Case-based reasoning for rare events prediction on strategic sites
This work addresses the challenge of automating rare event prediction in defense monitoring, though it appears incremental as it adapts existing case-based reasoning to this domain.
The researchers tackled the problem of predicting rare events at strategic sites using satellite imagery by proposing a case-based reasoning approach, which significantly outperformed random selection in experiments on submarine movements and rocket launches.
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.