{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:06:28Z","timestamp":1760609188224,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T00:00:00Z","timestamp":1568937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan","award":["2017YFB0504205"],"award-info":[{"award-number":["2017YFB0504205"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["41622109","41371017"],"award-info":[{"award-number":["41622109","41371017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Airports have a profound impact on our lives, and uncovering their distribution around the world has great significance for research and development. However, existing airport databases are incomplete and have a high cost of updating. Thus, a fast and automatic worldwide airport detection method can be of significance for global airport detection at regular intervals. However, previous airport detection studies are usually based on single remote sensing (RS) imagery, which seems an overwhelming burden for worldwide airport detection with traversal searching. Thus, we propose a hierarchical airport detection method consisting of broad-scale extraction of worldwide candidate airport regions based on spatial analysis of released RS products, including impervious surfaces from FROM-GLC10 (fine resolution observation and monitoring of global land cover 10) product, building distribution from OSMs (open street maps) and digital surface model from AW3D30 (ALOS World 3D\u201430 m). Moreover, narrow-scale aircraft detection was initially conducted by the Faster R-CNN (regional-convolutional neural networks) deep learning method. To avoid overestimation of background regions by Faster R-CNN, a second CNN classifier is used to refine the class labeling with negative samples. Specifically, our research focuses on target airports with at least 2 km length in three experimental regions. Results show that spatial analysis reduced the possible regions to 0.56% of the total area of 75,691 km2. The initial aircraft detection by Faster R-CNN had a mean user\u2019s accuracy of 88.90% and ensured that all the aircrafts could be detected. Then, by introducing the CNN reclassifier, the user\u2019s accuracy of aircraft detection was significantly increased to 94.21%. Finally, through an experienced threshold of aircraft number, 19 of the total 20 airports were detected correctly. Our results reveal the overall workflow is reliable for automatic and rapid airport detection around the world with the help of released RS products. This research promotes the application and progression of deep learning.<\/jats:p>","DOI":"10.3390\/rs11192204","type":"journal-article","created":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T03:26:32Z","timestamp":1569209192000},"page":"2204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8520-9764","authenticated-orcid":false,"given":"Fanxuan","family":"Zeng","sequence":"first","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"},{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"}]},{"given":"Liang","family":"Cheng","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"},{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"},{"name":"Collaborative Innovation Center for the South Sea Studies, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization, 163 Xianlin Road, Nanjing University, Nanjing 210023, China"}]},{"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"},{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"}]},{"given":"Nan","family":"Xia","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"},{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"}]},{"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"},{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"}]},{"given":"Xiao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"},{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"}]},{"given":"Manchun","family":"Li","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"},{"name":"School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,20]]},"reference":[{"key":"ref_1","unstructured":"Liu, D., He, L., and Carin, L. (2004, January 17\u201321). Airport Detection in Large Aerial Optical Imagery. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Montreal, QC,Canada."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Pan, L. (2016). Automatic Airport Recognition Based on Saliency Detection and Semantic Information. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5070115"},{"key":"ref_3","first-page":"264","article-title":"Airline network development in europe and its implications for airport planning","volume":"8","year":"2008","journal-title":"Eur. J. Transp. Infrast."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bajardi, P., Poletto, C., Ramasco, J.J., Tizzoni, M., Colizza, V., and Vespignani, A. (2011). Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0016591"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2013.08.001","article-title":"Object detection in remote sensing imagery using a discriminatively trained mixture model","volume":"85","author":"Cheng","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., and Van Gool, L. (2018). Domain Adaptive Faster R-CNN for Object Detection in the Wild. 2018 IEEE\/Cvf Conf. Comput. Vis. Pattern Recognit., 3339\u20133348.","DOI":"10.1109\/CVPR.2018.00352"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2016.2565706","article-title":"Efficient Airport Detection Using Line Segment Detector and Fisher Vector Representation","volume":"13","author":"Budak","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1109\/LGRS.2018.2792421","article-title":"Airport detection in large-scale sar images via line segment grouping and saliency analysis","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, F., Ren, R., Van De Voorde, T., Xu, W., Zhou, G., and Zhou, Y. (2018). Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks. Remote Sens., 10.","DOI":"10.3390\/rs10030443"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.neucom.2015.02.073","article-title":"A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF","volume":"164","author":"Yao","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2408","DOI":"10.1109\/LGRS.2015.2479681","article-title":"A Novel Airport Detection Method via Line Segment Classification and Texture Classification","volume":"12","author":"Tang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1109\/LGRS.2017.2712638","article-title":"Airport Detection Based on a Multiscale Fusion Feature for Optical Remote Sensing Images","volume":"14","author":"Xiao","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/LGRS.2012.2210189","article-title":"Texture-based airport runway detection","volume":"10","author":"Aytekin","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1109\/LGRS.2014.2384051","article-title":"Airport Target Detection in Remote Sensing Images: A New Method Based on Two-Way Saliency","volume":"12","author":"Zhu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/LGRS.2010.2051792","article-title":"Airport detection from large ikonos images using clustered sift keypoints and region information","volume":"8","author":"Tao","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1109\/JSTARS.2017.2669335","article-title":"Multiresolution airport detection via hierarchical reinforcement learning saliency model","volume":"10","author":"Zhao","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.measurement.2014.12.017","article-title":"Investigating performance of Airborne LiDAR data filtering algorithms for DTM generation","volume":"63","author":"Polat","year":"2015","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, T., and Ouyang, C. (2018). End-to-End Airplane Detection Using Transfer Learning in Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10010139"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhu, M., Xin, P., Li, S., Qi, M., and Ma, S. (2018). Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks. Sensors, 18.","DOI":"10.3390\/s18072335"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bo, S., and Jing, Y. (2010, January 16\u201318). Region-based airplane detection in remotely sensed imagery. Proceedings of the 2010 3rd International Congress on Image and Signal Processing, Yantai, China.","DOI":"10.1109\/CISP.2010.5647478"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Luo, Q.H., and Shi, Z.W. (2016, January 10\u201315). Airplane detection in remote sensing images based on object proposal. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729355"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1109\/LGRS.2017.2683495","article-title":"Feature Extraction by Rotation-Invariant Matrix Representation for Object Detection in Aerial Image","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/JSTARS.2016.2620900","article-title":"Airport detection and aircraft recognition based on two-layer saliency model in high spatial resolution remote-sensing images","volume":"10","author":"Zhang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2015.2404578","article-title":"Object Detection Based on Sparse Representation and Hough Voting for Optical Remote Sensing Imagery","volume":"8","author":"Yokoya","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1109\/LGRS.2012.2214022","article-title":"Aircraft recognition in high-resolution satellite images using coarse-to-fine shape prior","volume":"10","author":"Liu","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"23071","DOI":"10.3390\/s150923071","article-title":"Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map","volume":"15","author":"Tan","year":"2015","journal-title":"Sensors"},{"key":"ref_28","first-page":"981503","article-title":"Unsupervised-learning airplane detection in remote sensing images","volume":"9815","author":"Zhang","year":"2015","journal-title":"MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1109\/LGRS.2017.2749478","article-title":"Object detection using convolutional neural networks in a coarse-to-fine manner","volume":"14","author":"Li","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2017.2708722","article-title":"M-FCN: Effective Fully Convolutional Network-Based Airplane Detection Framework","volume":"14","author":"Yang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhu, M., Xu, Y., Ma, S., Li, S., Ma, H., and Han, Y. (2019). Effective Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion and Improved Nonmaximal Suppression Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11091062"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.isprsjprs.2015.04.014","article-title":"Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests","volume":"112","author":"Yu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guo, W., Yang, W., Zhang, H., and Hua, G. (2018). Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network. Remote Sens., 10.","DOI":"10.3390\/rs10010131"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.eswa.2019.04.006","article-title":"Hyperspectral imagery classification based on semi-supervised 3-D deep neural network and adaptive band selection","volume":"129","author":"Sellami","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1109\/LGRS.2017.2673118","article-title":"Airport Detection on Optical Satellite Images Using Deep Convolutional Neural Networks","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J., and Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conf. Comput. Vis. Pattern Recognit., 580\u2013587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Washington, DC, USA.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, L., Lin, L., Liang, X., and He, K. (2016, January 11\u201314). Is Faster R-CNN Doing Well for Pedestrian Detection?. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_28"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ren, Y., Zhu, C., and Xiao, S. (2018). Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10091470"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.isprsjprs.2018.05.005","article-title":"A light and faster regional convolutional neural network for object detection in optical remote sensing images","volume":"141","author":"Ding","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1080\/2150704X.2017.1378452","article-title":"A multi-model ensemble method based on convolutional neural networks for aircraft detection in large remote sensing images","volume":"9","author":"Zhang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Han, X.B., Zhong, Y.F., Feng, R.Y., and Zhang, L.P. (2017, January 23\u201328). Robust geospatial object detection based on pre-trained faster r-cnn framework for high spatial resolution imagery. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127716"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.asr.2013.04.025","article-title":"Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city, Delhi-India","volume":"52","author":"Mallick","year":"2013","journal-title":"Adv. Space Res."},{"key":"ref_47","first-page":"157","article-title":"GENERATION OF THE 30 M-MESH GLOBAL DIGITAL SURFACE MODEL BY ALOS PRISM","volume":"41","author":"Tadono","year":"2016","journal-title":"ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7966","DOI":"10.1080\/01431161.2019.1607982","article-title":"Vertical accuracy assessment of global digital elevation models and validation of gravity database heights in Niger","volume":"40","author":"Yahaya","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.3390\/rs4061804","article-title":"Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation","volume":"4","author":"Susaki","year":"2012","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/TGRS.2003.810682","article-title":"A progressive morphological filter for removing nonground measurements from airborne LIDAR data","volume":"41","author":"Zhang","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/13658816.2018.1511793","article-title":"Are all cities with similar urban form or not? Redefining cities with ubiquitous points of interest and evaluating them with indicators at city and block levels in China","volume":"32","author":"Song","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1007\/978-3-319-54193-8_14","article-title":"R-cnn for small object detection","volume":"10115","author":"Chen","year":"2017","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Eggert, C., Brehm, S., Winschel, A., Zecha, D., and Lienhart, R. (2017, January 10\u201314). A closer look: Small object detection in faster R-CNN. Proceedings of the 2017 IEEE Int. Conf. Multimed. Expo (Icme), Hong Kong, China .","DOI":"10.1109\/ICME.2017.8019550"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Foody, G.M., Ling, F., Boyd, D.S., Li, X., and Wardlaw, J. (2019). Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space. Remote Sens., 11.","DOI":"10.3390\/rs11030266"},{"key":"ref_55","first-page":"72","article-title":"A methodology to generate a synergetic land-cover map by fusion of different land-cover products","volume":"19","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1109\/TGRS.2006.874750","article-title":"A method to compare and improve land cover datasets: application to the GLC-2000 and MODIS land cover products","volume":"44","author":"See","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.tre.2009.08.003","article-title":"A comparative study of airport connectivity in China, Europe and US: Which network provides the best service to passengers?","volume":"46","author":"Paleari","year":"2010","journal-title":"Transp. Res. Part E: Logist. Transp. Rev."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the 2016 European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conf. Comput. Vis. Pattern Recognit. (Cvpr), 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2018.02.006","article-title":"Building instance classification using street view images","volume":"145","author":"Kang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.11.008","article-title":"From Google Maps to a fine-grained catalog of street trees","volume":"135","author":"Branson","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Cao, R., Zhu, J., Tu, W., Li, Q., Cao, J., Liu, B., Zhang, Q., and Qiu, G. (2018). Integrating Aerial and Street View Images for Urban Land Use Classification. Remote Sens., 10.","DOI":"10.3390\/rs10101553"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2204\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:22:36Z","timestamp":1760188956000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,20]]},"references-count":62,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11192204"],"URL":"https:\/\/doi.org\/10.3390\/rs11192204","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,9,20]]}}}