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 ² (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.

中文翻译：

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深度学习和地理分析支持的大范围未知机场检测框架


机场作为重要的经济和军事设施，其空间布局对人们的生活和社会经济产生重大影响。然而，由于更新成本高且信息不及时，现有机场数据库信息不完整、准确率低。由于大范围场景的复杂性，仅使用图像识别进行机场检测的准确率极低。本文提出了一个基于深度学习和地理分析的框架，用于在广泛的研究领域中检测未知的机场分布。首先，我们从现有机场数据库中提取正确的点，并训练基于谷歌图像数据的正负场景分类模型来扫描和提取候选机场区域。接下来，评估机场置信度以提取候选区域中机场的位置。同时利用路网、水系等地理数据对检测结果进行综合分析。对于21 9040.5 km ² （江苏、上海、浙江）研究区，本框架已知机场召回率为96.4%，机场完整率为97.2%。速度比传统视觉搜索快约20倍。通过系统比对，新发现8个机场；然而，缺少一个已建立的数据库机场。结果表明，所提出的框架可以在广泛的区域内以高精度有效检测未知机场，同时扩展深度学习、遥感和地理学的应用。