Temporal sequences of satellite images constitute a highly valuable and abundant
resource to analyze a given region. However, the labeled data needed to train most
machine learning models are scarce and difficult to obtain. In this context, we
investigate a fully unsupervised methodology that, given a sequence of images,
learns a semantic embedding and then, creates a partition of the ground according
to its semantic properties and its evolution over time. We illustrate the
methodology by conducting the semantic analysis of a sequence of satellite
images of a region of Navarre (Spain). The proposed approach reveals a novel
broad perspective of the land, where potentially large areas that share both a
similar semantic and a similar temporal evolution are connected in a compact and
well-structured manner. The results also show a close relationship between the
allocation of the clusters in the geographic space and their allocation in the
embedded spaces. The semantic analysis is completed by obtaining the
representative sequence of tiles corresponding to each cluster, the linear
interpolation between related areas, and a graph that shows the relationships
between the clusters, providing a concise semantic summary of the whole region.
03/03/2023
The seminar is given by Carlos Echegoyen Arruiti from the Spatial Statistics Group, Public University of Navarre at the following zoom link https://uniroma1.zoom.us/j/86881977368?pwd=SWRFcVFjMDZTa0lXZk05TE1zNm5adz09