The aim of this presentation is to present a fusion of methods for better object detection in satellite imagery, and apply it to a quantitative analysis of human rights crises. Refugee camps in and around Syria will serve as a case study for this presentation. This research illustrates how traditional remote sensing methods such as, Spectral Angle Analysis (SAM), and machine learning methods, specifically Convolutional Neural Networks, can be used for refugee tent detection. SAM, as a method of tent detection, was used over a time series of images, and achieved an accuracy of 88 percent in the Rubkan refugee camp on the border between Syria and Jordan. To scale this analysis over larger areas of satellite imagery, we will discuss using SAM to generate labeled training data for CNNs and the result of applying them over a large area.

1. Applying the Best of Remote Sensing and Deep Learning
to Refugee Tent Detection
Chloé Hampton and Martha Morrissey
© DigitalGlobe

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3. Outline
● The area of interest
● The problem
● Tools and data used
● Methodology: spectral angle mapping
● Methodology: machine learning
● Results and next steps
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4. ● Informal refugee settlement
on the Syria/Jordan border
● Explosive growth between
2014 and 2018
● Limited aid provided
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Area of Interest: Rukban Refugee Settlement
BBC

5. Introduction to tools: GBDX Notebooks
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6. • Text
• Code
• Output
• Interactive
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Highlights Jupyter Notebooks
NB credit Mike Gleason

7. ● Optical Earth Observing Satellite
● Bands and ground sampling distance:
○ Panchromatic--0.31 meters
○ 8 Multispectral--1.24 meters
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Introduction to Data: WorldView3 Imagery

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WV03 Landsat 8
Area Visualized = 12 km2

9. Remote Sensing Methods
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10. Methodology: Spectral Angle Mapping (SAM)
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11. SAM and Tent Detection
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16. Tent Count Estimates
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17. Machine Learning Methods
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Methods: Remote Sensing + Machine Learning
Training
Data
SAM
efficiently
stores
data
Veda CNN
ML
Model
Binary
Classification
Tent or No Tent

19. Training Data
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607 km2

20. Manual Verification
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21. Neural Network Modeling Basics
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22. CNN Basics
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Hidden Layer for
Feature Learning
Classification Prediction

23. Next Steps
• Model Refinement
・Initial accuracy 2017 data: 93%
・Initial accuracy 2018 data: 75%
• Expand Model
・Spatially
・Temporally
• Apply Remote Sensing + ML to other object detection problems
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24. Challenges
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• Imagery availability
• Processing Capability
• Image Coloring
• Veda in Alpha mode

25. Key Takeaways
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26. You can do this and MORE!
• GBDX Notebooks Account
・https://gbdx.co/gitr18
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27. Questions
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chloe.hampton@digitalglobe.com
martha.morrissey@digitalglobe.com

28. Modeling: CNN Basics II
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