Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences

TL;DR

This paper introduces an unsupervised method combining deep embeddings and time series clustering to analyze large regions from satellite image sequences, capturing semantic and temporal land changes without labeled data.

Contribution

The paper presents a novel unsupervised framework for spatio-temporal semantic analysis of satellite images, including a new embedding refinement procedure for better pattern extraction.

Findings

Analyzed a 220 km² region in northern Spain.

Revealed land structures based on climatic, phytological, and hydrological factors.

Provided a comprehensive understanding of land evolution over time.

Abstract

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.