Using convolutional neural networks to identify illegal roofs from unmanned aerial vehicle images

References

• Ali, R., & Cha, Y. J. (2022). Attention-based generative adversarial network with internal damage segmentation using thermography. Automation in Construction, 141, 104412. doi:10.1016/j.autcon.2022.104412
   Web of Science ®Google Scholar
• Ali, R., Kang, D., Suh, G., & Cha, Y. J. (2021). Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures. Automation in Construction, 130, 103831. doi:10.1016/j.autcon.2021.103831
   Web of Science ®Google Scholar
• Alidoost, F., & Arefi, H. (2016). Knowledge based 3D building model recognition using convolutional neural networks from lidar and areal imageries. In L. Halounova, K. Schindler, A. Limpouch, T. Pajdla, V. Šafář, H. Mayer, … U. Stilla (Eds.), The international archives of the photogrammetry, remote sensing and spatial information sciences (pp. 833–840). Göttingen: Copernicus Publications.
   Google Scholar
• Axelsson, M., Soderman, U., Berg, A., & Lithen, T. (2018). Roof type classification using deep convolutional neural networks on low resolution photogrammetric point clouds from aerial imagery. In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1293–1297). Calgary, Canada.
   Google Scholar
• Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828. doi:10.1109/TPAMI.2013.50
   PubMed Web of Science ®Google Scholar
• Bittner, K., Körner, M., & Reinartz, P. (2019). DSM building shape refinement from combined remote sensing images based on WNET-CGANS. In 2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 783–786). Yokohama, Japan.
   Google Scholar
• Castagno, J., & Atkins, E. (2018). Roof shape classification from LiDAR and satellite image data fusion using supervised learning. Sensors, 18(11), 3960. doi:10.3390/s18113960
   PubMed Web of Science ®Google Scholar
• Cha, Y. J., Choi, W., & Büyüköztürk, O. (2017). Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378. doi:10.1111/mice.12263
   Web of Science ®Google Scholar
• Cha, Y. J., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018). Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civil and Infrastructure Engineering, 33(9), 731–747. doi:10.1111/mice.12334
   Web of Science ®Google Scholar
• Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016). Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 6232–6251. doi:10.1109/TGRS.2016.2584107
   Web of Science ®Google Scholar
• Choi, W., & Cha, Y. J. (2019). SDDNet: Real-time crack segmentation. IEEE Transactions on Industrial Electronics, 67(9), 8016–8025. doi:10.1109/TIE.2019.2945265
   Web of Science ®Google Scholar
• Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92(6), 79–97. doi:10.1016/j.isprsjprs.2014.02.013
   Google Scholar
• Dong, L., & Shan, J. (2013). A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 84(2013), 85–99. doi:10.1016/j.isprsjprs.2013.06.011
   Google Scholar
• Fruh, C., & Zakhor, A. (2003). Constructing 3D city models by merging aerial and ground views. IEEE Computer Graphics and Applications, 23(6), 52–61. doi:10.1109/MCG.2003.1242382
   Web of Science ®Google Scholar
• Gerke, M., & Xiao, J. (2014). Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification. ISPRS Journal of Photogrammetry and Remote Sensing, 87(2014), 78–92. doi:10.1016/j.isprsjprs.2013.10.011
   Google Scholar
• Getzin, S., Nuske, R. S., & Wiegand, K. (2014). Using unmanned aerial vehicles (UAV) to quantify spatial gap patterns in forests. Remote Sensing, 6(8), 6988–7004. doi:10.3390/rs6086988
   Web of Science ®Google Scholar
• Gonzalez, R. C., & Woods, R. E. (2008). Digital image processing. Upper Saddle River, New Jersey: Prentice Hall.
   Google Scholar
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, Massachusetts: MIT Press.
