Deep learning-based semantic segmentation in remote sensing

Devis Tuia*, Diego Marcos, Konrad Schindler, Bertrand Le Saux

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

1 Citation (Scopus)


Semantic segmentation consists of the generation of a categorical map, given an image in which each pixel of the image is automatically assigned a class. Deep learning allows the influence of the pixel's context to be learned by capturing the non-linear relationships between surrounding image features at multiple scales, leading to large improvements in performance and opening up the door to new applications. This chapter explores the use of deep learning-based semantic segmentation in Earth observation imagery and presents in detail three approaches specifically aimed at Earth observation applications.

Original languageEnglish
Title of host publicationDeep Learning for the Earth Sciences
Subtitle of host publicationA Comprehensive Approach to Remote Sensing, Climate Science and Geosciences
EditorsGustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein
Number of pages21
ISBN (Electronic)9781119646181
ISBN (Print)9781119646143
Publication statusPublished - 20 Aug 2021


  • Deep learning architectures
  • Image classification
  • Remote sensing
  • Semantic segmentation


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