{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:39:12Z","timestamp":1775666352349,"version":"3.50.1"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:00:00Z","timestamp":1712188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:00:00Z","timestamp":1712188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Scientific and Technical Research Council of Turkey","award":["122E315"],"award-info":[{"award-number":["122E315"]}]},{"name":"Kayseri University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Detecting and repairing road defects is crucial for road safety, vehicle maintenance, and enhancing tourism on well-maintained roads. However, monitoring all roads by vehicle incurs high costs. With the widespread use of remote sensing technologies, high-resolution satellite images offer a cost-effective alternative. This study proposes a new technique, SDPH, for automated detection of damaged roads from vast, high-resolution satellite images. In the SDPH technique, satellite images are organized in a pyramid grid file system, allowing deep learning methods to efficiently process them. The images, generated as<jats:inline-formula><jats:alternatives><jats:tex-math>$$256\\times 256$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mn>256<\/mml:mn><mml:mo>\u00d7<\/mml:mo><mml:mn>256<\/mml:mn><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>dimensions, are stored in a directory with explicit location information. The SDPH technique employs a two-stage object detection models, utilizing classical and modified RCNNv3, YOLOv5, and YOLOv8. Classical RCNNv3, YOLOv5, and YOLOv8 and modified RCNNv3, YOLOv5, and YOLOv8 in the first stage for identifying roads, achieving f1 scores of 0.743, 0.716, 0.710, 0.955, 0.958, and 0.954, respectively. When the YOLOv5, with the highest f1 score, was fed to the second stage; modified RCNNv3, YOLOv5, and YOLOv8 detected road defects, achieving f1 scores of 0.957,0.971 and 0.964 in the second process. When the same CNN model was used for road and road defect detection in the proposed SDPH model, classical RCNNv3, improved RCNNv3, classical YOLOv5, improved YOLOv5, classical YOLOv8, improved RCNNv8 achieved micro f1 scores of 0.752, 0.956, 0.726, 0.969, 0.720 and 0.965, respectively. In addition, these models processed 11, 10, 33, 31, 37, and 36 FPS images by performing both stage operations, respectively. Evaluations on geotiff satellite images from Kayseri Metropolitan Municipality, ranging between 20 and 40 gigabytes, demonstrated the efficiency of the SDPH technique. Notably, the modified YOLOv5 outperformed, detecting paths and defects in 0.032\u00a0s with the micro f1 score of 0.969. Fine-tuning on TileCache enhanced f1 scores and reduced computational costs across all models.<\/jats:p>","DOI":"10.1007\/s11554-024-01451-7","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T10:01:38Z","timestamp":1712224898000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["SDPH: a new technique for spatial detection of path holes from huge volume high-resolution raster images in near real-time"],"prefix":"10.1007","volume":"21","author":[{"given":"Murat","family":"Tasyurek","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"issue":"1","key":"1451_CR1","doi-asserted-by":"publisher","first-page":"52","DOI":"10.5958\/0974-0848.2016.00014.2","volume":"38","author":"H Singh","year":"2016","unstructured":"Singh, H., Kushwaha, V., Agarwal, A.D., Sandhu, S.S.: Fatal road traffic accidents: causes and factors responsible. J. Indian Acad. Forensic Med. 38(1), 52\u201354 (2016)","journal-title":"J. Indian Acad. Forensic Med."