Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences
This work addresses the challenge of large-scale semantic analysis of satellite imagery for applications like environmental monitoring, but it is incremental as it builds on existing unsupervised and clustering techniques.
The authors tackled the problem of automatically analyzing large regions from satellite image sequences without labeled data, presenting an unsupervised method that combines deep embeddings and time series clustering to capture semantic properties and evolution over time, resulting in a comprehensive understanding of a 220 km² region in northern Spain with areas connected based on climatic, phytological, and hydrological factors.
Temporal sequences of satellite images constitute a highly valuable and abundant resource for analyzing regions of interest. However, the automatic acquisition of knowledge on a large scale is a challenging task due to different factors such as the lack of precise labeled data, the definition and variability of the terrain entities, or the inherent complexity of the images and their fusion. In this context, we present a fully unsupervised and general methodology to conduct spatio-temporal taxonomies of large regions from sequences of satellite images. Our approach relies on a combination of deep embeddings and time series clustering to capture the semantic properties of the ground and its evolution over time, providing a comprehensive understanding of the region of interest. The proposed method is enhanced by a novel procedure specifically devised to refine the embedding and exploit the underlying spatio-temporal patterns. We use this methodology to conduct an in-depth analysis of a 220 km$^2$ region in northern Spain in different settings. The results provide a broad and intuitive perspective of the land where large areas are connected in a compact and well-structured manner, mainly based on climatic, phytological, and hydrological factors.