Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 536777
Title Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest
Author(s) Yokoya, Naoto; Ghamisi, Pedram; Xia, Junshi; Sukhanov, Sergey; Heremans, Roel; Tankoyeu, Ivan; Bechtel, Benjamin; Saux, Bertrand Le; Moser, Gabriele; Tuia, Devis
Source IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 11 (2018)5. - ISSN 1939-1404 - p. 1363 - 1377.
DOI http://dx.doi.org/10.1109/JSTARS.2018.2799698
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2018
Abstract In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data (from OpenStreetMap). The competition, based on separate geographical locations for the training and testing of the proposed solution, aimed at models that were accurate (assessed by accuracy metrics on an undisclosed reference for the test cities), general (assessed by spreading the test cities across the globe), and computationally feasible (assessed by having a test phase of limited time). The techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and of mixed ideas and methodologies deriving from computer vision and machine learning but also deeply rooted in the specificities of remote sensing. In particular, rigorous atmospheric correction, the use of multidate images, and the use of ensemble methods fusing results obtained from different data sources/time instants made the difference.
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