Case-based reasoning for rare events prediction on strategic sites

TL;DR

This paper introduces a case-based reasoning method for predicting rare events on strategic sites using satellite imagery, effectively incorporating expert knowledge and handling small, irregular datasets, demonstrated on naval and space launch scenarios.

Contribution

The paper presents a novel case-based reasoning approach tailored for rare event prediction in satellite imagery, addressing data irregularities and expert knowledge integration.

Findings

Significantly outperforms random case selection in experiments

Effective on small, irregular datasets from real satellite images

Shows strong potential for defense and space applications

Abstract

Satellite imagery is now widely used in the defense sector for monitoring locations of interest. Although the increasing amount of data enables pattern identification and therefore prediction, carrying this task manually is hardly feasible. We hereby propose a cased-based reasoning approach for automatic prediction of rare events on strategic sites. This method allows direct incorporation of expert knowledge, and is adapted to irregular time series and small-size datasets. Experiments are carried out on two use-cases using real satellite images: the prediction of submarines arrivals and departures from a naval base, and the forecasting of imminent rocket launches on two space bases. The proposed method significantly outperforms a random selection of reference cases on these challenging applications, showing its strong potential.