Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark

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...

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Bibliographic Details
Published inIEEE International Geoscience and Remote Sensing Symposium proceedings pp. 3226 - 3229
Main Authors Maggiori, Emmanuel, Tarabalka, Yuliya, Charpiat, Guillaume, Alliez, Pierre
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Benchmark testing
Buildings
classification benchmark
convolutional neural networks
deep learning
High-resolution images
Labeling
Roads
Semantics
Training
Urban areas
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Summary: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.
ISSN:2153-7003
DOI:10.1109/IGARSS.2017.8127684