Validation of Global Airport Spatial Locations From Open Databases Using Deep Learning for Runway Detection

Airports are important transportation hubs, but their locations searched on open databases are not reliable. Manual verification of these locations is time-consuming and labor-intensive, so that a rapid and automated validation of their spatial location is necessary. In this study, three datasets of global airports were collected and fused into one dataset through coordinate and name matching. The fused dataset contained 46 290 airport (with runway) records. Then, we downloaded the remote sensing images of these airports from Google Earth. To determine whether there were airports in these images, we proposed a process framework. In this framework, we used a two-scale runway detector based on YOLOv3 to initially detect the airport runway, then used a re-classifier based on ResNet-101 to improve the accuracy of the initial detection results and gave a comprehensive result score. The precision of this process framework and the airport recall rate on the test dataset reached 95.4% and 95.6%, respectively. The framework was applied to airport locations around the world. When the threshold of the result score was set to 0.65, 29 259 airport records passed the verification. In addition, we manually verified the application results. The accuracy of the process framework reached 91%, while its speed was 15 times faster than that of the manual verification. The results showed that the entire process framework can quickly and reliably help verify the spatial locations of airports worldwide and provide processing ideas for the validation of the spatial locations of other remote sensing objects.