{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T18:12:57Z","timestamp":1769105577034,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PPMI-ITB-2023","award":["1219\/IT1.C01.1\/TA.00\/2023"],"award-info":[{"award-number":["1219\/IT1.C01.1\/TA.00\/2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Airborne laser technology produces point clouds that can be used to build 3D models of buildings. However, the work is a laborious process that could benefit from automation. Artificial intelligence (AI) has been widely used in automating building segmentation as one of the initial stages in the 3D modeling process. The algorithms with a high success rate using point clouds for automatic semantic segmentation are random forest (RF) and PointNet++, with each algorithm having its own advantages and disadvantages. However, the training and testing data to develop and test the model usually share similar characteristics. Moreover, producing a good automation model requires a lot of training data, which may become an issue for users with a small amount of training data (limited data). The aim of this research is to test the performance of the RF and PointNet++ models in different regions with limited training and testing data. We found that the RF model developed from a small amount data, in different regions between the training and testing data, performs well compared to PointNet++, yielding an OA score of 73.01% for the RF model. Furthermore, several scenarios have been used in this research to explore the capabilities of RF in several cases.<\/jats:p>","DOI":"10.3390\/ijgi13070235","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T11:23:55Z","timestamp":1719919435000},"page":"235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Performance Analysis of Random Forest Algorithm in Automatic Building Segmentation with Limited Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2068-3307","authenticated-orcid":false,"given":"Ratri","family":"Widyastuti","sequence":"first","affiliation":[{"name":"Spatial System and Cadastre Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0440-2253","authenticated-orcid":false,"given":"Deni","family":"Suwardhi","sequence":"additional","affiliation":[{"name":"Spatial System and Cadastre Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3488-9154","authenticated-orcid":false,"given":"Irwan","family":"Meilano","sequence":"additional","affiliation":[{"name":"Spatial System and Cadastre Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"}]},{"given":"Andri","family":"Hernandi","sequence":"additional","affiliation":[{"name":"Spatial System and Cadastre Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2873-0203","authenticated-orcid":false,"given":"Nabila S. E.","family":"Putri","sequence":"additional","affiliation":[{"name":"Spatial System and Cadastre Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8282-9490","authenticated-orcid":false,"given":"Asep Yusup","family":"Saptari","sequence":"additional","affiliation":[{"name":"Spatial System and Cadastre Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"}]},{"family":"Sudarman","sequence":"additional","affiliation":[{"name":"Spatial System and Cadastre Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ying, Y., Koeva, M., Kuffer, M., and Zevenbergen, J. (2023). Toward 3D Property Valuation\u2014A Review of Urban 3D Modelling Methods for Digital Twin Creation. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12010002"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"285","DOI":"10.22146\/ijg.41783","article-title":"Building Information Modeling (BIM) Utilization for 3D Fiscal Cadastre","volume":"51","author":"Hendriatiningsih","year":"2019","journal-title":"Indones. J. Geogr."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Biljecki, F., Ohori, K.A., Ledoux, H., Peters, R., and Stoter, J. (2016). Population estimation using a 3D City Model: A multi-scale country-wide study in the Netherlands. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0156808"},{"key":"ref_4","unstructured":"Ross, L., Buyuksalih, G., Buhur, S., Ross, L., B\u00fcy\u00fcksalih, G., and Baz, I. (2009, January 2\u20135). 3D City Modelling for Planning Activities, Case Study: Haydarpasa Train Station, Haydarpasa Port and Surrounding Backside Zones, Istanbul. Proceedings of the ISPRS Hannover Workshop, Hannover, Germany. Available online: https:\/\/www.researchgate.net\/publication\/237442235."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Suhari, K.T., Saptari, A.Y., Abidin, H.Z., Gunawan, P.H., Meilano, I., Hernandi, A., and Widyastuti, R. (2023). Exploring BIM-based queries for retrieving cultural heritage semantic data. Res. Sq., preprint.","DOI":"10.21203\/rs.3.rs-3812967\/v1"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Trisyanti, S.W., Suwardhi, D., Purnama, I., and Wikantika, K. (2023). A Preliminary Study of 3D Vernacular Documentation for Conservation and Evaluation: A Case Study in Keraton Kasepuhan Cirebon. Buildings, 13.","DOI":"10.3390\/buildings13020546"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/19475705.2022.2147455","article-title":"Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami","volume":"14","author":"Virtriana","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"012064","DOI":"10.1088\/1755-1315\/18\/1\/012064","article-title":"TLS for generating multi-LOD of 3D building model","volume":"18","author":"Akmalia","year":"2014","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.cag.2015.03.001","article-title":"Quantitative evaluation strategies for urban 3D model generation from remote sensing data","volume":"49","author":"Laefer","year":"2015","journal-title":"Comput. Graph."