Publication Cover
Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 49, 2023 - Issue 1
935
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Large-Scale LoD2 Building Modeling using Deep Multimodal Feature Fusion

Modélisation de bâtiments LoD2 à grande échelle à l’aide de la fusion de caractéristiques multimodales profondes

&
Article: 2236243 | Received 10 Feb 2023, Accepted 28 Jun 2023, Published online: 24 Jul 2023

References

  • Akmalia, R., Setan, H., Majid, Z., Suwardhi, D., and Chong, A. 2014. “TLS for generating multi-LOD of 3D building model.” IOP Conference Series: Earth and Environmental Science, Vol. 18(No. 1): pp. 012064. doi:10.1088/1755-1315/18/1/012064.
  • Alharthy, A., and Bethel, J. 2004. “Detailed building reconstruction from airborne laser data using a moving surface method.” In 20th Congress of International Society for Photogrammetry and Remote Sensing, 213–218.
  • Alidoost, F., and Arefi, H. 2018. “A CNN-based approach for automatic building detection and recognition of roof types using a single aerial image.” PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Vol. 86(No. 5-6): pp. 235–248. doi:10.1007/s41064-018-0060-5.
  • Assouline, D., Mohajeri, N., and Scartezzini, J.-L. 2017. Building rooftop classification using random forests for large-scale PV deployment. doi:10.1117/12.2277692.
  • Bengio, Y. 2012. Deep Learning of Representations for Unsupervised and Transfer Learning (Vol. 27). http://www.causality.inf.ethz.ch/unsupervised-learning.php.
  • Bittner, K., Körner, M., Fraundorfer, F., and Reinartz, P. 2019. “Multi-task cGAN for simultaneous spaceborne DSM refinement and roof-type classification.” Remote Sensing, Vol. 11(No. 11): pp. 1262. doi:10.3390/rs11111262.
  • Buyukdemircioglu, M., Can, R., and Kocaman, S. 2021. “Deep learning based roof type classification using very high resolution aerial imagery.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLIII-B3-2021(No. B3-2021): pp. 55–60. doi:10.5194/isprs-archives-XLIII-B3-2021-55-2021.
  • Chai, T., and Draxler, R.R. 2014. “Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature.” Geoscientific Model Development, Vol. 7(No. 3): pp. 1247–1250. doi:10.5194/gmd-7-1247-2014.
  • Cohen, J. 1960. “A coefficient of agreement for nominal scales.” Educational and Psychological Measurement, Vol. 20(No. 1): pp. 37–46. doi:10.1177/001316446002000104.
  • Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., and Barnard, K. 2021. Attentional Feature Fusion. https://github.com/YimianDai/open-aff.
  • Dehbi, Y., Henn, A., Gröger, G., Stroh, V., and Plümer, L. 2021. “Robust and fast reconstruction of complex roofs with active sampling from 3D point clouds.” Transactions in GIS, Vol. 25(No. 1): pp. 112–133. doi:10.1111/tgis.12659.
  • Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., and Fei-Fei, L. 2009. Imagenet: A largescale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, 248–255. IEEE.
  • Department of Economic and Social Affairs of the United Nations. 2018. “68% of the world population projected to live in urban areas by 2050.” Available from https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html
  • Derpanis, K.G. 2010. Overview of the RANSAC Algorithm. Image Rochester NY, Vol. 4(No. 1): pp. 2–3.
  • Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. 2013. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. http://arxiv.org/abs/1310.1531.
  • Dorninger, P., and Pfeifer, N. 2008. “A comprehensive automated 3D approach for building extraction, reconstruction, and regularization from airborne laser scanning point clouds.” Sensors (Basel, Switzerland), Vol. 8(No. 11): pp. 7323–7343. doi:10.3390/s8117323.
  • Doulamis, A., and Preka, D. 2016. 3D Building Modeling in LoD2 using the CityGML Standard. https://www.researchgate.net/publication/309384841.
  • Hartley, R., and Zisserman, A. 2003. Multiple View Geometry in Computer Vision. Cambridge university press.
  • Huang, H., Brenner, C., and Sester, M. 2011. “3D building roof reconstruction from point clouds via generative models.” In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 16–24.
  • Huang, H., Brenner, C., and Sester, M. 2013. “A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 79: pp. 29–43. doi:10.1016/j.isprsjprs.2013.02.004.
  • Huang, J., Stoter, J., Peters, R., and Nan, L. 2022. City3D: Largescale building reconstruction from airborne LiDAR point clouds. Remote Sensing, Vol. 14(No. 9): pp. 2254.
