An introduction to Machine Learning (ML) Anders U. Waldeland 22.09.19 - Force, Stavanger

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

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

Using ML to make a forest map of Africa

Training data

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

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

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

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

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

Visualizing the feature space

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

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

Training a classifier Dividing the classes in feature space

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

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

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

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

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

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

Results: Test-region Input data Prediction Ground truth

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

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

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

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

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

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

Deep learning for Forest mapping 256 x 256 256 x 256

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 %

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

Forest height map of Tanzania

Forest map of Africa with 10m x 10m resolution

NR-Projects using Deep Learning

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

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

Explaining Deep Learning Models

Explaining Deep Learning Models Age: 0 1 2 3 5 6 7

Mammography screening program

Thank you for your attention!