  Object detection in remotely sensed satellite pictures is fundamental in many
fields such as biophysical, and environmental monitoring. While deep learning
algorithms are constantly evolving, they have been mostly implemented and
tested on popular ground-based taken photos. This paper critically evaluates
and compares a suite of advanced object detection algorithms customized for the
task of identifying aircraft within satellite imagery. Using the large
HRPlanesV2 dataset, together with a rigorous validation with the GDIT dataset,
this research encompasses an array of methodologies including YOLO versions 5
and 8, Faster RCNN, CenterNet, RetinaNet, RTMDet, and DETR, all trained from
scratch. This exhaustive training and validation study reveal YOLOv5 as the
preeminent model for the specific case of identifying airplanes from remote
sensing data, showcasing high precision and adaptability across diverse imaging
conditions. This research highlight the nuanced performance landscapes of these
algorithms, with YOLOv5 emerging as a robust solution for aerial object
detection, underlining its importance through superior mean average precision,
Recall, and Intersection over Union scores. The findings described here
underscore the fundamental role of algorithm selection aligned with the
specific demands of satellite imagery analysis and extend a comprehensive
framework to evaluate model efficacy. The benchmark toolkit and codes,
available via https://github.com/toelt-llc/FlightScope_Bench, aims to further
exploration and innovation in the realm of remote sensing object detection,
paving the way for improved analytical methodologies in satellite imagery
applications.