  • Jiang, X., and Bunke, H. 1994. Fast segmentation of range images into planar regions by scan line grouping. Machine Vision and Applications, Vol. 7: pp. 115–122.
  • Kada, M. 2022. “3D reconstruction of simple buildings from point clouds using neural networks with continuous convolutions (CONVPOINT).” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLVIII-4/W4-2022(No. 4/W4-2022): pp. 61–66. doi:10.5194/isprs-archives-XLVIII-4-W4-2022-61-2022.
  • Krafczek, M., and Jabari, S. 2022. “Generating LOD2 city models using a hybrid-driven approach: A case study for New Brunswick urban environment.” Geomatica, Vol. 75(No. 1): pp. 130–147. doi:10.1139/geomat-2021-0016.
  • Lafarge, F., Descombes, X., Zerubia, J., and Pierrot-Deseilligny, M. 2010. “Structural approach for building reconstruction from a single DSM.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32(No. 1): pp. 135–147. doi:10.1109/TPAMI.2008.281ï.
  • LeCun, Y., Kavukcuoglu, K., and Farabet, C. 2010. Convolutional networks and applications in vision. Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 253–256. doi:10.1109/ISCAS.2010.5537907.
  • Li, L., Song, N., Sun, F., Liu, X., Wang, R., Yao, J., and Cao, S. 2022. Point2Roof: End-to-end 3D building roof modeling from airborne LiDAR point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 193: pp. 17–28.
  • Park, Y., and Guldmann, J.M. 2019. “Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach.” Computers, Environment and Urban Systems, Vol. 75: pp. 76–89. doi:10.1016/j.compenvurbsys.2019.01.004.
  • Partovi, T., Krauß, T., Arefi, H., Omidalizarandi, M., and Reinartz, P. 2014. Model-driven 3D building reconstruction based on integeration of DSM and spectral information of satellite images. In 2014 IEEE Geoscience and Remote Sensing Symposium, pp. 3168–3171. IEEE.
  • Partovi, T., Fraundorfer, F., Azimi, S., Marmanis, D., and Reinartz, P. 2017. “Roof type selection based on patch-based classification using deep learning for high resolution satellite imagery.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII-1/W1(No. 1W1): pp. 653–657. doi:10.5194/isprs-archives-XLII-1-W1-653-2017.
  • Pepe, M., Costantino, D., Alfio, V.S., Vozza, G., and Cartellino, E. 2021. “A novel method based on deep learning, gis and geomatics software for building a 3d city model from vhr satellite stereo imagery.” ISPRS International Journal of Geo-Information, Vol. 10(No. 10): pp. 697. doi:10.3390/ijgi10100697.
  • Peters, R., Dukai, B., Vitalis, S., van Liempt, J., and Stoter, J. 2022. Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands. Photogrammetric Engineering & Remote Sensing, Vol. 88(No. 3): pp. 165–170.
  • Qian, Z., Chen, M., Zhong, T., Zhang, F., Zhu, R., Zhang, Z., … and Lü, G. 2022. Deep Roof Refiner: A detail-oriented deep learning network for refined delineation of roof structure lines using satellite imagery. International Journal of Applied Earth Observation and Geoinformation, Vol. 107: pp. 102680.
  • Sarwinda, D., Paradisa, R.H., Bustamam, A., and Anggia, P. 2021. “Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer.” Procedia Computer Science, Vol. 179: pp. 423–431. doi:10.1016/j.procs.2021.01.025.
  • Shan, J., and Toth, C. K. (Eds.). 2018. Topographic laser ranging and scanning: principles and processing. CRC Press.
  • Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4–7, 2018, Proceedings, Part III 27, 270–279. Springer International Publishing.
  • Tripodi, S., Duan, L., Poujade, V., Trastour, F., Bauchet, J.P., Laurore, L., and Tarabalka, Y. 2020. “Operational pipeline for large-scale 3D reconstruction of buildings from satellite images.” In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, 445–448. IEEE.
  • United Nations. 2018. Retrieved from United Nations, Department of Economic and Social Affairs: https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html.
  • Wang, Y., Li, S., Teng, F., Lin, Y., Wang, M., and 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, Vol. 14(No. 2): pp. 265. doi:10.3390/rs14020265.
  • Zeineldin, R.A., and El-Fishawy, N.A. 2017. “A survey of RANSAC enhancements for plane detection in 3D point clouds.” Menoufia Journal of Electronic Engineering Research, Vol. 26(No. 2): pp. 519–537. doi:10.21608/mjeer.2017.63627.
  • Zhao, C., Zhang, C., Yan, Y., and Su, N. 2021. “A 3d reconstruction framework of buildings using single off-nadir satellite image.” Remote Sensing, Vol. 13(No. 21): pp. 4434. doi:10.3390/rs13214434.