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Research Articles

Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks

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Pages 947-968 | Received 30 Apr 2019, Accepted 20 Nov 2019, Published online: 28 Nov 2019

References

  • Abrishami, H., et al., 2018. P-qrs-t localization in ecg using deep learning. 2018 IEEE EMBS international conference on Biomedical Health Informatics (BHI), March, Las Vegas, NV, 210–213.
  • Akcay, S., et al. 2018. Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery. IEEE Transactions on Information Forensics and Security, 13 (9), 2203–2215. doi:10.1109/TIFS.2018.2812196
  • Alexe, B., Deselaers, T., and Ferrari, V., 2012. Measuring the objectness of image windows. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (11), 2189–2202. doi:10.1109/TPAMI.2012.28
  • Allord, G.J., Fishburn, K.A., and Walter, J.L., 2014. Standard for the us geological survey historical topographic map collection. Vol. 3. book section B11, 11. Denver, CO: US Geological Survey
  • Amit, S.N.K.B. and Aoki, Y., 2017. Disaster detection from aerial imagery with convolutional neural network. In: 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Jawa Timur, Indonesia, 239–245.
  • Ayrey, E. and Hayes, D., 2018. The use of three-dimensional convolutional neural networks to interpret lidar for forest inventory. Remote Sensing, 10 (4), 649. Available fromhttp://www.mdpi.com/2072-4292/10/4/649
  • Ball, J.E., Anderson, D.T., and Chan, C.S., 2017. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community. Journal of Applied Remote Sensing, 11, 11–54. doi:10.1117/1.JRS.11.042609
  • Bhanu, B., 1986. Automatic target recognition: state of the art survey. IEEE Transactions on Aerospace and Electronic Systems, AES-22 (4), 364–379. doi:10.1109/TAES.1986.310772
  • Cetinic, E., Lipic, T., and Grgic, S., 2018. Fine-tuning convolutional neural networks for fine art classification. Expert Systems with Applications, 114, 107–118. Available from http://www.sciencedirect.com/science/article/pii/S0957417418304421
  • Chen, C.C., Knoblock, C.A., and Shahabi, C., 2008. Automatically and accurately conflating raster maps with orthoimagery. GeoInformatica, 12 (3), 377–410. doi:10.1007/s10707-007-0033-0
  • Chen, S., Zhan, R., and Zhang, J., 2018. Geospatial object detection in remote sensing imagery based on multiscale single-shot detector with activated semantics. Remote Sensing, 10 (6), 820. Available fromhttp://www.mdpi.com/2072-4292/10/6/820
  • Chew, R.F., et al., 2018. Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery. International Journal of Health Geographics, 17 (1), 12. doi:10.1186/s12942-018-0132-1
  • Chiang, Y.Y., et al., 2009. Automatic and accurate extraction of road intersections from raster maps. GeoInformatica, 13 (2), 121–157. doi:10.1007/s10707-008-0046-3
  • Chiang, Y.Y., Knoblock, C.A., and Chen, C.C., 2005. Automatic extraction of road intersections from raster maps. In: Proceedings of the 13th annual ACM international workshop on geographic information systems, GIS ’05. New York, NY, USA: ACM, 267–276. doi:10.1145/1097064.1097102.
  • Chiang, Y.Y., Leyk, S., and Knoblock, C.A., 2013. Efficient and robust graphics recognition from historical maps. In: Y.B. Kwon and J.M. Ogier, eds.. Graphics recognition. new trends and challenges. Berlin, Heidelberg: Springer Berlin Heidelberg, 25–35.
  • Chollet, F., 2017. Deep learning with python. 1st ed. Greenwich, CT: Manning Publications Co.
  • Deng, J., et al., 2009. Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, June. 248–255. doi:10.1037/a0016184
  • Deng, Z., et al. 2017. Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (8), 3652–3664. doi:10.1109/JSTARS.2017.2694890
  • Dickerson, N.L., 2017. Refining bounding-box regression for object localization. Thesis (Masters). Portland State University, Department of Computer Science.
  • Ding, P., et al., 2018. A light and faster regional convolutional neural network for object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 141, 208–218. Available from: http://www.sciencedirect.com/science/article/pii/S0924271618301382
  • Dodge, S. and Karam, L., 2016. Understanding how image quality affects deep neural networks. In: 2016 Eighth international conference on Quality of Multimedia Experience (QoMEX), June, Lisbon, Portugal, 1–6
  • Duan, W. and Chiang, Y.Y., 2018. Src: a fully automatic geographic feature recognition system. SIGSPATIAL Special, 9 (3), 6–7. doi:10.1145/3178392.3178396
  • Everingham, M., et al., 2010. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88 (2), 303–338. doi:10.1007/s11263-009-0275-4
  • Gao, W., et al., 2010. An improved sobel edge detection. In: 2010 3rd international conference on computer science and information technology. July, 5, Chengdu, China, 67–71.
