Aguilar, F.J., Rivas, J.R., Nemmaoui, A., Peñalver, A., & Aguilar, M.A. (2019). UAV-based digital terrain model generation under leaf-off conditions to support teak plantations inventories in tropical dry forests. A Case Of The Coastal Region Of Ecuador. Sensors, 19(8), 1934. https://doi.org/10.3390/s19081934
Arefi, H., d’Angelo, P., Mayer, H., & Reinartz, P. (2011). Iterative approach for efficient digital terrain model production from CARTOSAT-1 stereo images. Journal of Applied Remote Sensing, 5(1), 053527-053527. https://doi.org/10.1117/1.3595265
Axelsson, P (2000). DEM generation from laser scanner data using adaptive TIN models. International archives of photogrammetry and remote sensing, 33, 111–118.
Ayala, D., Brunet, P., Juan, R., & Navazo, I. (1985). Object representation by means of nonminimal division quadtrees and octrees. ACM Transactions on Graphics (TOG), 4(1), 41-59. https://doi.org/10.1145/3973.3975
Bauwens, S., Bartholomeus, H., Calders, K., & Lejeune, P. (2016). Forest inventory with terrestrial LiDAR: A comparison of static and hand-held mobile laser scanning. Forests, 7(6), 127. https://doi.org/10.3390/f7060127
Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, 21, 1-13. https://doi.org/10.1186/s12864-019-6413-7
Chicco, D., Warrens, M.J., & Jurman, G. (2021). The Matthews correlation coefficient (MCC) is more informative than Cohen’s Kappa and Brier score in binary classification assessment. IEEE Access, 9, 78368-78381. https://doi.org/10.1109/ACCESS.2021.3084050
Chicco, D., Tötsch, N., & Jurman, G. (2021). The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData mining, 14(1), 1-22. https://doi.org/10.1186/s13040-021-00244-z
Cho, Y.J. (2021). Weighted Intersection over Union (wIoU): A New Evaluation Metric for Image Segmentation. arXiv preprint arXiv:2107.09858. https://doi.org/10.48550/arXiv.2107.09858
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46. https://doi.org/10.1177/001316446002000104
Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of environment, 37(1), 35-46. https://doi.org/10.1016/0034-4257(91)90048-B
Dalagan, A.C., & Principe, J.A. (2021). Gis-Based Landslide Volume Estimation Using Digital Terrain Models Derived from LIDAR and Radar Systems: Case of Pidigan, Abra. International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, 46, 4. https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-109-2021
d’Alessandro, M.M., & Tebaldini, S. (2019). Digital terrain model retrieval in tropical forests through P-band SAR tomography. IEEE Transactions on Geoscience and Remote Sensing, 57(9), 6774-6781. https://doi.org/10.1109/TGRS.2019.2908517
De Diego, I.M., Redondo, A.R., Fernández, R.R., Navarro, J., & Moguerza, J.M. (2022). General Performance Score for classification problems. Applied Intelligence, 52(10), 12049-12063. https://doi.org/10.1007/s10489-021-03041-7
Elseberg, J., Borrmann, D., & Nüchter, A. (2013). One billion points in the cloud–an octree for efficient processing of 3D laser scans. ISPRS Journal of Photogrammetry and Remote Sensing, 76, 76-88. https://doi.org/10.1016/j.isprsjprs.2012.10.004
Fahle, L., Petruska, A.J., Walton, G., Brune, J.F., & Holley, E.A. (2023). Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data. Remote Sensing, 15(7), 1764. https://doi.org/10.3390/rs15071764
Gollob, C., Ritter, T., & Nothdurft, A. (2020). Comparison of 3D point clouds obtained by terrestrial laser scanning and personal laser scanning on forest inventory sample plots. Data, 5(4), 103. https://doi.org/10.3390/data5040103
Heritage, G., & Large, A. (Eds.). (2009). Laser scanning for the environmental sciences. John Wiley & Sons. https://doi.org/10.1002/9781444311952
