Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark

Emmanuel Maggiori 1 Yuliya Tarabalka 1 Guillaume Charpiat 2 Pierre Alliez 1
1 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : New challenges in remote sensing impose the necessity of designing pixel classification methods that, once trained on a certain dataset, generalize to other areas of the earth. This may include regions where the appearance of the same type of objects is significantly different. In the literature it is common to use a single image and split it into training and test sets to train a classifier and assess its performance, respectively. However, this does not prove the generalization capabilities to other inputs. In this paper, we propose an aerial image labeling dataset that covers a wide range of urban settlement appearances, from different geographic locations. Moreover, the cities included in the test set are different from those of the training set. We also experiment with convolutional neural networks on our dataset.
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Communication dans un congrès
IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Jul 2017, Fort Worth, United States
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https://hal.inria.fr/hal-01468452
Contributeur : Emmanuel Maggiori <>
Soumis le : lundi 13 mars 2017 - 11:00:54
Dernière modification le : jeudi 15 juin 2017 - 09:09:12
Document(s) archivé(s) le : mercredi 14 juin 2017 - 13:02:36

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  • HAL Id : hal-01468452, version 1

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Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat, Pierre Alliez. Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark. IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Jul 2017, Fort Worth, United States. 〈hal-01468452〉

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