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

@article{Maggiori2017CanSL,
  title={Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark},
  author={Emmanuel Maggiori and Yuliya Tarabalka and Guillaume Charpiat and Pierre Alliez},
  journal={2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  year={2017},
  pages={3226-3229}
}
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… CONTINUE READING
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