Hossin, M., & Sulaiman, M.N. (2015). A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process, 5(2), 1. https://doi.org/10.5121/ijdkp.2015.5201
Hsieh, C.S., & Ruan, X.J. (2023). Automated Semantic Segmentation of Indoor Point Clouds from Close-Range Images with Three-Dimensional Deep Learning. Buildings, 13(2), 468. https://doi.org/10.3390/buildings13020468
Jiménez-Jiménez, S.I., Ojeda-Bustamante, W., Marcial-Pablo, M.D.J., & Enciso, J. (2021). Digital terrain models generated with low-cost UAV photogrammetry: Methodology and accuracy. ISPRS International Journal of Geo-Information, 10(5), 285. https://doi.org/10.3390/ijgi10050285
Khorrami, R.A., Darvishsefat, A.A., Tabari Kochaksaraei, M., & Shataee Jouybari, S. (2014). Potential of LIDAR data for estimation of individual tree height of Acer velutinum and Carpinus betulus. Iranian Journal of Forest, 6(2), 127-140. (In Persian)
Kraus, K., & Pfeifer, N. (1998). Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and remote Sensing, 53(4), 193-203. https://doi.org/10.1016/S0924-2716(98)00009-4
Lari, Z., Habib, A., & Kwak, E. (2012). An adaptive approach for segmentation of 3D laser point cloud. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, 38, 103-108. https://doi.org/10.5194/isprsarchives-XXXVIII-5-W12-103-2011
Lavrenov, R., Zakiev, A., & Magid, E. (2017). Automatic mapping and filtering tool: From a sensor-based occupancy grid to a 3D Gazebo octomap. In 2017 International Conference on Mechanical, System and Control Engineering. IEEE, 190-195. https://doi.org/10.1109/ICMSC.2017.7959469
Martens, J., Blut, T., & Blankenbach, J. (2023). Cross domain matching for semantic point cloud segmentation based on image segmentation and geometric reasoning. Advanced Engineering Informatics, 57, 102076. https://doi.org/10.1016/j.aei.2023.102076
Matthews, B.W. (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451. https://doi.org/10.1016/0005-2795(75)90109-9
McHugh, M.L. (2012). Interrater reliability: the kappa statistic. Biochemia medica, 22(3), 276-282.
Meagher, D. (1982). Geometric modeling using octree encoding. Computer graphics and image processing, 19(2), 129-147. https://doi.org/10.1016/0146-664X(82)90104-6
Mielcarek, M., Kamińska, A., & Stereńczak, K. (2020). Digital aerial photogrammetry (DAP) and airborne laser scanning (ALS) as sources of information about tree height: Comparisons of the accuracy of remote sensing methods for tree height estimation.
Remote Sensing, 12(11), 1808.
https://doi.org/10.3390/rs12111808
Moradi, A., Satari, M., & Momeni, M. (2018). Extracting the individual trees of urban forests from high density airborne LiDAR data. Iranian Journal of Forest, 10(1), 27-42. (In Persian)
Nocerino, E., Menna, F., Remondino, F., Toschi, I., & Rodríguez-Gonzálvez, P. (2017). Investigation of indoor and outdoor performance of two portable mobile mapping systems. In Videometrics, Range Imaging, and Applications XIV. SPIE, 10332, 125-139https://doi.org/10.1117/12.2270761
Pierdicca, R., Paolanti, M., Matrone, F., Martini, M., Morbidoni, C., Malinverni, E.S., Frontoni, E., & Lingua, A. M. (2020). Point cloud semantic segmentation using a deep learning framework for cultural heritage. Remote Sensing, 12(6), 1005. https://doi.org/10.3390/rs12061005
Powers, D.M. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv, 2010, 16061. https://doi.org/10.48550/arXiv.2010.16061
Puletti, N., Grotti, M., Masini, A., Bracci, A., & Ferrara, C. (2022). Enhancing wall-to-wall forest structure mapping through detailed co-registration of airborne and terrestrial laser scanning data in Mediterranean forests. Ecological Informatics, 67, 101497. https://doi.org/10.1016/j.ecoinf.2021.101497
