CE23 - Intelligence Artificielle

Deep generative models for detecting land cover changes from satellite image times series – DeepChange

Submission summary

Accurate and up-to-date land cover information constitutes key environmental data for developing efficient policies in this era of resource scarcity and climate change. New Satellite Image Times Series offer new opportunities for detecting land cover class transitions. Nevertheless, the challenges of the "Big Data" have become imminent for the exploitation of this massive flow of data. Deep generative models are one of the most promising tools for big data analysis. The use of such models has just started to emerge in the remote sensing. In this project, Generative Adversarial Networks and Variational Autoencoders want to be explored to face common remote sensing challenges, which are the lack of reference data and the exploitation of complex and heterogeneous information. The originality of the project relies on the development of new online change detection methodologies by using generative models, which incorporate the temporal dynamics of the data and physical knowledge constraints

Project coordination

Silvia Valero (Centre d'études spatiales de la biosphère)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


CESBIO Centre d'études spatiales de la biosphère

Help of the ANR 204,445 euros
Beginning and duration of the scientific project: March 2021 - 48 Months

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