Large image time series analysis for updating vineyard geographic databases

Project Summary

The available geographic databases, particularly in the agricultural landscapes, contain important information at parcel resolution for crop type detection (crop type maps) and monitoring (the RPG database). This database is mostly completed and updated by annual declarations of farmers within the framework of the Common Agricultural Policy (CAP) in Europe. Because of their manual aspect, these declarations may contain errors or inaccuracies and they are not exhaustive. Currently, thanks to the increasing availability of high spatial resolution satellite imagery and their accessibility via European programs, it is becoming possible to use these series of images to update this type of geographic database or to check the consistency of the data through an exploitation of the visual content of the images or of features extracted from the time sequence, more frequently. In particular, satellite image time series (SITS) make it possible to study from 2D+t imaging data the spatiotemporal evolutions of the territory, which may for example indicate a change in management of the cropping system. The objective of this internship is to use deep learning image analysis methods to deliver up-to-dated geographic vineyard databases in a timely and accurate manner over large areas and via an automatic analysis of SITS thanks to spatio-temporal image representations.

Camille Kurtz