FineAir: Finest-grained Airplanes in High-resolution Satellite Images

Deep learning models require precisely categorized objects for the detection of small objects in high-resolution satellite images. Coarse-grained objects, such as airplanes, could be easily and accurately annotated. However, determining fine-grained objects, for example Airbus 320, is more challenging. In this respect, transponders are sub-stantial sources. While they transmit data about an air-plane's current state, such as location, arrival, and departure time, they also provide not only fine-grained class, but even Finest-Grained Class (FtGC), for example, A320-200. In this work, we match the collection time of the satel-lite images with transponder data points to generate accu-rately annotated airplanes for Dataset of FINEst-grained AIRplanes in satellite images with 30-cm spatial resolution (FineAir). Our data set addresses the limitations of other popular data sets and annotates airplanes with their FtGC in high-resolution satellite images for the first time. In FineAir, around 1350 instances are annotated with FtGC and various levels of classes are provided for more than 5,500 airplanes in total. Furthermore, we have developed a unique way to split FineAir into non-overlapping sets. Our approach ensures that image data and class distributions are preserved along the sets for sparsely placed objects in high-resolution images. In addition, our empirical experiments show that annotations are substantially more accurate than those of other public datasets for the joint classes. Furthermore, the evaluations of state-of-the-art models for the complete set of 20 fine-grained classes in FineAir demonstrate that our two-step approach, detection followed by classification, predominantly outperforms the corresponding one-step approaches. We release FineAir in satellitepy for academic usages.