  • Girshick, R., et al., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE conference on computer vision and pattern recognition. June, Columbus, OH, 580–587.
  • Girshick, R., 2015. Fast r-cnn. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV ‘15, Washington, DC, USA: IEEE Computer Society, 1440–1448. doi: 10.1109/ICCV.2015.169.
  • Goodfellow, I., Bengio, Y., and Courville, A., 2016. Deep learning. MIT Press. Available from: http://www.deeplearningbook.org
  • Grm, K., et al. 2018. Strengths and weaknesses of deep learning models for face recognition against image degradations. IET Biometrics, 7 (1), 81–89. doi:10.1049/iet-bmt.2017.0083
  • He, K., et al., 2016. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June, Las Vegas, NV, 770–778.
  • He, K., et al., 2017. Mask r-cnn. In: 2017 IEEE International Conference on Computer Vision (ICCV). Oct, Venice, Italy, 2980–2988.
  • Henderson, T.C., 2014. Road and road intersection extraction. New York, NY: Springer New York, 141–179.
  • Henderson, T.C. and Linton, T., 2009. Raster map image analysis. In: 2009 10th international conference on document analysis and recognition. July, Barcelona, Spain, 376–380.
  • Henry, C., Azimi, S.M., and Merkle, N., 2018. Road segmentation in SAR satellite images with deep fully-convolutional neural networks. CoRR, abs/1802.01445. Available from: http://arxiv.org/abs/1802.01445.
  • Huang, J., et al., 2017. Speed/accuracy trade-offs for modern convolutional object detectors. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July, Honolulu, HI, 3296–3297.
  • Jiang, H. and Learned-Miller, E., 2017. Face detection with the faster r-cnn. In: 2017 12th IEEE international conference on automatic Face Gesture Recognition (FG 2017). May, Washington, DC, 650–657.
  • Jo, H., Na, Y., and Song, J., 2017. Data augmentation using synthesized images for object detection. In: 2017 17th International Conference on Control, Automation and Systems (ICCAS). Oct, Jeju, Korea, 1035–1038.
  • Kanan, C. and Cottrell, G.W., 2012. Color-to-grayscale: does the method matter in image recognition?. PloS One, 7 (1), 1–7. doi:10.1371/journal.pone.0029740
  • Karahan, S., et al., 2016. How image degradations affect deep cnn-based face recognition? In: 2016 International Conference of the Biometrics Special Interest Group (BIOSIG). Sept., Darmstadt, Germany, 1–5
  • Khan, S., et al., 2018. A guide to convolutional neural networks for computer vision. Synthesis Lectures on Computer Vision, 8 (1), 1–207. doi:10.2200/S00822ED1V01Y201712COV015
  • Krahenbuhl, P. and Koltun, V., 2015. Learning to propose objects. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June, 1574–1582. doi:10.1109/CVPR.2015.7298765.
  • Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. In: F. Pereira, et al., eds.. Advances in neural information processing systems 25. Curran Associates, Inc., 1097–1105. Available from: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
  • Lin, T.Y., et al. 2014. Microsoft coco: common objects in context. In: D. Fleet, et al., eds.. Computer vision – ECCV 2014. Cham: Springer International Publishing, 740–755.
  • Liu, W., et al. 2016. Ssd: single shot multibox detector. In: B. Leibe, et al., eds. Computer vision – ECCV 2016. Cham: Springer International Publishing, 21–37.
  • Long, Y., 2016. Redefining chinese city system with emerging new data. Applied Geography, 75, 36–48. Available from http://www.sciencedirect.com/science/article/pii/S0143622816302867
  • Lu, K., et al., 2018. Lightweight convolutional neural networks for player detection and classification. Computer Vision and Image Understanding, 172, 77–87. Available from http://www.sciencedirect.com/science/article/pii/S1077314218300341
  • Lv, J.J., et al., 2017. Data augmentation for face recognition. Neurocomputing, 230, 184–196. Available from: http://www.sciencedirect.com/science/article/pii/S0925231216315016
  • Marmanis, D., et al. 2016. Deep learning earth observation classification using imagenet pretrained networks. IEEE Geoscience and Remote Sensing Letters, 13 (1), 105–109. doi:10.1109/LGRS.2015.2499239
  • Masucci, A.P., et al., 2015. Logistic growth and ergodic properties of urban forms. arXiv preprint arXiv:1504.07380.
  • Murcio, R., et al., 2015. Multifractal to monofractal evolution of the london street network. Physical Review E, 92 (6), 062130. doi:10.1103/PhysRevE.92.062130
  • Naik, Z.K. and Gandhi, M.R., 2018. A review: object detection using deep learning. International Journal of Computer Applications, 180 (29), 46–48. Available fromhttp://www.ijcaonline.org/archives/volume180/number29/29181-2018916708
  • O’Shea, K. and Nash, R., 2015. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
