{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:02:13Z","timestamp":1784646133554,"version":"3.55.0"},"reference-count":24,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T00:00:00Z","timestamp":1633651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicles (UAVs) must keep track of their location in order to maintain flight plans. Currently, this task is almost entirely performed by a combination of Inertial Measurement Units (IMUs) and reference to GNSS (Global Navigation Satellite System). Navigation by GNSS, however, is not always reliable, due to various causes both natural (reflection and blockage from objects, technical fault, inclement weather) and artificial (GPS spoofing and denial). In such GPS-denied situations, it is desirable to have additional methods for aerial geolocalization. One such method is visual geolocalization, where aircraft use their ground facing cameras to localize and navigate. The state of the art in many ground-level image processing tasks involve the use of Convolutional Neural Networks (CNNs). We present here a study of how effectively a modern CNN designed for visual classification can be applied to the problem of Absolute Visual Geolocalization (AVL, localization without a prior location estimate). An Xception based architecture is trained from scratch over a &gt;1000 km2 section of Washington County, Arkansas to directly regress latitude and longitude from images from different orthorectified high-altitude survey flights. It achieves average localization accuracy on unseen image sets over the same region from different years and seasons with as low as 115 m average error, which localizes to 0.004% of the training area, or about 8% of the width of the 1.5 \u00d7 1.5 km input image. This demonstrates that CNNs are expressive enough to encode robust landscape information for geolocalization over large geographic areas. Furthermore, discussed are methods of providing uncertainty for CNN regression outputs, and future areas of potential improvement for use of deep neural networks in visual geolocalization.<\/jats:p>","DOI":"10.3390\/rs13194017","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:37:49Z","timestamp":1633901869000},"page":"4017","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Direct Aerial Visual Geolocalization Using Deep Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Winthrop","family":"Harvey","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chase","family":"Rainwater","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5548-6955","authenticated-orcid":false,"given":"Jackson","family":"Cothren","sequence":"additional","affiliation":[{"name":"Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2014.40","article-title":"Attacks on GPS Time Reliability","volume":"12","author":"Bonebrake","year":"2014","journal-title":"IEEE Secur. Priv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103666","DOI":"10.1016\/j.robot.2020.103666","article-title":"A review on absolute visual localization for UAV","volume":"135","author":"Couturier","year":"2021","journal-title":"Robot. Auton. Syst."},{"key":"ref_3","unstructured":"Chollet, F. (2021, October 07). Xception: Deep Learning with Depthwise Separable Convolutions. CoRR, Available online: https:\/\/arxiv.org\/abs\/1610.02357."},{"key":"ref_4","unstructured":"Nister, D., Naroditsky, O., and Bergen, J. (July, January 27). Visual odometry. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Forster, C., Pizzoli, M., and Scaramuzza, D. (June, January 31). SVO: Fast semi-direct monocular visual odometry. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6906584"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1177\/0278364914554813","article-title":"Keyframe-based visual\u2013inertial odometry using nonlinear optimization","volume":"34","author":"Leutenegger","year":"2015","journal-title":"Int. J. Robot. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/70.938381","article-title":"A solution to the simultaneous localization and map building (SLAM) problem","volume":"17","author":"Dissanayake","year":"2001","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, S., Feng, M., Nguyen, R.M.H., and Lee, G.H. (2018, January 18\u201322). CVM-Net: Cross-View Matching Network for Image-Based Ground-to-Aerial Geo-Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00758"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Costea, D., and Leordeanu, M. (2016). Aerial image geolocalization from recognition and matching of roads and intersections. arXiv.","DOI":"10.5244\/C.30.118"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Amer, K., Samy, M., ElHakim, R., Shaker, M., and ElHelw, M. (2017, January 22\u201329). Convolutional neural network-based deep urban signatures with application to drone localization. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.250"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nassar, A., Amer, K., ElHakim, R., and ElHelw, M. (2018, January 18\u201322). A deep cnn-based framework for enhanced aerial imagery registration with applications to uav geolocalization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00201"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Goforth, H., and Lucey, S. (2019, January 20\u201324). GPS-denied UAV localization using pre-existing satellite imagery. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793558"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"575","DOI":"10.5194\/isprs-archives-XLII-2-W13-575-2019","article-title":"Translating aerial images into street-map-like representations for visual self-localization of UAVs","volume":"2019","author":"Schleiss","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_15","unstructured":"Marcu, A., Costea, D., Slusanschi, E., and Leordeanu, M. (2018). A multi-stage multi-task neural network for aerial scene interpretation and geolocalization. arXiv."},{"key":"ref_16","unstructured":"Chollet, F. (2021, October 07). Keras. Available online: https:\/\/keras.io."},{"key":"ref_17","unstructured":"Tan, M., and Le, Q.V. (2020). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Identity Mappings in Deep Residual Networks. arXiv.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_19","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2019). MobileNetV2: Inverted Residuals and Linear Bottlenecks. arXiv.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015). Rethinking the Inception Architecture for Computer Vision. arXiv.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_22","unstructured":"Guo, C., Pleiss, G., Sun, Y., and Weinberger, K.Q. (2017, January 6\u201311). On calibration of modern neural networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"64270","DOI":"10.1109\/ACCESS.2018.2877890","article-title":"Benchmark analysis of representative deep neural network architectures","volume":"6","author":"Bianco","year":"2018","journal-title":"IEEE Access"},{"key":"ref_24","unstructured":"Blalock, D., Ortiz, J.J.G., Frankle, J., and Guttag, J. (2020). What is the state of neural network pruning?. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/4017\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:10:28Z","timestamp":1760166628000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/4017"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,8]]},"references-count":24,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13194017"],"URL":"https:\/\/doi.org\/10.3390\/rs13194017","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,8]]}}}