   Google Scholar
• Kang, B., Kim, J. G., Kim, D., & Kang, D. H. (2019). Flow estimation using drone optical imagery with non-uniform flow modeling in a controlled experimental channel. KSCE Journal of Civil Engineering, 23(4), 1891–1898. doi:10.1007/s12205-019-1438-7
   Web of Science ®Google Scholar
• Kang, D. H., & Cha, Y. J. (2018). Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging. Computer-Aided Civil and Infrastructure Engineering, 33(10), 885–902. doi:10.1111/mice.12375
   Web of Science ®Google Scholar
• Kang, D. H., & Cha, Y. J. (2022). Efficient attention-based deep encoder and decoder for automatic crack segmentation. Structural Health Monitoring, 21(5), 2190–2205. doi:10.1177/14759217211053776
   PubMed Web of Science ®Google Scholar
• Kashani, A. G., & Graettinger, A. J. (2015). Cluster-based roof covering damage detection in Ground-based lidar data. Automation in Construction, 58(2015), 19–27. doi:10.1016/j.autcon.2015.07.007
   Google Scholar
• Khaloo, A., Lattanzi, D., Cunningham, K., Dell’Andrea, R., & Riley, M. (2018). Unmanned aerial vehicle inspection of the placer river trail bridge through image-based 3D modelling. Structure and Infrastructure Engineering, 14(1), 124–136. doi:10.1080/15732479.2017.1330891
   Web of Science ®Google Scholar
• Lee, S. H., Woo, S. H., Ryu, J. R., & Choo, S. Y. (2019). Automated building occupancy authorization using BIM and UAV-based spatial information: Photogrammetric reverse engineering. Journal of Asian Architecture and Building Engineering, 18(2), 154–161. doi:10.1080/13467581.2019.1631172
   Google Scholar
• Leichtle, T., Geiß, C., Wurm, M., Lakes, T., & Taubenböck, H. (2017). Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment. International Journal of Applied Earth Observation and Geoinformation, 54(2017), 15–27. doi:10.1016/j.jag.2016.08.010
   Google Scholar
• Li, J., Huang, X., Tu, L., Zhang, T., & Wang, L. (2022). A review of building detection from very high resolution optical remote sensing images. GIScience & Remote Sensing, 59(1), 1199–1225. doi:10.1080/15481603.2022.2101727
   Web of Science ®Google Scholar
• Li, S., Zhao, X., & Zhou, G. (2019). Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network. Computer-Aided Civil and Infrastructure Engineering, 34(7), 616–634. doi:10.1111/mice.12433
   Web of Science ®Google Scholar
• Lillesand, T. M., & Kiefer, R. W. (2000). Remote sensing and image interpretation. New York: John Wiley and Sons Press.
   Google Scholar
• Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Lecture notes in computer science (pp. 21–37). Cham: Springer.
   Google Scholar
• Liu, W., Yang, M., Xie, M., Guo, Z., Li, E., Zhang, L., … Wang, D. (2019). Accurate building extraction from fused DSM and UAV images using a chain fully convolutional neural network. Remote Sensing, 11(24), 2912. doi:10.3390/rs11242912
   Web of Science ®Google Scholar
• Liu, Y., Sun, Y., Tao, S., Wang, M., Shen, Q., & Huang, J. (2021). Discovering potential illegal construction within building roofs from UAV images using semantic segmentation and object-based change detection. Photogrammetric Engineering & Remote Sensing, 87(4), 263–271. doi:10.14358/PERS.87.4.263
   Web of Science ®Google Scholar
• Liuzzi, M., Aravena Pelizari, P., Geiß, C., Masi, A., Tramutoli, V., & Taubenböck, H. (2019). A transferable remote sensing approach to classify building structural types for seismic risk analyses: The case of Val d’Agri area (Italy). Bulletin of Earthquake Engineering, 17(8), 4825–4853. doi:10.1007/s10518-019-00648-7
   Google Scholar
• Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. doi:10.1080/01431160600746456
   Web of Science ®Google Scholar
• Martinez, C., Sampedro, C., Chauhan, A., Collumeau, J. F., & Campoy, P. (2018). The Power Line Inspection Software (PoLIS): A versatile system for automating power line inspection. Engineering Applications of Artificial Intelligence, 71(2018), 293–314. doi:10.1016/j.engappai.2018.02.008
   Google Scholar
• Merwe, D. V., & Price, K. P. (2015). Harmful algal bloom characterization at ultra-high spatial and temporal resolution using small unmanned aircraft systems. Toxins, 7(4), 1065–1078. doi:10.3390/toxins7041065
   PubMed Web of Science ®Google Scholar
• Mohajeri, N., Assouline, D., Guibouda, B., Bill, A., Gudmundssonc, A., & Scartezzini, J. L. (2018). A city-scale roof shape classification using machine learning for solar energy applications. Renewable Energy, 121(2018), 81–93. doi:10.1016/j.renene.2017.12.096
   Google Scholar
• Notarangelo, N. M., Mazzariello, A., Albano, R., & Sole, A. (2021). Comparing three machine learning techniques for building extraction from a digital surface model. Applied Sciences, 11(13), 6072. doi:10.3390/app11136072
   Google Scholar
• Ps, P., & Aithal, B. H. (2022). Building footprint extraction from very high-resolution satellite images using deep learning. Journal of Spatial Science, 1–17. doi:10.1080/14498596.2022.2037473
   Web of Science ®Google Scholar
• Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
   Google Scholar
• Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in neural information processing systems (pp. 91–99). La Jolla, CA: Neural Information Processing Systems Foundation, Inc.
