Large-scale semantic classification: outcome of the first year of Inria aerial image labeling benchmark

Abstract : Over the recent years, there has been an increasing interest in large-scale classification of remote sensing images. In this context, the Inria Aerial Image Labeling Benchmark has been released online in December 2016. In this paper, we discuss the outcomes of the first year of the benchmark contest, which consisted in dense labeling of aerial images into building / not building classes, covering areas of five cities not present in the training set. We present four methods with the highest numerical accuracies, all four being convolutional neural network approaches. It is remarkable that three of these methods use the U-net architecture, which has thus proven to become a new standard in image dense labeling.
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Communication dans un congrès
IEEE International Geoscience and Remote Sensing Symposium – IGARSS 2018, Jul 2018, Valencia, Spain
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https://hal.inria.fr/hal-01767807
Contributeur : Yuliya Tarabalka <>
Soumis le : lundi 16 avril 2018 - 15:39:35
Dernière modification le : lundi 11 juin 2018 - 09:46:46

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  • HAL Id : hal-01767807, version 1

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Bohao Huang, Kangkang Lu, Nicolas Audebert, Andrew Khalel, Yuliya Tarabalka, et al.. Large-scale semantic classification: outcome of the first year of Inria aerial image labeling benchmark. IEEE International Geoscience and Remote Sensing Symposium – IGARSS 2018, Jul 2018, Valencia, Spain. 〈hal-01767807〉

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