Roberts, K.C., Lindsay, J.B., & Berg, A.A. (2019). An analysis of ground-point classifiers for terrestrial LiDAR. Remote Sensing, 11(16), 1915. https://doi.org/10.3390/rs11161915
Sadeghi, Y., St-Onge, B., Leblon, B., & Simard, M. (2016). Canopy height model (CHM) derived from a TanDEM-X InSAR DSM and an airborne lidar DTM in boreal forest. IEEE, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(1), 381-397. https://doi.org/10.1109/JSTARS.2015.2512230
Sammartano, G., & Spanò, A. (2018). Point clouds by SLAM-based mobile mapping systems: accuracy and geometric content validation in multisensor survey and stand-alone acquisition. Applied geomatics, 10, 317-339. https://doi.org/10.1007/s12518-018-0221-7
Song, D., Li, S., Li, S., Jin, S., Tang, D., & Tan, Y. (2022). A Method of Making DEM by Data Fusion of Multi-Device Point Cloud. In IOP Conference Series: Earth and Environmental Science. IOP Publishing, 1101(7), 072007. https://doi.org/10.1088/1755-1315/1101/7/072007
Starek, M.J., Chu, T., Mitasova, H., & Harmon, R.S. (2020). Viewshed simulation and optimization for digital terrain modelling with terrestrial laser scanning. International Journal of Remote Sensing, 41(16), 6409-6426. https://doi.org/10.1080/01431161.2020.1752952
Tharwat, A., Moemen, Y.S., & Hassanien, A.E. (2017). Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines. Journal of biomedical informatics, 68, 132-149. https://doi.org/10.1016/j.jbi.2017.03.002
Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics, 17(1), 168-192. https://doi.org/10.1016/j.aci.2018.08.003
Trier, Ø.D., Cowley, D.C., & Waldeland, A.U. (2019). Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland. Archaeological Prospection, 26(2), 165-175. https://doi.org/10.1002/arp.1731
Trochta, J., Krůček, M., Vrška, T., & Král, K. (2017). 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR.
PloS one, 12(5), e0176871.
https://doi.org/10.1371/journal.pone.0176871
Varney, N.M., Asari, V.K., & Sargent, G.C. (2016). A novel feature extraction methodology for region classification in lidar data. In Image and Signal Processing for Remote Sensing XXII. SPIE, 10004, 151-164.https://doi.org/10.1117/12.2242163
Vo, A.V., Truong-Hong, L., Laefer, D.F., & Bertolotto, M. (2015). Octree-based region growing for point cloud segmentation.
ISPRS Journal of Photogrammetry and Remote Sensing, 104, 88-100. https://doi.org/
10.1016/j.isprsjprs.2015.01.011
Vosselman, G. (2000). Slope based filtering of laser altimetry data. International archives of photogrammetry and remote sensing, 33(3), 935-942.
Wang, M., & Tseng, Y.H. (2004). Lidar data segmentation and classification based on octree structure. parameters, 1(5).
Wilhelms, J., & Van Gelder, A. (1992). Octrees for faster isosurface generation.
ACM Transactions on Graphics (TOG), 11(3), 201-227.
https://doi.org/10.1145/130881.130882
Wilkes, P., Lau, A., Disney, M., Calders, K., Burt, A., de Tanago, J.G., ... & Herold, M. (2017). Data acquisition considerations for terrestrial laser scanning of forest plots. Remote Sensing of Environment, 196, 140-153.
https://doi.org/10.1016/j.rse.2017.04.030
Xu, H., Cheng, L., Li, M., Chen, Y., & Zhong, L. (2015). Using octrees to detect changes to buildings and trees in the urban environment from airborne LiDAR data.
Remote Sensing, 7(8), 9682-9704.
https://doi.org/10.3390/rs70809682
Yang, M.Z., Huo, L., & Gao, L. (2018). Improved octree filtering algorithm of airborne LiDAR data in forest environment. Journal of Beijing Forestry University, 40(11), 102-111. https://doi.org/10.13332/j.1000-1522.20180130
Yuan, M.S., & Chen, M. (2023). Improved lazy theta∗ algorithm based on octree map for path planning of UAV. Defence Technology, 23, 8-18. https://doi.org/10.1016/j.dt.2022.01.006