SCIENCE@home

Multi-nomenclature, multi-resolution joint translation: an application to land-cover mapping

Luc Baudoux,J. Inglada,C. Mallet

Published 2022 in International Journal of Geographical Information Science

ABSTRACT

Abstract Land-use/land-cover (LULC) maps describe the Earth’s surface with discrete classes at a specific spatial resolution. The chosen classes and resolution highly depend on peculiar uses, making it mandatory to develop methods to adapt these characteristics for a large range of applications. Recently, a convolutional neural network (CNN)-based method was introduced to take into account both spatial and geographical context to translate a LULC map into another one. However, this model only works for two maps: one source and one target. Inspired by natural language translation using multiple-language models, this article explores how to translate one LULC map into several targets with distinct nomenclatures and spatial resolutions. We first propose a new data set based on six open access LULC maps to train our CNN-based encoder-decoder framework. We then apply such a framework to convert each of these six maps into each of the others using our Multi-Landcover Translation network (MLCT-Net). Extensive experiments are conducted at a country scale (namely France). The results reveal that our MLCT-Net outperforms its semantic counterparts and gives on par results with mono-LULC models when evaluated on areas similar to those used for training. Furthermore, it outperforms the mono-LULC models when applied to totally new landscapes.

PUBLICATION RECORD

  • Publication year

    2022

  • Venue

    International Journal of Geographical Information Science

  • Publication date

    2022-10-10

  • Fields of study

    Geography, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1080/13658816.2022.2120996
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-91 of 91 references · Page 1 of 1

CITED BY