},{"issue":"5","key":"1451_CR2","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1007\/s11554-021-01122-x","volume":"18","author":"PN Srinivasu","year":"2021","unstructured":"Srinivasu, P.N., Bhoi, A.K., Jhaveri, R.H., Reddy, G.T., Bilal, M.: Probabilistic deep q network for real-time path planning in censorious robotic procedures using force sensors. J. Real-Time Image Process. 18(5), 1773\u20131785 (2021)","journal-title":"J. Real-Time Image Process."},{"issue":"1","key":"1451_CR3","doi-asserted-by":"publisher","first-page":"22","DOI":"10.31004\/jestm.v1i1.11","volume":"1","author":"NYS Munti","year":"2021","unstructured":"Munti, N.Y.S., Setiawan, B., et al.: Analysis of web-based geographic information system mapping of broken roads in Kampar regency. J. Eng. Sci. Technol. Manag. (JES-TM) 1(1), 22\u201327 (2021)","journal-title":"J. Eng. Sci. Technol. Manag. (JES-TM)"},{"key":"1451_CR4","doi-asserted-by":"publisher","first-page":"8728","DOI":"10.1007\/s11227-020-03604-4","volume":"77","author":"L Long","year":"2021","unstructured":"Long, L., He, F., Liu, H.: The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in jinsha river. J. Supercomput. 77, 8728\u20138744 (2021)","journal-title":"J. Supercomput."},{"key":"1451_CR5","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1007\/s11554-019-00925-3","volume":"17","author":"X Li","year":"2020","unstructured":"Li, X., Yirui, W., Zhang, W., Wang, R., Hou, F.: Deep learning methods in real-time image super-resolution: a survey. J. Real-Time Image Process. 17, 1885\u20131909 (2020)","journal-title":"J. Real-Time Image Process."},{"issue":"10","key":"1451_CR6","doi-asserted-by":"publisher","first-page":"12710","DOI":"10.1007\/s11227-022-04379-6","volume":"78","author":"V Chaudhary","year":"2022","unstructured":"Chaudhary, V., Buttar, P.K., Sachan, M.K.: Satellite imagery analysis for road segmentation using u-net architecture. J. Supercomput. 78(10), 12710\u201312725 (2022)","journal-title":"J. Supercomput."},{"issue":"17","key":"1451_CR7","doi-asserted-by":"publisher","first-page":"18524","DOI":"10.1007\/s11227-022-04617-x","volume":"78","author":"H Huan","year":"2022","unstructured":"Huan, H., Zou, N., Zhang, Y., Xie, Y., Wang, C.: Remote sensing image reconstruction using an asymmetric multi-scale super-resolution network. J. Supercomput. 78(17), 18524\u201318550 (2022)","journal-title":"J. Supercomput."},{"key":"1451_CR8","doi-asserted-by":"publisher","first-page":"1494","DOI":"10.1007\/s11227-016-1677-z","volume":"72","author":"I Anagnostopoulos","year":"2016","unstructured":"Anagnostopoulos, I., Zeadally, S., Exposito, E.: Handling big data: research challenges and future directions. J. Supercomput. 72, 1494\u20131516 (2016)","journal-title":"J. Supercomput."},{"key":"1451_CR9","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/s11554-018-0831-7","volume":"15","author":"C Chen","year":"2018","unstructured":"Chen, C., Li, W., Gao, L., Li, H., Plaza, J.: Special issue on advances in real-time image processing for remote sensing. J. Real-Time Image Process. 15, 435\u2013438 (2018)","journal-title":"J. Real-Time Image Process."},{"issue":"4","key":"1451_CR10","doi-asserted-by":"publisher","first-page":"388","DOI":"10.17694\/bajece.1059070","volume":"10","author":"M Tasyurek","year":"2022","unstructured":"Tasyurek, M.: A novel approach to improve the performance of the database storing big data with time information. Balk. J. Electr. Comput. Eng. 10(4), 388\u2013396 (2022)","journal-title":"Balk. J. Electr. Comput. Eng."},{"issue":"5","key":"1451_CR11","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1007\/s11554-021-01113-y","volume":"18","author":"TD Ngo","year":"2021","unstructured":"Ngo, T.D., Bui, T.T., Pham, T.M., Thai, H.T.B., Nguyen, G.L., Nguyen, V.: Image deconvolution for optical small satellite with deep learning and real-time gpu acceleration. J. Real-Time Image Process. 