Deep learning models to count buildings in high-resolution overhead images

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1 Citation (Scopus)

Abstract

This paper addresses the problem of counting buildings in very high-resolution overhead true color imagery. We study and discuss the relevance of deep-learning based methods to this task. Two architectures and two loss functions are proposed and compared. We show that a model enforcing equivariance to rotations is beneficial for the task of counting in remotely sensed images. We also highlight the importance of robustness to outliers of the loss function when considering remote sensing applications.

Original languageEnglish
Title of host publication2019 Joint Urban Remote Sensing Event, JURSE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728100098, 9781728100081
ISBN (Print)9781728100104
DOIs
Publication statusPublished - 22 Aug 2019
Event2019 Joint Urban Remote Sensing Event, JURSE 2019 - Vannes, France
Duration: 22 May 201924 May 2019

Publication series

NameJoint Urban Remote Sensing Event (JURSE)
PublisherIEEE
ISSN (Print)2334-0932
ISSN (Electronic)2642-9535

Conference

Conference2019 Joint Urban Remote Sensing Event, JURSE 2019
CountryFrance
CityVannes
Period22/05/1924/05/19

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Keywords

  • counting
  • Deep learning
  • equivariance
  • loss functions
  • regression
  • remote sensing

Cite this

Lobry, S., & Tuia, D. (2019). Deep learning models to count buildings in high-resolution overhead images. In 2019 Joint Urban Remote Sensing Event, JURSE 2019 [8809058] (Joint Urban Remote Sensing Event (JURSE)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/JURSE.2019.8809058