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12070","DOI":"10.3390\/s140712070","article-title":"Terrestrial and aerial laser scanning data integration using wavelet analysis for the purpose of 3D building modeling","volume":"14","author":"Kedzierski","year":"2014","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Grilli, E., Daniele, A., Bassier, M., Remondino, F., and Serafini, L. (2023). Knowledge Enhanced Neural Networks for Point Cloud Semantic Segmentation. Remote Sens., 15.","DOI":"10.3390\/rs15102590"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2020.11.011","article-title":"Automatic 3D building reconstruction from multi-view aerial images with deep learning","volume":"171","author":"Yu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2022, May 17). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Available online: http:\/\/arxiv.org\/abs\/1706.02413."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kim, T., Cho, W., Matono, A., and Kim, K.S. (2020, January 3\u20136). PinSout: Automatic 3D Indoor Space Construction from Point Clouds with Deep Learning. Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Seattle, WA, USA.","DOI":"10.1145\/3397536.3422343"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.neunet.2018.09.001","article-title":"DGCNN: A convolutional neural network over large-scale labeled graphs","volume":"108","author":"Phan","year":"2018","journal-title":"Neural Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"471","DOI":"10.5194\/isprs-archives-XLIII-B2-2021-471-2021","article-title":"Unsupervised object-based clustering in support of supervised point-based 3D point cloud classification","volume":"43","author":"Grilli","year":"2021","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.\u2014ISPRS Arch."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Buyukdemircioglu, M., Kocaman, S., and Isikdag, U. (2018). Semi-automatic 3D city model generation from large-format aerial images. Can. Hist. Rev., 7.","DOI":"10.3390\/ijgi7090339"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"653","DOI":"10.5194\/isprs-archives-XLI-B4-653-2016","article-title":"State-of-the-art of 3D national mapping in 2016","volume":"41","author":"Stoter","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.\u2014ISPRS Arch."},{"key":"ref_19","first-page":"121","article-title":"An Integrated 3D Cadastre-Malaysia as an Example","volume":"37","author":"Hassan","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"37","DOI":"10.5194\/isprs-annals-VI-4-W1-2020-37-2020","article-title":"Exploration of Open Data in Southeast Asia to Generate 3D Building Models","volume":"6","author":"Biljecki","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bello, S.A., Yu, S., Wang, C., Adam, J.M., and Li, J. (2020). Review: Deep learning on 3D point clouds. Remote Sens., 12.","DOI":"10.3390\/rs12111729"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"97495","DOI":"10.1109\/ACCESS.2021.3094127","article-title":"DALES Objects: A Large Scale Benchmark Dataset for Instance Segmentation in Aerial Lidar","volume":"9","author":"Singer","year":"2021","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103609","DOI":"10.1016\/j.regsciurbeco.2020.103609","article-title":"Comparing cities in developed and developing countries: Population, land area, building height and crowding","volume":"86","author":"Jedwab","year":"2021","journal-title":"Reg. Sci. Urban Econ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"48","DOI":"10.31127\/tuje.669566","article-title":"Classification of UAV Point Clouds by Random Forest Machine Learning Algorithm","volume":"5","author":"Zeybek","year":"2021","journal-title":"Turk. J. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ramadan, T., Islam, T.Z., Phelps, C., Pinnow, N., and Thiagarajan, J.J. (2021, January 28\u201330). Comparative Code Structure Analysis using Deep Learning for Performance Prediction. Proceedings of the 2021 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2021, Stony Brook, NY, USA.","DOI":"10.1109\/ISPASS51385.2021.00032"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_27","unstructured":"Hu, Q., Yang, B., Khalid, S., Xiao, W., Trigoni, N., and Markham, A. (2024, February 02). Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges. Available online: http:\/\/arxiv.org\/abs\/2009.03137."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"91","DOI":"10.5194\/isprs-archives-XLVI-4-W4-2021-91-2021","article-title":"ALS Point Cloud Classification using PointNet++ and KPConv with Prior Knowledge","volume":"46","author":"Kada","year":"2021","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.\u2014ISPRS Arch."