New DFG project: IdealVGI – Deep Learning with OSM

Recently a new DFG project proposal was accepted to the GIScience Research Group Heidelberg within the DFG priority programme VisVGI (Volunteered Geographic Information: Interpretation, Visualisation and Social Computing” [SPP 1894]). It is joint collaboration project together with Prof. Begüm Demir from TU Berlin.

IDEAL-VGI: Information Discovery from Big Earth Observation Data Archives by Learning from Volunteered Geographic Information

During the last decade, huge amount of remote sensing (RS) images have been acquired, leading to massive Earth Observation (EO) data archives from which mining and retrieving useful information are challenging. Volunteered Geographic Information (VGI) such as OpenStreetMap (OSM) can offer rich geometric and semantic information that goes beyond land use tags, which can be very beneficial for accessing and extracting vital information for observing Earth from big Earth Observation archives. However, user-provided tags within OSM can be noisy, incomplete and redundant.

The IDEAL-VGI project aims address very important scientific and practical problems by focusing on the main challenges of:

1) VGI for land use classification which are: a missing framework to exploit the rich semantic information present at different scales and the uncertainty of OSM derived land use classes.
2) Big EO data, which are: RS image characterization, indexing and search from massive archives.

To this end, we will develop innovative methods, which can significantly improve the state-of-the-art both in the theory and in the tools currently available. In particular, novel methods will be developed, aiming to:

1. identification of the importance, uncertainty and quality of different OSM derived features;
2. enhancing methods for better assessment of quality to promote relevant semantic content of OSM and integration of supporting complementary VGI data streams;
3. developing machine learning/deep learning algorithms in the framework of RS image classification for automatic OSM tag refinement and assignment;
4. developing RS image classification, search and retrieval methods that consider OSM tags with their uncertainty information;
5. improve both OSM semantic land use description as well as remote sensing image classification based on a comparison between the two classification approaches;
6. make full use of VGI to generate accurate annotated data sets and improve accuracy of labelling, which should contribute to more convincing training data sets.

The IDEAL-VGI will contribute to the following research domains indicated in the priority programme:

1) Information Retrieval and Analysis of VGI (machine learning and algorithmic interpretation for VGI and quality assessment and uncertainty analysis of VGI.
2) Active Participation, Social Context and Privacy Awareness (information management and decision analysis based on VGI data.

We hope to start the project soon and are looking forward to the collaboration with colleagues from TU Berlin.

RELATED PUBLICATIONS:

• Chen, J., Y. Zhou, A. Zipf and H. Fan (2018):Deep Learning from Multiple Crowds: A Case Study of Humanitarian Mapping. IEEE Transactions on Geoscience and Remote Sensing (TGRS). 1-10. https://doi.org/10.1109/TGRS.2018.2868748
• Herfort, B., Li, H., Fendrich, S., Lautenbach, S., Zipf, A. (2019): Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning. Remote Sensing 11(15), 1799. https://doi.org/10.3390/rs11151799
• Li, H., Herfort, B., Zipf, A. (2019): Estimating OpenStreetMap Missing Built-up Areas using Pre-trained Deep Neural Networks. Proceedings of the 22nd AGILE Conference on Geographic Information Science, Limassol, Cyprus.
• Raifer, M., Troilo, R., Kowatsch, F., Auer, M., Loos, L., Marx, S., Przybill, K., Fendrich, S., Mocnik, F.-B.& Zipf, A. (2019): OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data.Open Geospatial Data, Software and Standards 2019 4:3. https://doi.org/10.1186/s40965-019-0061-3
• Degrossi L.C., J. Porto de Albuquerque, R. dos Santos Rocha, A. Zipf (2018): A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information. Transactions in GIS (TGIS). Wiley. DOI:10.1111/tgis.12329. 22(2), 542–560.
• Yan, Y., Schultz, M., Zipf, A. (2019): An exploratory analysis of usability of Flickr tags for land use/land cover attribution, Geo-spatial Information Science (GSIS), Taylor & Francis. https://doi.org/10.1080/10095020.2018.1560044
• Barron, C., Neis, P. & Zipf, A. (2013): A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis. , Transactions in GIS, DOI: 10.1111/tgis.12073.
• Mocnik, F.-B., Zipf, A., Raifer, M. (2017): The OpenStreetMap folksonomy and its evolution. Geo-spatial Information Science. DOI: 10.1080/10095020.2017.1368193.
• Ballatore, A. and Zipf, A. (2015): A Conceptual Quality Framework for Volunteered Geographic Information. COSIT – CONFERENCE ON SPATIAL INFORMATION THEORY XII. October 12-16, 2015. Santa Fe, New Mexico, USA. Lecture Notes in Computer Science, pp. 1-20.
• Dorn, H., Törnros, T. & Zipf, A. (2015): Quality Evaluation of VGI using Authoritative Data – A Comparison with Land Use Data in Southern Germany. ISPRS International Journal of Geo-Information. Vol 4(3), pp. 1657-1671, doi: 10.3390/ijgi4031657
• Jokar Arsanjani, J., Mooney, P., Helbich, M., Zipf, A., (2015): An exploration of future patterns of the contributions to OpenStreetMap and development of a Contribution Index, Transactions in GIS, 19(6): 896–914. John Wiley & Sons. DOI: 10.1111/tgis.12139.
• Jokar Arsanjani, J., Helbich, M., Bakillah, M., Hagenauer,J. & Zipf, A. (2013): Toward mapping land-use patterns from volunteered geographic information. International Journal of Geographical Information Science (IJGIS). Taylor & Francis. DOI: 10.1080/13658816.2013.800871.
• Fan, H., Zipf, A., Fu, Q. & Neis, P. (2014): Quality assessment for building footprints data on OpenStreetMap. International Journal of Geographical Information Science (IJGIS). DOI: 10.1080/13658816.2013.867495.