  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.