   Google Scholar
• Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61(2015), 85–117. doi:10.1016/j.neunet.2014.09.003
   PubMedGoogle Scholar
• Schwarzrock, J., Zacarias, I., Bazzan, A. L. C., de Araujo Fernandes, R. Q., Moreira, L. H., & de Freitas, E. P. (2018). Solving task allocation problem in multi unmanned aerial vehicles systems using Swarm intelligence. Engineering Applications of Artificial Intelligence, 72(2018), 10–20. doi:10.1016/j.engappai.2018.03.008
   Google Scholar
• Sharma, A., Liu, X., Yang, X., & Shi, D. (2017). A patch-based convolutional neural network for remote sensing image classification. Neural Networks, 95(2017), 19–28. doi:10.1016/j.neunet.2017.07.017
   PubMedGoogle Scholar
• Spencer Jr, B. F., Hoskere, V., & Narazaki, Y. (2019). Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering, 5(2019), 199–222. doi:10.1016/j.eng.2018.11.030
   Google Scholar
• Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
   Google Scholar
• Unger, J., Reich, M., & Heipke, C. (2014). UAV-based photogrammetry: Monitoring of a building zone. In F. Remondino & F. Menna (Eds.), International archives of the photogrammetry, remote sensing and spatial information sciences (pp. 601–606). Göttingen: Copernicus Publications.
   Google Scholar
• Varol, B., Yılmaz, E. Ö., Maktav, D., Bayburt, S., & Gürdal, S. (2019). Detection of illegal constructions in urban cities: Comparing LIDAR data and stereo KOMPSAT-3 images with development plans. European Journal of Remote Sensing, 52(1), 335–344. doi:10.1080/22797254.2019.1604082
   Web of Science ®Google Scholar
• Vetrivel, A., Gerke, M., Kerle, N., & Vosselman, G. (2015). Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images. ISPRS Journal of Photogrammetry and Remote Sensing, 105(2015), 61–78. doi:10.1016/j.isprsjprs.2015.03.016
   Google Scholar
• Wang, Y., Li, S., Teng, F., Lin, Y., Wang, M., & Cai, H. (2022). Improved mask R-CNN for rural building roof type recognition from uav high-resolution images: A case study in hunan province, China. Remote Sensing, 14(2), 265. doi:10.3390/rs14020265
   Web of Science ®Google Scholar
• Waqas, A., Kang, D., & Cha, Y. J. (2023). Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring. Structural Health Monitoring, 14759217231177314. doi:10.1177/14759217231177314
   Google Scholar
• Xiong, C., Li, Q., & Lu, X. (2020). Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network. Automation in Construction, 109(2020), 102994. doi:10.1016/j.autcon.2019.102994
   Google Scholar
• Xu, S., Vosselman, G., & Oude Elberink, S. (2015). Detection and classification of changes in buildings from airborne laser scanning data. Remote Sensing, 7(12), 17051–17076. doi:10.3390/rs71215867
   Web of Science ®Google Scholar
• Xu, Y., Wei, S., Bao, Y., & Li, H. (2019). Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network. Structural Control and Health Monitoring, 26(3), e2313. doi:10.1002/stc.2313
   Web of Science ®Google Scholar
• Yao, H., Qin, R., & Chen, X. (2019). Unmanned aerial vehicle for remote sensing applications—A review. Remote Sensing, 11(12), 1443. doi:10.3390/rs11121443
   Web of Science ®Google Scholar
• Yeum, C. M., Dyke, S. J., & Ramirez, J. (2018). Visual data classification in post-event building reconnaissance. Engineering Structures, 155(2018), 16–24. doi:10.1016/j.engstruct.2017.10.057
   Google Scholar
• Zhang, Q., Qin, R., Huang, X., Fang, Y., & Liu, L. (2015). Classification of ultra-high resolution orthophotos combined with DSM using a dual morphological top hat profile. Remote Sensing, 7(12), 16422–16440. doi:10.3390/rs71215840
   Web of Science ®Google Scholar
• Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., & Oliva, A. (2014). Learning deep features for scene recognition using places database. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, & K. Q. Weinberger (Eds.), Advances in neural information processing systems (pp. 487–495). Cambridge, Massachusetts: The MIT Press.
   Google Scholar