, cattle feeders, houses
• 32% of cairn on deep peat
• Peat extraction piles
• Solution: Pre-processing masks based on
land use and peat depth
Conclusion
• Minefield: AI isn’t plug and play
• Develop deep understanding of dataset
• Approach: first simple then complex
• Domain knowledge is key
• Especially at data gathering and post-processing
stages
Thank you.
@ickkramer
i.kramer@soton.ac.uk
https://soton.academia.edu/IrisKramer
CAA 2016
CAA 2016

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Navigating the AI minefield: A step-by-step workflow to apply Deep Learning to detect Archaeological Sites

  • 1. Navigating the AI minefield: A step-by-step workflow to apply Deep Learning to detect Archaeological Sites
  • 2. An AI minefield? Interdisciplinary research • Tutorials don’t work anymore • Not a benchmark dataset • Noisy dataset with limited known/labelled examples • Difficult to generalise approach • Making the approach robust • Train on one area test in another (Google Maps, 2019)
  • 3. Workflow 1. Find a solvable problem 2. Try the simple approach 3. Start adding complexity 4. Reflect on results 5. Tune Examples from a case study on Arran, Scotland (Google Maps, 2019)
  • 4. Workflow 1. Find a solvable problem 2. Try the simple approach 3. Start adding complexity 4. Reflect on results 5. Tune
  • 5. Find a solvable problem • Consult with domain expert • What archaeological sites of similar size are numbersome in your case study area? Round houses 20x20 meter 203 known Small cairns 10x10 meter 344 known Shieling huts 10x10 meter 403 known
  • 6. Find a solvable problem • Consult with domain expert • What is your preferred remote sensor? (Cowley and López-López, 2017) © Historic England
  • 7. Workflow 1. Find a solvable problem 2. Try the simple approach 3. Start adding complexity 4. Reflect on results 5. Tune
  • 8. Try the simple approach Image classification • Image cropped around each object
  • 9. Try the simple approach Image classification • Image cropped around each object • Set baseline before applying deep learning • Train a simple network • Construct a human baseline
  • 10. Workflow 1. Find a solvable problem 2. Try the simple approach 3. Start adding complexity 4. Reflect on results 5. Tune
  • 11. Start adding complexity Image classification • Deeper network (e.g. ResNet50) • Data augmentation • Pretrained network One at a time • Get performance boost you expect? • Visualise results  Result: 90-92%
  • 12. Start adding complexity Object detection • Use optimised network • Add object detection (e.g. RetinaNet) • Expect worse results • Few foreground to many background objects
  • 13. Workflow 1. Find a solvable problem 2. Try the simple approach 3. Start adding complexity 4. Reflect on results 5. Tune
  • 14. Reflect on results Disappointing results of object detection • Validation mAP: 0.25 Validation areas Round house Small cairn Shieling Detections 70 70 66 Average precision 0.50 0.05 0.20
  • 15. Reflect on results Disappointing results of object detection • Validation mAP: 0.25 • Training mAP: 0.54 • TOO LOW! Validation areas Round house Small cairn Shieling Detections 70 70 66 Average precision 0.50 0.05 0.20 Training areas Round house Small cairn Shieling Detections 286 622 514 Average precision 0.73 0.39 0.49
  • 16. Reflect on results Disappointing results of object detection • Training mAP: 0.54 • OOOOH! – new site detections Training areas Round house Small cairn Shieling Detections 286 622 514 Average precision 0.73 0.39 0.49
  • 17. Reflect on results Include domain knowledge • Where do false positives occur where they are not expected? • 25% in build up areas • Golf courses, cattle feeders, houses • 32% of cairn on deep peat • Peat extraction piles
  • 18. Workflow 1. Find a solvable problem 2. Try the simple approach 3. Start adding complexity 4. Reflect on results 5. Tune
  • 19. Tune Where do false positives occur where they are not expected? • 25% in build up areas • Golf courses, cattle feeders, houses • 32% of cairn on deep peat • Peat extraction piles • Solution: Pre-processing masks based on land use and peat depth
  • 20. Conclusion • Minefield: AI isn’t plug and play • Develop deep understanding of dataset • Approach: first simple then complex • Domain knowledge is key • Especially at data gathering and post-processing stages

Editor's Notes

  1. (transfer learning) U-net from medical imagery Segnet (Ronneberger et al., 2015) , (Badrinarayanan et al., 2015) Multi remote sensing (Ghamisi et al., 2017)
  2. (transfer learning) U-net from medical imagery Segnet (Ronneberger et al., 2015) , (Badrinarayanan et al., 2015) Multi remote sensing (Ghamisi et al., 2017)
  3. (transfer learning) U-net from medical imagery Segnet (Ronneberger et al., 2015) , (Badrinarayanan et al., 2015) Multi remote sensing (Ghamisi et al., 2017)
  4. (transfer learning) U-net from medical imagery Segnet (Ronneberger et al., 2015) , (Badrinarayanan et al., 2015) Multi remote sensing (Ghamisi et al., 2017)
  5. (transfer learning) U-net from medical imagery Segnet (Ronneberger et al., 2015) , (Badrinarayanan et al., 2015) Multi remote sensing (Ghamisi et al., 2017)