  • Pech-Pacheco, J.L., et al., 2000. Diatom autofocusing in brightfield microscopy: a comparative study. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000. Sept, 3, Barcelona, Spain, 314–317.
  • Peters, R.A. and Strickland, R.N., 1990. Image complexity metrics for automatic target recognizers. doi:10.1099/00221287-136-2-327
  • Pezeshk, A. and Tutwiler, R.L., 2011. Automatic feature extraction and text recognition from scanned topographic maps. IEEE Transactions on Geoscience and Remote Sensing, 49 (12), 5047–5063. doi:10.1109/TGRS.2011.2157697
  • Phung, S.L. and Bouzerdoum, A., 2007. Detecting people in images: an edge density approach. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP ’07. April, 1, I-1229–I-1232.
  • Rahnemoonfar, M. and Sheppard, C., 2017. Real-time yield estimation based on deep learning. Proceeding of SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II. doi:10.1117/12.2263097
  • Redmon, J. and Farhadi, A., 2017. Yolo9000: better, faster, stronger. In: 2017 IEEE conference on Computer Vision and Pattern Recognition (CVPR). July, 6517–6525. doi:10.1109/CVPR.2017.690.
  • Ren, S., et al. 2017. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (6), 1137–1149. doi:10.1109/TPAMI.2016.2577031
  • Salamon, J. and Bello, J.P., 2017. Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Processing Letters, 24 (3), 279–283. doi:10.1109/LSP.2017.2657381
  • Shojaee, A., Li, K., and Atluri, G., 2019. A machine learning framework for accurate functional connectome fingerprinting and an application of a siamese network. In: M.D. Schirmer, et al., eds. Connectomics in neuroimaging. Cham: Springer International Publishing, 83–94.
  • Simonyan, K. and Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Szegedy, C., et al., 2015. Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June, Boston, MA, 1–9.
  • Szegedy, C., et al., 2016. Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261.
  • Tao, Y., et al., 2016. Deep neural networks for precipitation estimation from remotely sensed information. In: 2016 IEEE Congress on Evolutionary Computation (CEC). July, Vancouver, BC, 1349–1355.
  • Uhl, J., et al., 2018. Map archive mining: visual-analytical approaches to explore large historical map collections. ISPRS International Journal of Geo-Information, 7 (4), 148. Available from: http://www.mdpi.com/2220-9964/7/4/148
  • Uhl, J.H., et al., 2017. Extracting human settlement footprint from historical topographic map series using context-based machine learning. In: 8th International Conference of Pattern Recognition Systems (ICPRS 2017). July, Madrid, Spain, 1–6.
  • Uijlings, J.R.R., et al., 2013. Selective search for object recognition. International Journal of Computer Vision, 104 (2), 154–171. doi:10.1007/s11263-013-0620-5
  • USGS, 2005. Topographic map symbols. Report. Available from: http://pubs.er.usgs.gov/publication/70039164.
  • Volokitin, A., Roig, G., and Poggio, T.A., 2017. Do deep neural networks suffer from crowding? CoRR, abs/1706.08616. Available from: http://arxiv.org/abs/1706.08616.
  • Wang, J., et al., 2015. Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine. International Journal of Remote Sensing, 36 (12), 3144–3169. doi:10.1080/01431161.2015.1054049
  • Wu, C., et al., 2017. Boosted convolutional neural networks (bcnn) for pedestrian detection. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). Mar, 540–549. doi:10.4070/kcj.2016.0445
  • Xi, Y., et al., 2018. Sr-pod: sample rotation based on principal-axis orientation distribution for data augmentation in deep object detection. Cognitive Systems Research, 52, 144–154. Available from: http://www.sciencedirect.com/science/article/pii/S1389041718301323
  • Xia, G., et al., 2017. DOTA: a large-scale dataset for object detection in aerial images. CoRR, abs/1711.10398. Available from: http://arxiv.org/abs/1711.10398.
  • Yang, Z., Dong Mu, X., and Zhao, F.A., 2018. Scene classification of remote sensing image based on deep network and multi-scale features fusion. Optik, 171, 287–293. Available from http://www.sciencedirect.com/science/article/pii/S0030402618308271
  • Zarbaf, S.E.H.A.M., et al., 2018. Vibration-based cable condition assessment: a novel application of neural networks. Engineering Structures, 177, 291–305. Available from: http://www.sciencedirect.com/science/article/pii/S0141029617339664
  • Zeiler, M.D. and Fergus, R., 2014. Visualizing and understanding convolutional networks. In: D. Fleet, et al., eds.. Computer vision – ECCV 2014. Cham: Springer International Publishing, 818–833.
  • Zitnick, C.L. and Dollár, P., 2014. Edge boxes: locating object proposals from edges. In: D. Fleet, et al., eds.. Computer vision – ECCV 2014. Cham: Springer International Publishing, 391–405.

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