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The detection of critical infrastructures in large territories represented byaerial and satellite images is of high importance in several fields such as insecurity, anomaly detection, land use planning and land use change detection.However, the detection of such infrastructures is complex as they have highlyvariable shapes and sizes, i.e., some infrastructures, such as electricalsubstations, are too small while others, such as airports, are too large.Besides, airports can have a surface area either small or too large withcompletely different shapes, which makes its correct detection challenging. Asfar as we know, these limitations have not been tackled yet in previous works.This paper presents (1) a smart Critical Infrastructure dataset, namedCI-dataset, organised into two scales, small and large scales criticalinfrastructures and (2) a two-level resolution-independent criticalinfrastructure detection (DetDSCI) methodology that first determines thespatial resolution of the input image using a classification model, thenanalyses the image using the appropriate detector for that spatial resolution.The present study targets two representative classes, airports and electricalsubstations. Our experiments show that DetDSCI methodology achieves up to37,53% F1 improvement with respect to Faster R-CNN, one of the most influentialdetection models.

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