Data Fusion Contest 2017 (DFC2017)

Data Fusion Contest 2017 (DFC2017)

Citation Author(s):
Devis
Tuia
Wageningen University and Research
Moser
Gabriele
University of Genoa
Bertrand
Le Saux
ONERA
Benjamin
Bechtel
Ruhr-University Bochum
Submitted by:
Bertrand Le Saux
Last updated:
Tue, 10/29/2019 - 09:58
DOI:
10.21227/e56j-eh82
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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:

 

 

@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},}

 

 

 

Instructions: 

 

Overview

The 2017 Data Fusion Contest will consist in a classification benchmark. The task to perform is classification of land use (more precisely, Local Climate Zones or LCZ) in various urban environments. Several cities have been selected all over the world to test the ability of both LCZ prediction and domain adaptation. Input data are multi-temporal, multi-source and multi-mode (image and semantic layers). 5 cities are considered for training: Berlin, Hong Kong, Paris, Rome and Sao Paulo.

Content

Each city folder contains:grid/        sampling gridlandsat_8/    Landsat 8 images at various dates (resampled at 100m res., split in selected bands)lcz/        Local Climate Zones as rasters (see below)osm_raster/    Rasters with areas (buildings, land-use, water) derived from OpenStreetMap layersosm_vector/    Vector data with OpenStreetMap zones and linessentinel_2/    Sentinel2 image (resampled at 100m res., split in selected bands)

 

Local Climate Zones

The lcz/ folder contains:`<city>_lcz_GT.tif`: The ground-truth for local climate zones, as a raster. It is single-band, in byte format. The pixel values range from 1 to 17 (maximum number of classes). Unclassified pixels have 0 value.`<city>_lcz_col.tif`: Color, georeferenced LCZ map, for visualization convenience only.Class nembers are the following:10 urban LCZs corresponding to various built types:

  • 1. Compact high-rise;
  • 2. Compact midrise;
  • 3. Compact low-rise;
  • 4. Open high-rise;
  • 5. Open midrise;
  • 6. Open low-rise;
  • 7. Lightweight low-rise;
  • 8. Large low-rise;
  • 9. Sparsely built;
  • 10. Heavy industry.

7 rural LCZs corresponding to various land cover types:

  • 11. Dense trees;
  • 12. Scattered trees;
  • 13. Bush and scrub;
  • 14. Low plants;
  • 15. Bare rock or paved;
  • 16. Bare soil or sand;
  • 17. Water

 

More...

More info:http://www.grss-ieee.org/community/technical-committees/data-fusion/data-fusion-contest/

Discuss:https://www.linkedin.com/groups/IEEE-Geoscience-Remote-Sensing-Society-3678437

 

Acknowledgments

The 2017 IEEE GRSS Data Fusion Contest is organized by the Image Analysis and Data Fusion Technical Committee of IEEE GRSSLandsat 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).The Contest is being organized in collaboration with the WUDAPT (http://www.wudapt.org/) and GeoWIKI (http://geo-wiki.org/) initiatives. The IADF TC chairs would like to thank the organizers and the IEEE GRSS for continuously supporting the annual Data Fusion Contest through funding and resources.

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[1] Devis Tuia, Moser Gabriele, Bertrand Le Saux, Benjamin Bechtel, "Data Fusion Contest 2017 (DFC2017)", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/e56j-eh82. Accessed: Feb. 28, 2020.
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author = {Devis Tuia; Moser Gabriele; Bertrand Le Saux; Benjamin Bechtel },
publisher = {IEEE Dataport},
title = {Data Fusion Contest 2017 (DFC2017)},
year = {2019} }
TY - DATA
T1 - Data Fusion Contest 2017 (DFC2017)
AU - Devis Tuia; Moser Gabriele; Bertrand Le Saux; Benjamin Bechtel
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PB - IEEE Dataport
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Devis Tuia, Moser Gabriele, Bertrand Le Saux, Benjamin Bechtel. (2019). Data Fusion Contest 2017 (DFC2017). IEEE Dataport. http://dx.doi.org/10.21227/e56j-eh82
Devis Tuia, Moser Gabriele, Bertrand Le Saux, Benjamin Bechtel, 2019. Data Fusion Contest 2017 (DFC2017). Available at: http://dx.doi.org/10.21227/e56j-eh82.
Devis Tuia, Moser Gabriele, Bertrand Le Saux, Benjamin Bechtel. (2019). "Data Fusion Contest 2017 (DFC2017)." Web.
1. Devis Tuia, Moser Gabriele, Bertrand Le Saux, Benjamin Bechtel. Data Fusion Contest 2017 (DFC2017) [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/e56j-eh82
Devis Tuia, Moser Gabriele, Bertrand Le Saux, Benjamin Bechtel. "Data Fusion Contest 2017 (DFC2017)." doi: 10.21227/e56j-eh82