CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study

TL;DR

This paper introduces a new dataset and a two-level detection methodology for identifying critical infrastructures like airports and substations in satellite images, addressing size variability challenges.

Contribution

The paper presents the CI-dataset and DetDSCI methodology, enabling resolution-independent detection of infrastructures with improved accuracy over existing models.

Findings

DetDSCI achieves up to 37.53% F1 score improvement over Faster R-CNN.

The dataset includes small and large scale critical infrastructures.

The methodology effectively handles size and shape variability in satellite image detection.

Abstract

The detection of critical infrastructures in large territories represented by aerial and satellite images is of high importance in several fields such as in security, anomaly detection, land use planning and land use change detection. However, the detection of such infrastructures is complex as they have highly variable shapes and sizes, i.e., some infrastructures, such as electrical substations, are too small while others, such as airports, are too large. Besides, airports can have a surface area either small or too large with completely different shapes, which makes its correct detection challenging. As far as we know, these limitations have not been tackled yet in previous works. This paper presents (1) a smart Critical Infrastructure dataset, named CI-dataset, organised into two scales, small and large scales critical infrastructures and (2) a two-level resolution-independent critical infrastructure detection (DetDSCI) methodology that first determines the spatial resolution of the input image using a classification model, then analyses the image using the appropriate detector for that spatial resolution. The present study targets two representative classes, airports and electrical substations. Our experiments show that DetDSCI methodology achieves up to 37,53% F1 improvement with respect to Faster R-CNN, one of the most influential detection models.