1. An introduction to Machine Learning (ML)
Anders U. Waldeland
Force, Stavanger

2. Input data/ Data sample
Machine Learning
Input data/ Data sample
Output/
Prediction
Output
Input
Cat

3. Input data/ Data sample
Machine Learning
Input data/ Data sample
Output/
Prediction
Output
Input
Cat

4. Using ML to make a forest map of Africa

6. Training data

7. Input data/ Data sample
Forest mapping
Input data/ Data sample
Output/
Prediction

8. Classification Regression
Class 1: 0 - 2m
Class 2: 4 - 7m
Class 3: > 10m
Tree hegith
Feature(s)

9. Input data/ Data sample
Traditional ML
Input data/ Data sample
Features/
Attributes
Model/ Classifier
Output/
Prediction

10. Features (attributes)
Raw pixel-values:
Red / Green / Blue
Near Infra-red (NIR)
Vegetation Red-edge
NDVI ( normalized difference vegetation index)
GARI, SLAVI

11. Making a feature plot
NDVI
GARI
RED
GREEN
NDVI
NDVI
RED
Red

12. Visualizing the feature space

13. Visualizing the feature space
PCA (Principal Component Analysis)
TSNE (t-distributed stochastic neighbor embedding)
PCA component 2
PCA component 1

14. Feature-extraction and analysis
Input data/ Data sample
Features/
Attributes
Model/ Classifier
Output/
Prediction

15. Training a classifier
Dividing the classes in feature space

16. Training and testing
Train and test on the same data
Cross-validation

17. Training and testing
Train and test on the same data
Cross-validation

18. Training and testing
Train and test on the same data
Cross-validation

19. Training and testing
Train and test on the same data
Cross-validation

20. Training and testing
Train and test on the same data
Cross-validation

21. Training and testing Train and test on the same data Cross-validation
Divide area in two
Test

22. Results: Test-region
Input data
Prediction
Ground truth

23. Precision True positive
Scoring
Confusion matrix:
Recall _______True positive_____ True positive + false negative
Accuracy:
Train: 62%
Test: 59%
(Random guessing = 33%)
Precision True positive
N positives

24. Different types of classifiers
Simple
Probalisitc classifier
Neural
network
Random forest
Train
60 %
81 %
100%
Test
59 %
63 %
58 %
Bad features 

25. ML ( = using training data)
Deep learning
ML ( = using training data)
Input data/ Data sample
Features/
Attributes
Model/ Classifier
Output/
Prediction

26. ML ( = using training data)
Deep learning
ML ( = using training data)
Input data/ Data sample
Convolutional Neural Network
(Deep Learning)
Output/
Prediction

27. Deep learning for images
Object detection
Deep learning for images
Image classification
«Cat»
Image segmentation

28. Deep learning Great if you have a lot of training data Easy to overfit
Can be unreliable when applied to new data
Hard to explain how it works

29. Deep learning for Forest mapping
256 x 256
256 x 256

30. Probalisitc classifier
Results – 3 classes
Simple
Probalisitc classifier
Neural
network
Random forest
Convolutional
Neural Network
Train
60 %
81 %
100 %
94 %
Test
59 %
63 %
58 %
87 %

31. Results - regression
Input data Ground truth Prediction Prediction vs ground truth

32. Forest height map of Tanzania

33. Forest map of Africa with 10m x 10m resolution

34. NR-Projects using Deep Learning

35. Detection of cultural heritages
Hill shade image constructed from a digital elevation model

36. Detection of cultural heritages
Able to identify, grave mounds, kilns, trapping pits,…

37. Explaining Deep Learning Models

38. Explaining Deep Learning Models
Age:

39. Mammography screening program

40. Thank you for your attention!