Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark
Year: 2018
Type: conference-paper
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.
Institutions: Duke University, National University of Singapore, Université Paris-Saclay, Office National d'Études et de Recherches Aérospatiales, Université de Bretagne Sud, Institut de Recherche en Informatique et Systèmes Aléatoires, Département Traitement de l’Information et Systèmes, Institut national de recherche en sciences et technologies du numérique, TITANE: Modélisation géométrique d'environnements 3D, Communauté d'universités et d'établissements Université Côte d'Azur, Laboratoire d'Informatique Fondamentale et Appliquée de Tours
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2804962028