Repository used for a machine learning class project.
The goal was to experiment with pixel-wise classification of buildings vs non-buildings in aerial imagery (30 cm resolution) from training and test polygons created by Open Street Map (OSM). Some of the code was inspired/modified from https://github.com/machinalis/satimg.
The script can take one training and one test geotiff image. Then use respective 'building' and 'non-building' training/test polygons (shp) to create numpy arrays that can be used by scikit-learn machine learning classifiers kNN, random forest, svm and multilayer perceptron. It is also possible to stretch histograms (not working), create a 3D scatter of the training data and aggregate the pixels of an image after it was predicted by a classifier to get better results.
Some train/test polygons are mislabeled. We still get an accuracy around 70%, which is alright for simple pixel-wise classification with RGB bands only.
The data we used can be dowloaded at: https://www.dropbox.com/s/uklsson5kjv8zjb/data.zip?dl=0
Here is the result of the multilayer perceptron ([16,8,4]) after pixel aggregation:
