Framework for Unknown Airport Detection in Broad Areas Supported by Deep Learning and Geographic Analysis

Abstract

Airports serve as important economic and military facilities, and thus, their spatial distribution can strongly impact people's lives and social economy. However, existing airport databases have incomplete information and low accuracy rates owing to the high cost associated with updates and lack of timely information. Due to the complexity of broad-area scenes, the accuracy of airport detection using only image recognition is extremely low. This article proposes a framework for detecting unknown airport distributions in a broad research area based on deep learning and geographic analysis. First, we extracted correct points from an existing airport database, and a positive and negative scene classification model based on Google image data was trained to scan and extract candidate airport regions. Next, the airport confidence was evaluated to extract the positions of airports in the candidate area. Simultaneously, geographical data such as road networks and water systems were used to comprehensively analyze the detection results. For the 21 9040.5 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (Jiangsu, Shanghai, Zhejiang) study area, the recall rate of known airports of this framework was 96.4%, and the airport integrity rate was 97.2%. The speed was approximately 20 times faster than that of traditional visual searches. Through systematic comparison, eight airports were newly discovered; however, one established database airport was missing. The results demonstrate that the proposed framework can validly detect unknown airports with high accuracy in a broad area and concurrently, expand the applications of deep learning, remote sensing, and geography.