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.isprsjprs.2020.05.022","article-title":"Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters","volume":"166","author":"Pan","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100001","DOI":"10.1016\/j.ophoto.2021.100001","article-title":"The Hessigheim 3D (H3D) benchmark on semantic segmentation of high-resolution 3D point clouds and textured meshes from UAV LiDAR and Multi-View-Stereo","volume":"1","author":"Laupheimer","year":"2021","journal-title":"ISPRS Open J. Photogramme-Try Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.3390\/rs4061671","article-title":"Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use","volume":"4","author":"Watts","year":"2012","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","first-page":"3327","article-title":"Comparison of UAV image and UAV lidar for construction of 3D geospatial information","volume":"31","author":"Lee","year":"2019","journal-title":"Sens. Mater."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ongsulee, P. (2017, January 22\u201324). Artificial Intelligence, Machine Learning and Deep Learning. Proceedings of the Fifteenth International Conference on ICT and Knowledge Engineering, Bangkok, Thailand.","DOI":"10.1109\/ICTKE.2017.8259629"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bulut, V. (2022). Classifying Surface Points Based on Developability Using Machine Learning. Eur. J. Sci. Technol., 171\u2013176.","DOI":"10.31590\/ejosat.1039296"},{"key":"ref_37","unstructured":"Ali, J., Khan, R., Ahmad, N., and Maqsood, I. (2024, May 06). Random Forests and Decision Trees. Available online: www.IJCSI.org."},{"key":"ref_38","unstructured":"(2024, February 15). Geosun Geosun. Available online: https:\/\/www.geosunlidar.com\/sale-13560662-geosun-gairhawk-sesries-gs-100c-lidar-scanning-system-entry-level-3d-data-collection-livox-avia-sens.html."},{"key":"ref_39","unstructured":"Chen, H.-P., Chang, K.-T., and Liu, J.-K. (2012, January 26\u201330). Stripe Adjustment of Airborne Lidar Data Using Ground points. Proceedings of the Asian Conference on Remote Sensing, Pattaya, Thailand."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"87","DOI":"10.5194\/isprs-archives-XLII-1-87-2018","article-title":"Statistical outlier detection method for airborne LiDAR data","volume":"42","author":"Carrilho","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"022022","DOI":"10.1088\/1742-6596\/1168\/2\/022022","article-title":"An Overview of Overfitting and its Solutions","volume":"1168","author":"Ying","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_42","unstructured":"(2024, February 15). Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning. Mathworks. Available online: https:\/\/www.mathworks.com\/help\/lidar\/ug\/aerial-lidar-segmentation-using-pointnet-network.html."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5194\/isprsannals-II-3-9-2014","article-title":"Shape distribution features for point cloud analysis\u2014A geometric histogram approach on multiple scales","volume":"II-3","author":"Blomley","year":"2014","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_44","first-page":"333","article-title":"Principal components analysis","volume":"12","year":"1993","journal-title":"Aten. Primaria\/Soc. Espa\u00f1ola Med. Fam. Comunitaria"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lim, G., and Doh, N. (2021). Automatic reconstruction of multi-level indoor spaces from point cloud and trajectory. Sensors, 21.","DOI":"10.3390\/s21103493"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shi, W., Ahmed, W., Li, N., Fan, W., Xiang, H., and Wang, M. (2019). Semantic geometric modelling of unstructured indoor point cloud. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010009"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.measurement.2018.10.013","article-title":"Point cloud filtering on UAV based point cloud","volume":"133","author":"Zeybek","year":"2019","journal-title":"Measurement"},{"key":"ref_48","unstructured":"Frisch, D. (2023, May 17). point2trimesh()\u2014Distance Between Point and Triangulated Surface. MATLAB Central File Exchange, 2023. Available online: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/52882-point2trimesh-distance-between-point-and-triangulated-surface."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_52","unstructured":"Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., and Grobler, J. (2013, January 23\u201327). API design for machine learning software: Experiences from the scikit-learn project. Proceedings of the ECML PKDD Workshop: Languages for Data Mining and Machine Learning, Prague, Czech Republic."},{"key":"ref_53","unstructured":"(2022, December 01). Confusion Matrix\u2014An overview\u2014ScienceDirect Topics. Available online: https:\/\/www.sciencedirect.com\/topics\/engineering\/confusion-matrix."},{"key":"ref_54","unstructured":"Yan, X., Zheng, C., Li, Z., Wang, S., and Cui, S. (2022, December 01). PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling. Available online: http:\/\/arxiv.org\/abs\/2003.00492."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Guarda, A.F.R., Rodrigues, N.M.M., and Pereira, F. (2019, January 28\u201331). Deep Learning-Based Point Cloud Coding: A Behavior and Performance Study. Proceedings of the 8th European Workshop on Visual Information Processing (EUVIP), Roma, Italy.","DOI":"10.1109\/EUVIP47703.2019.8946211"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/7\/235\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:09:02Z","timestamp":1760108942000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/7\/235"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,2]]},"references-count":55,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["ijgi13070235"],"URL":"https:\/\/doi.org\/10.3390\/ijgi13070235","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,2]]}}}