18(5), 1697\u20131710 (2021)","journal-title":"J. Real-Time Image Process."},{"key":"1451_CR12","doi-asserted-by":"crossref","unstructured":"Xu, H.: Arcgis data models for managing and procesing imagery (2012)","DOI":"10.5194\/isprsarchives-XXXIX-B4-97-2012"},{"key":"1451_CR13","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s12061-008-9005-5","volume":"1","author":"M Gibin","year":"2008","unstructured":"Gibin, M., Singleton, A., Milton, R., Mateos, P., Longley, P.: An exploratory cartographic visualisation of London through the google maps api. Appl. Spat. Anal. Policy 1, 85\u201397 (2008)","journal-title":"Appl. Spat. Anal. Policy"},{"key":"1451_CR14","unstructured":"Yin, F., Feng, M.: A webgis framework for vector geospatial data sharing based on open source projects. In: Proceedings. The 2009 International Symposium on Web Information Systems and Applications (WISA 2009), p. 124. Academy Publisher (2009)"},{"key":"1451_CR15","doi-asserted-by":"crossref","unstructured":"Ta\u015fy\u00fcrek, M.: Regenerating large volume vector layers with a denormalization-based method. In: 2021 6th International Conference on Computer Science and Engineering (UBMK). IEEE. pp. 124\u2013128 (2021)","DOI":"10.1109\/UBMK52708.2021.9558893"},{"key":"1451_CR16","doi-asserted-by":"crossref","unstructured":"Guo, D., Zou, Y., Wang, S.: An effective tile caching mechanism of uav remote sensing map based on hilbert coding index. In: 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN). IEEE. pp. 535\u2013541 (2019)","DOI":"10.1109\/ICCSN.2019.8905357"},{"key":"1451_CR17","doi-asserted-by":"crossref","unstructured":"Garc\u00eda, R., de\u00a0Castro, J.P., Verd\u00fa, E., Verd\u00fa, M.J., Regueras, L.M.: Web map tile services for spatial data infrastructures: Management and optimization. In: Cartography-A Tool for Spatial Analysis, pp. 26\u201348 (2012)","DOI":"10.5772\/46129"},{"key":"1451_CR18","doi-asserted-by":"crossref","unstructured":"Mahajan, S., Rajesh, M.A., Panigrahi, N.: Scale space visualization of dynamic track clusters in an enterprise gis. In: 2022 3rd International Conference for Emerging Technology (INCET). IEEE. pp. 1\u20136 (2022)","DOI":"10.1109\/INCET54531.2022.9825018"},{"key":"1451_CR19","doi-asserted-by":"publisher","first-page":"8590","DOI":"10.1007\/s11227-020-03159-4","volume":"76","author":"A Khamparia","year":"2020","unstructured":"Khamparia, A., Gupta, D., de Albuquerque, V.H.C., Sangaiah, A.K., Jhaveri, R.H.: Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning. J. Supercomput. 76, 8590\u20138608 (2020)","journal-title":"J. Supercomput."},{"key":"1451_CR20","doi-asserted-by":"publisher","first-page":"11083","DOI":"10.1007\/s11227-021-03712-9","volume":"77","author":"L Qiu","year":"2021","unstructured":"Qiu, L., Zhang, D., Tian, Y., Al-Nabhan, N.: Deep learning-based algorithm for vehicle detection in intelligent transportation systems. J. Supercomput. 77, 11083\u201311098 (2021)","journal-title":"J. Supercomput."},{"issue":"6","key":"1451_CR21","doi-asserted-by":"publisher","first-page":"7616","DOI":"10.1007\/s11227-021-04184-7","volume":"78","author":"M Toshpulatov","year":"2022","unstructured":"Toshpulatov, M., Lee, W., Lee, S., Haghighian Roudsari, A.: Human pose, hand and mesh estimation using deep learning: A survey. J. Supercomput. 78(6), 7616\u20137654 (2022)","journal-title":"J. Supercomput."},{"issue":"2","key":"1451_CR22","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1007\/s00371-023-02827-9","volume":"40","author":"M Tasyurek","year":"2024","unstructured":"Tasyurek, M.: Odrp: a new approach for spatial street sign detection from EXIF using deep learning-based object detection, distance estimation, rotation and projection system. Vis. Comput. 40(2), 983\u20131003 (2024)","journal-title":"Vis. Comput."