DS07 - Société de l'information et de la communication

Efficient rePresentation TO structure large-scale satellite iMagEs – EPITOME

Submission summary

The continuous proliferation and improvement of satellite sensors yields a huge volume of Earth's images with high spatial and temporal resolution. To efficiently extract the information from these data for real-life applications, it is crucial and urgent to devise new representations for these images. The goal of the EPITOME project is to devise a novel effective representation for large-scale satellite images, that would be generic, i.e., applicable for images from all over the world and for a wide application range, and structure-preserving, i.e. best representing the meaningful objects in the image scene. To address this challenge, we will bridge the gap between advanced machine learning and geometric modeling tools to devise a multi-resolution vector-based representation, together with the methods for its efficient generation and manipulation. Numerous applications will benefit from this new information layer for large-scale image data, such as natural disaster monitoring, urban development planning and autonomous driving.

Project coordination

Yuliya Tarabalka (Centre de Recherche Inria Sophia Antipolis - Méditerranée)

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.


INRIA Sophia Antipolis Centre de Recherche Inria Sophia Antipolis - Méditerranée

Help of the ANR 225,658 euros
Beginning and duration of the scientific project: September 2017 - 48 Months

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