{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:10:23Z","timestamp":1767337823586,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T00:00:00Z","timestamp":1650499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets.<\/jats:p>","DOI":"10.3390\/s22093193","type":"journal-article","created":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:45:21Z","timestamp":1650761121000},"page":"3193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Weather Classification by Utilizing Synthetic Data"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1359-3389","authenticated-orcid":false,"given":"Saad","family":"Minhas","sequence":"first","affiliation":[{"name":"School of Computer Science & Electrical Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK"}]},{"given":"Zeba","family":"Khanam","sequence":"additional","affiliation":[{"name":"School of Computer Science & Electrical Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK"}]},{"given":"Shoaib","family":"Ehsan","sequence":"additional","affiliation":[{"name":"School of Computer Science & Electrical Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6412-8519","authenticated-orcid":false,"given":"Klaus","family":"McDonald-Maier","sequence":"additional","affiliation":[{"name":"School of Computer Science & Electrical Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1563-9934","authenticated-orcid":false,"given":"Aura","family":"Hern\u00e1ndez-Sabat\u00e9","sequence":"additional","affiliation":[{"name":"Computer Vision Centre, Universitat Aut\u00f2noma de Barcelona, Pla\u00e7a C\u00edvica, 08193 Bellaterra, Spain"},{"name":"Departament de Ci\u00e8ncies de la Computaci\u00f3, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,21]]},"reference":[{"key":"ref_1","unstructured":"(2022, January 26). Position Paper on Road Worthiness. Available online: https:\/\/knowledge-base.connectedautomateddriving.eu\/wp-content\/uploads\/2019\/08\/CARTRE-Roadworthiness-Testing-Safety-Validation-position-Paper_3_After_review.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1175\/2009WCAS1014.1","article-title":"Assessing the impact of weather on traffic intensity","volume":"2","author":"Cools","year":"2010","journal-title":"Weather Clim. Soc."},{"key":"ref_3","unstructured":"Achari, V.P.S., Khanam, Z., Singh, A.K., Jindal, A., Prakash, A., and Kumar, N. (2021, January 7\u201310). I 2 UTS: An IoT based Intelligent Urban Traffic System. Proceedings of the 2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR), Paris, France."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.trf.2006.11.002","article-title":"Effects of weather and weather forecasts on driver behaviour","volume":"10","author":"Summala","year":"2007","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lu, C., Lin, D., Jia, J., and Tang, C.K. (2014, January 23\u201328). Two-class weather classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.475"},{"key":"ref_6","first-page":"798","article-title":"Classification of weather situations on single color images","volume":"10","author":"Roser","year":"2008","journal-title":"IEEE Intell. Veh. Symp."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Ma, H. (2015, January 27\u201330). Multi-class weather classification on single images. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351637"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1109\/LGRS.2020.2993899","article-title":"ShipDeNet-20: An only 20 convolution layers and <1-MB lightweight SAR ship detector","volume":"18","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","first-page":"4004905","article-title":"Balance scene learning mechanism for offshore and inshore ship detection in SAR images","volume":"19","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","first-page":"5210322","article-title":"HOG-ShipCLSNet: A novel deep learning network with hog feature fusion for SAR ship classification","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","first-page":"4019905","article-title":"Squeeze-and-excitation Laplacian pyramid network with dual-polarization feature fusion for ship classification in sar images","volume":"19","author":"Zhang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., and Ke, X. (2021). Quad-FPN: A novel quad feature pyramid network for SAR ship detection. Remote Sens., 13.","DOI":"10.3390\/rs13142771"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108365","DOI":"10.1016\/j.patcog.2021.108365","article-title":"A polarization fusion network with geometric feature embedding for SAR ship classification","volume":"123","author":"Zhang","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_14","unstructured":"Khanam, Z., Soni, P., and Raheja, J.L. (2016). Development of 3D high definition endoscope system. Information Systems Design and Intelligent Applications, Springer."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Khanam, Z., and Raheja, J.L. (2018). Tracking of miniature-sized objects in 3D endoscopic vision. Algorithms and Applications, Springer.","DOI":"10.1007\/978-981-10-8102-6_6"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Aslam, B., Saha, S., Khanam, Z., Zhai, X., Ehsan, S., Stolkin, R., and McDonald-Maier, K. (2019, January 27\u201330). Gamma-induced degradation analysis of commercial off-the-shelf camera sensors. Proceedings of the 2019 IEEE SENSORS, Montreal, QC, Canada.","DOI":"10.1109\/SENSORS43011.2019.8956620"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Khanam, Z., Saha, S., Aslam, B., Zhai, X., Ehsan, S., Cazzaniga, C., Frost, C., Stolkin, R., and McDonald-Maier, K. (2019, January 8\u201312). Degradation measurement of kinect sensor under fast neutron beamline. Proceedings of the 2019 IEEE Radiation Effects Data Workshop, San Antonio, TX, USA.","DOI":"10.1109\/REDW.2019.8906531"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Khanam, Z., Aslam, B., Saha, S., Zhai, X., Ehsan, S., Stolkin, R., and McDonald-Maier, K. (2021). Gamma-Induced Image Degradation Analysis of Robot Vision Sensor for Autonomous Inspection of Nuclear Sites. IEEE Sens. J., 1.","DOI":"10.1109\/JSEN.2021.3050168"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7489","DOI":"10.1109\/ACCESS.2021.3138167","article-title":"E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights","volume":"10","author":"Gil","year":"2021","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-Sabat\u00e9, A., Yauri, J., Folch, P., Piera, M.