As a consequence of the increasing number of multi-temporal and multi-source images, in remote sensing, the need of new concepts and techniques to use the time dimension, is growing rapidly up. The forecoming french satellites SPOT,for the observation of the Earth, will speed up the flow of high-resolution and repetitive data. This paper focuses on the multitemporal segmentation, extraction and analysis of remote sensing images, as a part of geometric reasoning and scene understanding. In the context of an agricultural experiment, the "Lauragais project", the following features are described: - how to individualize entities (parcels of land), on each mono-temporal image : a non-exhaustive multispectral segmentation, based on fuzzy sets approach. - how to give a geometric description of the spatial relations between the segmented entities : a geometric database to access image data on an entity-by-entity basis. - how to compare these geometric descriptions, from date to date, and to give a multitemporal description of the landscape, by mixing all these segmentation results, in a training set, for a new classification scheme.