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Article Dans Une Revue International Journal of Geographical Information Science Année : 2023

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

Résumé

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.
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hal-03808724 , version 1 (31-03-2023)

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Luc Baudoux, Jordi Inglada, Clément Mallet. Multi-nomenclature, multi-resolution joint translation: an application to land-cover mapping. International Journal of Geographical Information Science, 2023, 37 (2), pp.403-437. ⟨10.1080/13658816.2022.2120996⟩. ⟨hal-03808724⟩
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