\u00c0., and Gil, D. (2022). Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals. Appl. Sci., 12.","DOI":"10.3390\/app12052298"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1162\/neco_a_01321","article-title":"Assessing Goodness-of-Fit in Marked Point Process Models of Neural Population Coding via Time and Rate Rescaling","volume":"32","author":"Yousefi","year":"2020","journal-title":"Neural Comput."},{"key":"ref_22","unstructured":"Azizi, A., Tahmid, I., Waheed, A., Mangaokar, N., Pu, J., Javed, M., Reddy, C.K., and Viswanath, B. (2021). T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Qian, Y., Almazan, E.J., and Elder, J.H. (2016, January 25\u201328). Evaluating features and classifiers for road weather condition analysis. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533192"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1109\/TITS.2020.3025542","article-title":"Effects of Non-Driving Related Tasks During Self-Driving Mode","volume":"23","author":"Minhas","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","unstructured":"Hua, G., and J\u00e9gou, H. (2016). LEE: A Photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode. Computer Vision\u2014ECCV 2016 Workshops, Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands, 8\u201316 October 2016, Springer International Publishing."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., and Darrell, T. (2020). BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. arXiv.","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref_27","first-page":"471","article-title":"Image Recognition of Road Surface Conditions using Polarization and Wavelet Transform","volume":"27","author":"Lim","year":"2007","journal-title":"J. Korean Soc. Civ. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kawai, S., Takeuchi, K., Shibata, K., and Horita, Y. (2012, January 5\u20138). A method to distinguish road surface conditions for car-mounted camera images at night-time. Proceedings of the 2012 12th International Conference on ITS Telecommunications, Taipei, Taiwan.","DOI":"10.1109\/ITST.2012.6425265"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kurihata, H., Takahashi, T., Ide, I., Mekada, Y., Murase, H., Tamatsu, Y., and Miyahara, T. (2005, January 6\u20138). Rainy weather recognition from in-vehicle camera images for driver assistance. Proceedings of the IEEE Proceedings. Intelligent Vehicles Symposium, Las Vegas, NV, USA.","DOI":"10.1109\/IVS.2005.1505103"},{"key":"ref_30","unstructured":"Yu, W., He, H., and Zhang, N. (2009). Weather Recognition Based on Images Captured by Vision System in Vehicle. Advances in Neural Networks\u2014ISNN 2009, Springer."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1109\/TPAMI.2016.2640295","article-title":"Two-Class Weather Classification","volume":"39","author":"Lu","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sun, F., Hu, D., and Liu, H. (2014). Weather Condition Recognition Based on Feature Extraction and K-NN. Foundations and Practical Applications of Cognitive Systems and Information Processing, Springer.","DOI":"10.1007\/978-3-642-37835-5"},{"key":"ref_33","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017, January 13\u201315). CARLA: An Open Urban Driving Simulator. Proceedings of the 1st Annual Conference on Robot Learning, Mountain View, CA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A.M. (2016, January 27\u201330). The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. Proceedings of the The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Feng, H., and Fan, H. (2012, January 22\u201327). 3D weather simulation on 3D virtual earth. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351536"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2019). High-speed ship detection in SAR images based on a grid convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11101206"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.isprsjprs.2020.05.016","article-title":"HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery","volume":"167","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Shi, J., and Wei, S. (2019). Depthwise separable convolution neural network for high-speed SAR ship detection. Remote Sens., 11.","DOI":"10.3390\/rs11212483"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Ke, X., Zhan, X., Shi, J., Wei, S., Pan, D., Li, J., Su, H., and Zhou, Y. (2020). LS-SSDD-v1.0: A deep learning dataset dedicated to small ship detection from large-scale Sentinel-1 SAR images. Remote Sens., 12.","DOI":"10.3390\/rs12182997"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.isprsjprs.2021.10.010","article-title":"Balance learning for ship detection from synthetic aperture radar remote sensing imagery","volume":"182","author":"Zhang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","unstructured":"Guerra, J.C.V., Khanam, Z., Ehsan, S., Stolkin, R., and McDonald-Maier, K. (2018, January 6\u20139). Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks. Proceedings of the 2018 NASA\/ESA Conference on Adaptive Hardware and Systems (AHS). IEEE, Edinburgh, UK."},{"key":"ref_42","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_43","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 20\u201325). Repvgg: Making vgg-style convnets great again. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01352"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3193\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:58:17Z","timestamp":1760137097000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,21]]},"references-count":46,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093193"],"URL":"https:\/\/doi.org\/10.3390\/s22093193","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,4,21]]}}}