OSM buildings noisy labels dataset

This dataset contains tile imagery from the OpenStreetMap project alongside label masks for buildings from OpenStreetMap.  Besides the original clean label set, additional noisy label sets for random noise, removed and added buildings are provided.

The purpose of this dataset is to provide training data for analysing the impact of noisy labels on the performance of models for semantic segmentation in Earth observation.

The code for downloading and creating the datasets as well as for performing some preliminary analyses is also provided, however it is necessary to have access to a tile server where OpenStreetMap tiles can be downloaded in sufficient amounts.

To reproduce the dataset and perform analysis on it, do the following:

unzip data.zip and code.zip

create the folder structure from data

Build and activate a python environment from environment.yml

Insert the url of a suitable tile server for OSM tiles in line 76 of utils.py

Execute download_OSM_dataset.py to download OSM image tiles alongside OSM labels

Execute create_noisy_labels.py for the OSM dataset to create noisy label sets

Divide the images and labels into train and test data. split_data.py can be used as a baseline for this, but pathnames have to be adjusted and the corresponding directories have to be created first.

Call train_model.py to train a model on the data. Specify the data size and the label set by giving command line arguments as shown in train_model.sh