A benchmark dataset for deep learning-based airplane detection: HRPlanes

Abstract

Airplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide reliable and accurate solutions for automatic detection of airplanes; however, huge amount of training data is required to obtain promising results. In this study, we create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) and labeling the bounding box of each plane on the images. HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites. We evaluated our dataset with two widely used object detection methods namely YOLOv4 and Faster R-CNN. Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications. Moreover, proposed architectures and results of this study could be used for transfer learning of different datasets and models for airplane detection.

A Multiple Geospatial Approach for Intangible Cultural Heritage Tourism Potentiality Mapping in Iran

“…), standardization is a critical step for any MCDA implementation. Thus, the standardization process leads all heterogeneous evaluation scales to be standardized on the same scale for criterion evaluation and aggregation [51][52][53][54]. Essentially, this is a critical step in implementing GIS-MCDA, which leads to unifying the heterogeneous evaluation scales of the selected criteria.…”