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.

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 multi-scale runway detector based on YOLO-v3 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 accuracy 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, 29259 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.

Road traffic is occasionally blocked by landslide geological disasters in remote mountainous areas, causing obstruction to economic society and national defense construction. It is vital to conduct landslide geological disaster risk assessment and vulnerability research on the road network. Based on landslide geological disaster risk on the road network, this study analyzed the potential effects of the main environmental elements. Due to the lack of previous research works, this study proposed an effective, rational, and understandable multicriteria heuristic analytical hierarchy process model, fuzzy comprehensive evaluation, and frequency ratio-interactive fuzzy stack analysis for vulnerability assessment of road networks in large and complex networks. Based on the comprehensive use of geographic information technology, the road network vulnerability of Tibet in China was evaluated by introducing slope, topographic relief, normalized difference vegetation index (NDVI), annual mean precipitation, distance from river drainage, glaciers and snow, habitation, seismic center and geological fault zone, and soil erosion intensity. According to the findings of the study, the three-stage framework proposed in this study can provide correct inferences and explanations for the potential phenomena of landslide geological disasters; the geological disaster risk are unevenly distributed in the study area; the distribution of the road network vulnerability in China’s Tibet significantly differs among different cities; the high-vulnerability section presents significant regional characteristics, which overlap with the area with a high risk of landslide geological disasters, and its distribution is mostly located in traffic arteries, link aggregations, and relatively frequent human activity.

As an important transportation facility, airports substantially affect the economic lives of people. However, the full extraction of airports located in a vast area is concerning. The size of an airport in a previous wide area detection framework is relatively large and has a strong saliency in remote sensing images, whereas the contradiction between a complex geographical background and a small airport size has yet to be resolved. In this study, we propose a set of automatic detection frameworks to realize efficient detection for various airports in nine Indian states/union territories under the condition that only runway samples are labeled. A preliminary extraction of runway features is performed with a high F1 and recall rate, and teacher nodes judge and guide the results. Next, the output is connected to classification and segmentation for outlier elimination and pixel extraction to locate the runways. For the study area, the proposed framework airport retention rate (RR) is 92.7%, with the false alarm reduction rate(FARR) reduced by a maximum of 95.3%. A total of 192 airports are discovered, and the effective airport growth rate(GR) is 47.4%. Compared to previous work, RR, GR, FARR, and run efficiency increased by 2.2%, 16.0%, 4.5%, and 432.5%, respectively, with more small- and medium-sized airports detected. Furthermore, the framework is tested in Japan, and 155 airports are detected. Thus, the proposed framework effectively improves detection capability for small- and medium-sized airports in large-scale areas and updates the airport database.