},{"issue":"12","key":"1451_CR23","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1111\/mice.12387","volume":"33","author":"H Maeda","year":"2018","unstructured":"Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., Omata, H.: Road damage detection and classification using deep neural networks with smartphone images. Comput.-Aided Civ. Infrastruct. Eng. 33(12), 1127\u20131141 (2018)","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"1451_CR24","unstructured":"Rath, S.: Fine tuning yolov7 on custom dataset (2022)"},{"key":"1451_CR25","doi-asserted-by":"crossref","unstructured":"Parvathavarthini, S., Shreekanth, M., Vigneshkumar, S., Santhos, NS.: Road damage detection using deep learning. In: 2023 7th International Conference on Computing Methodologies and Communication (ICCMC). IEEE. pp. 314\u2013318 (2023)","DOI":"10.1109\/ICCMC56507.2023.10083795"},{"issue":"4","key":"1451_CR26","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7542","volume":"35","author":"C \u00d6zt\u00fcrk","year":"2023","unstructured":"\u00d6zt\u00fcrk, C., Ta\u015fy\u00fcrek, M., T\u00fcrkdamar, M.U.: Transfer learning and fine-tuned transfer learning methods\u2019 effectiveness analyse in the cnn-based deep learning models. Concurr. Comput. Pract. Exp. 35(4), e7542 (2023)","journal-title":"Concurr. Comput. Pract. Exp."},{"issue":"3","key":"1451_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11554-023-01311-w","volume":"20","author":"M Tasyurek","year":"2023","unstructured":"Tasyurek, M., Arslan, R.S.: Rt-droid: a novel approach for real-time android application analysis with transfer learning-based cnn models. J Real-Time Image Process 20(3), 1\u201317 (2023)","journal-title":"J Real-Time Image Process"},{"issue":"28","key":"1451_CR28","doi-asserted-by":"publisher","first-page":"20939","DOI":"10.1007\/s00521-023-08809-1","volume":"35","author":"FM Talaat","year":"2023","unstructured":"Talaat, F.M., ZainEldin, H.: An improved fire detection approach based on yolo-v8 for smart cities. Neural Comput. Appl. 35(28), 20939\u201320954 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"4","key":"1451_CR29","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.3390\/make5040083","volume":"5","author":"J Terven","year":"2023","unstructured":"Terven, J., C\u00f3rdova-Esparza, D.-M., Romero-Gonz\u00e1lez, J.-A.: A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas. Mach. Learn. Knowl. Extr. 5(4), 1680\u20131716 (2023)","journal-title":"Mach. Learn. Knowl. Extr."},{"issue":"12","key":"1451_CR30","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2021","unstructured":"Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33(12), 6999\u20137019 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1451_CR31","doi-asserted-by":"publisher","first-page":"50850","DOI":"10.1109\/ACCESS.2018.2868993","volume":"6","author":"W Kehe","year":"2018","unstructured":"Kehe, W., Chen, Z., Li, W.: A novel intrusion detection model for a massive network using convolutional neural networks. Ieee Access 6, 50850\u201350859 (2018)","journal-title":"Ieee Access"},{"key":"1451_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101515","volume":"67","author":"L Nanni","year":"2022","unstructured":"Nanni, L., Manf\u00e8, A., Maguolo, G., Lumini, A., Brahnam, S.: High performing ensemble of convolutional neural networks for insect pest image detection. Ecol. Inform. 67, 101515 (2022)","journal-title":"Ecol. Inform."},{"issue":"6","key":"1451_CR33","first-page":"2461","volume":"13","author":"SB Jadhav","year":"2021","unstructured":"Jadhav, S.B., Udupi, V.R., Patil, S.B.: Identification of plant diseases using convolutional neural networks. Int. J. Inf. Technol. 13(6), 2461\u20132470 (2021)","journal-title":"Int. J. Inf. Technol."