In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the scientific results obtained by the winners of the 2-D contest, which studied either the complementarity of RGB and LiDAR with deep neural networks (winning team) or provided a comprehensive benchmarking evaluation of new classification strategies for extremely high-resolution multimodal data (runner-up team). The data and the previously undis-

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This paper deals with classification of remote sensing data to extract objects for industrial mapping. While land-cover or urban mapping have been extensively studied, industrial cartography remains a field yet to explore, in spite of tremendous needs. We present and compare here four approaches for railway detection in very high resolution images. They use various kind of filtering approaches, including the trained filters of fully convolutional networks. Moreover, they benefit from different a-priori and post-processing techniques to make them more robust. We evaluate all approaches on a challenging dataset captured on an operating station site with complex objects.