USING OPALS PROGRAM SYSTEM AND SPARSE CNN MODEL IN PROCESSING AND CLASSIFYING AIRBORNE LASER SCANNING DATA
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[1] Tomljenovic, I., Höfle, B., Tiede, D., Blaschke, T.: Building extraction from airborne laser scanning data: an analysis of the state of the art. Remote Sensing 7(4), Art. no. 4 (2015). https://doi.org/10.3390/rs70403826
[2] Wehr, A., Lohr, U.: Airborne laser scanning—an introduction and overview. ISPRS J. Photogramm. Remote. Sens. 54(2), 68–82 (1999). https://doi.org/10.1016/S0924-2716(99)00011-8
[3] Pfeifer, N., Stadler, P., Briese, C.: Derivation of digital terrain models in the SCOP++ environment. In: Proceedings of OEEPE Workshop on Airborne Laserscanning and Interferometric SAR for Detailed Digital Terrain Models. Stockholm, Sweden (2001)
[4] Mallet, A., David, N.: Digital terrain models derived from airborne LiDAR data. In: Baghdadi, N., Zribi, M. (eds.) Optical Remote Sensing of Land Surface, pp. 299–319. Elsevier (2016). https://doi.org/10.1016/B978-1-78548-102-4.50007-7
[5] Roussel, J.-R., et al.: LidR: an r package for analysis of airborne laser scanning (ALS) data. Remote Sens. Environ. 251, 112061 (2020). https://doi.org/10.1016/j.rse.2020.112061
[6] Liu, A., Wang, M., Foroosh, H., Tappen, M., Pensky, M.: Sparse convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
[7] OPALS Reference documentation. https://opals.geo.tuwien.ac.at/html/nightly/ref_index.html
[8] White, J.C., et al.: A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. For. Chron. 89(06), 722–723 (2013). https://doi.org/10.5558/tfc2013-132
[9] Vosselman, G., Kessels, P., Gorte, B.: The utilisation of airborne laser scanning for mapping. Int. J. Appl. Earth Obs. Geoinf. 6(3), 177–186 (2005). https://doi.org/10.1016/j.jag.2004.10.005
[10] Pfeifer, N., Gottfried, M., Johannes, O., Karel, W.: OPALS – a framework for airborne laser scanning data analysis. ELSEVIER, vol. 45, no. Computers, Environment and Urban Systems, pp. 125–136 (2013)
[11] Mandlburger, G., Otepka, J., Karel, W., Wagner, W., Pfeifer, N.: Orientation and processing of airborne laser scanning data (OPALS)—Concept and first results of a comprehensive ALS software. In: ISPRS Workshop Laserscanning (2009)
[12] Lai, Y.: A comparison of traditional machine learning and deep learning in image recognition. J. Phys.: Conf. Ser. 1314(1), 012148 (2019). https://doi.org/10.1088/1742-6596/1314/1/012148
[13] Korotcov, A., Tkachenko, V., Russo, D.P., Ekins, S.: Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol. Pharmaceutics 14(12), 4462–4475 (2017). https://doi.org/10.1021/acs.molpharmaceut.7b00578
[14] Nikou, M., Mansourfar, G., Bagherzadeh, J.: Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management 26(4), 164–174 (2019). https://doi.org/10.1002/isaf.1459
[15] Li, N., Kähler, O., Pfeifer, N.: A comparison of deep learning methods for airborne lidar point clouds classification. IEEE J. Select. Topics in Appl. Earth Observat. Remote Sens. 14, 6467–6486 (2021). https://doi.org/10.1109/JSTARS.2021.3091389
[16] Qi, A.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Info. Process. Sys. 30 (2017)
[17] Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic Graph CNN for Learning on Point Clouds (2019)
[18] Mandlburger, G., Lehner, H., Pfeifer, N.: A Comparison of single photon and full waveform lidar. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2(W5), 397–404 (2019). https://doi.org/10.5194/isprs-annals-IV-2-W5-397-2019
[19] Li, N., Kähler, O., Pfeifer, N.: Tiles of airborne laser scanning point clouds of Vienna. Austria (2016) (2021). https://doi.org/10.5281/zenodo.4777087
[20] Otepka, J., Mandlburger, G., Karel, W.: The opals data manager –efficient data management for processing large airborne laser scanning projects. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I–3, 153–159 (2012). https://doi.org/10.5194/isprsannals-I-3-153-2012
[21] “k-d tree,” Wikipedia (2024). Accessed: 19 Aug. 2024. https://en.wikipedia.org/w/index.php?title=K-d_tree&oldid=1225343225
[22] Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234–241. Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
[23] “Module Import.” Department of geodesy and geoinfomation, TU WIEN. https://opals.geo.tuwien.ac.at/html/nightly/ModuleImport.html
[24] “python.workflows.preTiling Namespace Reference.” Department of geodesy and geoinfomation, TU WIEN. https://opals.geo.tuwien.ac.at/html/nightly/namespacepython_1_1workflows_1_1preTiling.html
[25] “Python script preCutting.” Department of geodesy and geoinfomation, TU WIEN. https://opals.geo.tuwien.ac.at/html/nightly/preCutting.html
[26] Kraus, K., Pfeifer, N.: Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J. Photogramm. Remote. Sens. 53(4), 193–203 (1998). https://doi.org/10.1016/S0924-2716(98)00009-4
[27] “Module TerrainFilter.” Department of geodesy and geoinfomation, TU WIEN. https://opals.geo.tuwien.ac.at/html/nightly/ModuleTerrainFilter.html
[28] “Module AddInfo.” Department of geodesy and geoinfomation, TU WIEN. https://opals.geo.tuwien.ac.at/html/nightly/ModuleAddInfo.html