},{"key":"1451_CR34","doi-asserted-by":"crossref","unstructured":"Koller, O., Zargaran, O., Ney, H., Bowden, R.: Deep sign: Hybrid cnn-hmm for continuous sign language recognition. In: Proceedings of the British Machine Vision Conference 2016 (2016)","DOI":"10.5244\/C.30.136"},{"key":"1451_CR35","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.cviu.2018.09.001","volume":"176","author":"G Ciocca","year":"2018","unstructured":"Ciocca, G., Napoletano, P., Schettini, R.: Cnn-based features for retrieval and classification of food images. Comput. Vis. Image Underst. 176, 70\u201377 (2018)","journal-title":"Comput. Vis. Image Underst."},{"key":"1451_CR36","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.biosystemseng.2020.07.019","volume":"198","author":"B Achour","year":"2020","unstructured":"Achour, B., Belkadi, M., Filali, I., Laghrouche, M., Lahdir, M.: Image analysis for individual identification and feeding behaviour monitoring of dairy cows based on convolutional neural networks (cnn). Biosyst. Eng. 198, 31\u201349 (2020)","journal-title":"Biosyst. Eng."},{"issue":"1","key":"1451_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56989-5","volume":"10","author":"D Bermejo-Pel\u00e1ez","year":"2020","unstructured":"Bermejo-Pel\u00e1ez, D., Ash, S.Y., Washko, G.R., Est\u00e9par, R.S.J., Ledesma-Carbayo, M.J.: Classification of interstitial lung abnormality patterns with an ensemble of deep convolutional neural networks. Sci. Rep. 10(1), 1\u201315 (2020)","journal-title":"Sci. Rep."},{"key":"1451_CR38","first-page":"223","volume-title":"Emerg. Technol. Data Min. Inf. Secur.","author":"A Rao","year":"2021","unstructured":"Rao, A., Motwani, R., Sarguroh, N., Kingrani, P., Khandaskar, S.: Real-time traffic sign recognition using convolutional neural networks. In: Emerg. Technol. Data Min. Inf. Secur., pp. 223\u2013234. Springer, NY (2021)"},{"key":"1451_CR39","doi-asserted-by":"crossref","unstructured":"Yang, W.-J., Luo, C.-C., Chung, P.-C., Yang, J.-F.: Simplified neural networks with smart detection for road traffic sign recognition. In: Future of Information and Communication Conference. Springer. pp. 237\u2013249 (2019)","DOI":"10.1007\/978-3-030-12388-8_17"},{"issue":"18","key":"1451_CR40","doi-asserted-by":"publisher","first-page":"4021","DOI":"10.3390\/s19184021","volume":"19","author":"J Cao","year":"2019","unstructured":"Cao, J., Song, C., Peng, S., Xiao, F., Song, S.: Improved traffic sign detection and recognition algorithm for intelligent vehicles. Sensors 19(18), 4021 (2019)","journal-title":"Sensors"},{"key":"1451_CR41","unstructured":"LeCun, Y., et\u00a0al.: Lenet-5, convolutional neural networks. URL: http:\/\/yann. lecun. com\/exdb\/lenet 20(5):14 (2015)"},{"key":"1451_CR42","doi-asserted-by":"crossref","unstructured":"Sultana, F., Sufian, A., Dutta, P.: Advancements in image classification using convolutional neural network. In: 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE. pp. 122\u2013129 (2018)","DOI":"10.1109\/ICRCICN.2018.8718718"},{"key":"1451_CR43","doi-asserted-by":"crossref","unstructured":"Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et\u00a0al.: Speed\/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7310\u20137311 (2017)","DOI":"10.1109\/CVPR.2017.351"},{"key":"1451_CR44","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, GE.: 2012 alexnet (2012)"},{"key":"1451_CR45","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1451_CR46","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1451_CR47","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1451_CR48","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"1451_CR49","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"1451_CR50","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"1451_CR51","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"issue":"9","key":"1451_CR52","doi-asserted-by":"publisher","first-page":"1970","DOI":"10.3390\/rs14091970","volume":"14","author":"H Kawauchi","year":"2022","unstructured":"Kawauchi, H., Fuse, T.: Shap-based interpretable object detection method for satellite imagery. Remote Sens. 14(9), 1970 (2022)","journal-title":"Remote Sens."