Data Fusion Contest 2017 (DFC2017)

The Contest: Goals and Organization

The 2017 IEEE GRSS Data Fusion Contest, organized by the IEEE GRSS Image Analysis and Data Fusion Technical Committee, aimed at promoting progress on fusion and analysis methodologies for multisource remote sensing data.

The 2017 Data Fusion Contest consisted in a classification benchmark. The task to perform was classification of land use (more precisely, Local Climate Zones, LCZ, Stewart and Oke, 2012) in various urban environments. Several cities have been selected to test the ability of LCZ prediction at generalizing all over the world. Input data are multi-temporal, multi-source and multi-modal (image and semantic layers). Local climate zones are a generic, climate-based typology of urban and natural landscapes, which delivers information on basic physical properties of an area that can be used by land use planners or climate modelers [Bechtel et al., 2015]. LCZ are used as first order discretization of urban areas by the World Urban Database and Access Portal Tools initiative (WUDAPT, http://www.wudapt.org), which aims to collect, store and disseminate data on the form and function of cities around the world.

The Data:

The dataset comprises several city sites. For each city, we provide:

• Landsat data, in the form of images with 8 multispectral bands (i.e. visible, short and long infrared wavelengths) resampled at 100m resolution (courtesy of the U.S. Geological Survey);
• Sentinel2 images, with 9 multispectral bands (i.e. visible, vegetation red edges and short infrared wavelengths) resampled at 100m resolution (Contains modified Copernicus Data 2016); participants are encouraged to use the full resolution data, for which a direct link is provided in the data package.
• Ancillary data: Open Street Map (OSM) layers with land use information: building, natural, roads and land-use areas (Data © OpenStreetMap contributors, available under the Open Database Licence – http://www.openstreetmap.org/copyright). We also provide rasterized versions of OSM layers at 20m resolution for building and land-use areas, superimposable with the satellite images.
• Moreover, for the training cities only, we also provide ground-truth of the various LCZ classes on several areas of the city (defined as polygons using the class codes above). They are provided as raster layers at 100m resolution, superimposable to the satellite images. The ground-truth for the test set will remain undisclosed and will be used for evaluation of the results.

Evaluation

Results can be evaluated on the IEEE GRSS Data and Algorithm Standard Evaluation website: http://dase.grss-ieee.org/

Contest Terms and conditions

The data are provided only for the purpose of participation in the 2017 Data Fusion Contest. Participants acknowledge that they have read and agree to the following Contest Terms and Conditions:

• The owner of the data and of the copyright on the data are the respective providers: U.S. Geological Surver (https://www.usgs.gov/), OpenStreetMap contributors (http://www.openstreetmap.org/copyright), the European Space Agency (https://sentinel.esa.int).
• Local Climate Zone maps are provided by the WUDAPT (http://www.wudapt.org/) and GeoWIKI (http://geo-wiki.org/) initiatives
• Any dissemination or distribution of the data packages by any registered user is strictly forbidden.
• The data can be used in scientific publications subject to approval by the IEEE GRSS Image Analysis and Data Fusion Technical Committee and by the WUDAPT on a case-by-case basis. To submit a scientific publication for approval, the publication shall be sent as an attachment to an e-mail addressed to iadf_chairs@grss-ieee.org and benjamin.bechtel@uni-hamburg.de.
• In any scientific publication using the data, the data shall be identified as “grss_dfc_2017” and shall be referenced as follows: “[REF. NO.] 2017 IEEE GRSS Data Fusion Contest.
• Any scientific publication using the data shall include a section “Acknowledgement”. This section shall include the following sentence: “The authors would like to thank the WUDAPT(​ http://www.wudapt.org/​ ) and GeoWIKI (​ http://geo-wiki.org/​ ) initiatives for providing the data packages used in this study, the DASE benchmarking platform ( http://dase.ticinumaerospace.com/ ), and the IEEE GRSS Image Analysis and Data Fusion Technical Committee. Landsat 8 data available from the U.S. Geological Survey (https://www.usgs.gov/). OpenStreetMap Data © OpenStreetMap contributors, available under the Open Database Licence – http://www.openstreetmap.org/copyright. Original Copernicus Sentinel Data 2016 available from the European Space Agency (https://sentinel.esa.int).”
• Any scientific publication using the data shall include a citation to the JSTARS article reporting the outcome of the contest:

@article{iadf-18jstars-dfc17,
 author = {Yokoya, Naoto and Ghamisi, Pedram and Xia, Junxi and Sukhanov, Sergei and Heremans, Roel and Debes, Christian and Bechtel, Benjamin  and {Le Saux}, Bertrand and Moser, Gabriele and Tuia, Devis},
 title = {Open data for global multimodal land use classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest},
 journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
 year={2018},
volume={11},
number={5},
pages={1363-1377},
keywords={geophysical image processing;image classification;image fusion;land use;learning (artificial intelligence);remote sensing;terrain mapping;AD 2017;Landsat 8;OpenStreetMap;Sentinel-2;computer vision;data fusion contest;ensemble methods;geographical locations;global multimodal land use classification;local climate zones classification;machine learning;multidate images;remote sensing;Artificial satellites;Data integration;Earth;Image resolution;Remote sensing;Training;Urban areas;Convolutional neural networks (CNNs);OpenStreetMap (OSM);crowdsourcing;deep learning (DL);ensemble learning;image analysis and data fusion (IADF);multimodal;multiresolution;multisource;random fields},
keywords = {mine},
doi={10.1109/JSTARS.2018.2799698},
ISSN={1939-1404},
month={May},
}