},{"key":"1451_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107885","volume":"113","author":"W Zhi-Ze","year":"2021","unstructured":"Zhi-Ze, W., Wang, X.-F., Zou, L., Li-Xiang, X., Li, X.-L., Weise, T.: Hierarchical object detection for very high-resolution satellite images. Appl. Soft Comput. 113, 107885 (2021)","journal-title":"Appl. Soft Comput."},{"issue":"8","key":"1451_CR54","doi-asserted-by":"publisher","first-page":"2827","DOI":"10.1080\/01431161.2020.1826059","volume":"42","author":"Z Song","year":"2021","unstructured":"Song, Z., Sui, H., Hua, L.: A hierarchical object detection method in large-scale optical remote sensing satellite imagery using saliency detection and cnn. Int. J. Remote Sens. 42(8), 2827\u20132847 (2021)","journal-title":"Int. J. Remote Sens."},{"issue":"12","key":"1451_CR55","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.3390\/rs14122861","volume":"14","author":"H Gong","year":"2022","unstructured":"Gong, H., Tingkui, M., Li, Q., Dai, H., Li, C., He, Z., Wang, W., Han, F., Tuniyazi, A., Li, H., et al.: Swin-transformer-enabled yolov5 with attention mechanism for small object detection on satellite images. Remote Sens. 14(12), 2861 (2022)","journal-title":"Remote Sens."},{"issue":"8","key":"1451_CR56","doi-asserted-by":"publisher","first-page":"9489","DOI":"10.1007\/s13369-021-06288-x","volume":"47","author":"SD Khan","year":"2021","unstructured":"Khan, S.D., Alarabi, L., Basalamah, S.: A unified deep learning framework of multi-scale detectors for geo-spatial object detection in high-resolution satellite images. Arab. J. Sci. Eng. 47(8), 9489\u2013504 (2021)","journal-title":"Arab. J. Sci. Eng."},{"key":"1451_CR57","doi-asserted-by":"crossref","unstructured":"Johanson, M., Belenki, S., Jalminger, J., Fant, M., Gjertz, M.: Big automotive data: Leveraging large volumes of data for knowledge-driven product development. In: 2014 IEEE international conference on big data (Big Data). IEEE. pp. 736\u2013741 (2014)","DOI":"10.1109\/BigData.2014.7004298"},{"key":"1451_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105584","volume":"175","author":"C Zhang","year":"2020","unstructured":"Zhang, C., Marzougui, A., Sankaran, S.: High-resolution satellite imagery applications in crop phenotyping: an overview. Comput. Electron. Agric. 175, 105584 (2020)","journal-title":"Comput. Electron. Agric."},{"key":"1451_CR59","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"1451_CR60","unstructured":"Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv: 1804.02767 (2018)"},{"key":"1451_CR61","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"1451_CR62","unstructured":"Jocher, G., Nishimura, K., Mineeva, T., Vilari\u00f1o, R.: Yolov5 (2020)"},{"key":"1451_CR63","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","volume":"199","author":"P Jiang","year":"2022","unstructured":"Jiang, P., Ergu, D., Liu, F., Cai, Y., Ma, B.: A review of yolo algorithm developments. Procedia Comput. Sci. 199, 1066\u20131073 (2022)","journal-title":"Procedia Comput. Sci."},{"issue":"3","key":"1451_CR64","doi-asserted-by":"publisher","first-page":"5390","DOI":"10.15376\/biores.16.3.5390-5406","volume":"16","author":"Y Fang","year":"2021","unstructured":"Fang, Y., Guo, X., Chen, K., Zhou, Z., Ye, Q.: Accurate and automated detection of surface knots on sawn timbers using yolo-v5 model. BioResources 16(3), 5390 (2021)","journal-title":"BioResources"},{"key":"1451_CR65","unstructured":"Munawar, R., Jocher, G.: Ultralytics yolov8 (2023)"},{"key":"1451_CR66","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, 91\u201399 (2015)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1451_CR67","volume-title":"GeoServer Beginner\u2019s Guide","author":"B Youngblood","year":"2013","unstructured":"Youngblood, B.: GeoServer Beginner\u2019s Guide. Packt Publishing Ltd (2013)"},{"key":"1451_CR68","unstructured":"MetaCarta. Tilecache-web map tile caching (2010)"},{"issue":"2","key":"1451_CR69","first-page":"715","volume":"36","author":"M Tasyurek","year":"2021","unstructured":"Tasyurek, M., Celik, M.: Fastgtwr: a fast geographically and temporally weighted regression approach. J. Fac. Eng. Archit. Gazi Univ. 36(2), 715\u2013726 (2021)","journal-title":"J. Fac. Eng. Archit. Gazi Univ."},{"issue":"17","key":"1451_CR70","doi-asserted-by":"publisher","first-page":"14777","DOI":"10.1007\/s00521-022-07311-4","volume":"34","author":"M Tasyurek","year":"2022","unstructured":"Tasyurek, M., Celik, M.: 4d-gwr: geographically, altitudinal, and temporally weighted regression. Neural Comput. Appl. 34(17), 14777\u201314791 (2022)","journal-title":"Neural Comput. Appl."},{"key":"1451_CR71","unstructured":"Stackexchange. Calculate lat lon bounds for individual tile generated from gdal2tiles (2012)"},{"key":"1451_CR72","unstructured":"Agafonkin, V.: Leaflet (2011)"},{"key":"1451_CR73","volume-title":"OpenStreetMap","author":"J Bennett","year":"2010","unstructured":"Bennett, J.: OpenStreetMap. Packt Publishing Ltd (2010)"},{"key":"1451_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, D., Wang, J., Zhao, X.: Estimating the uncertainty of average f1 scores. In: Proceedings of the 2015 International conference on the theory of information retrieval. pp. 317\u2013320 (2015)","DOI":"10.1145\/2808194.2809488"},{"issue":"17","key":"1451_CR75","doi-asserted-by":"publisher","first-page":"19879","DOI":"10.1007\/s11227-023-05409-7","volume":"79","author":"G Zhou","year":"2023","unstructured":"Zhou, G., Yuan, S., Xing, H., Jiang, Y., Geng, P., Cao, Y., Ben, X.: Micro-expression action unit recognition based on dynamic image and spatial pyramid. J. Supercomput. 79(17), 19879\u2013902 (2023)","journal-title":"J. Supercomput."},{"key":"1451_CR76","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s11265-020-01611-5","volume":"94","author":"RK Nath","year":"2021","unstructured":"Nath, R.K., Thapliyal, H., Caban-Holt, A.: Machine learning based stress monitoring in older adults using wearable sensors and cortisol as stress biomarker. J. Signal Process. Syst. 94, 513\u2013525 (2021)","journal-title":"J. Signal Process. Syst."},{"issue":"2","key":"1451_CR77","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s11554-023-01402-8","volume":"21","author":"W Ahmad","year":"2024","unstructured":"Ahmad, W., Mahdavi, H., Hamzaoglu, I.: An efficient versatile video coding motion estimation hardware. J. Real-Time Image Process. 21(2), 25 (2024)","journal-title":"J. Real-Time Image Process."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01451-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01451-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01451-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T17:33:37Z","timestamp":1731692017000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01451-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,4]]},"references-count":77,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1451"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01451-7","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,4]]},"assertion":[{"value":"4 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"70"}}