{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T23:36:26Z","timestamp":1772667386735,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,12]],"date-time":"2018-09-12T00:00:00Z","timestamp":1536710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41431178"],"award-info":[{"award-number":["41431178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Natural Science Foundation of Guangdong Province, China","award":["2016A030311016"],"award-info":[{"award-number":["2016A030311016"]}]},{"name":"the National Administration of Surveying, Mapping and Geoinformation of China","award":["GZIT2016-A5-147"],"award-info":[{"award-number":["GZIT2016-A5-147"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Identifying and extracting building boundaries from remote sensing data has been one of the hot topics in photogrammetry for decades. The active contour model (ACM) is a robust segmentation method that has been widely used in building boundary extraction, but which often results in biased building boundary extraction due to tree and background mixtures. Although the classification methods can improve this efficiently by separating buildings from other objects, there are often ineluctable salt and pepper artifacts. In this paper, we combine the robust classification convolutional neural networks (CNN) and ACM to overcome the current limitations in algorithms for building boundary extraction. We conduct two types of experiments: the first integrates ACM into the CNN construction progress, whereas the second starts building footprint detection with a CNN and then uses ACM for post processing. Three level assessments conducted demonstrate that the proposed methods could efficiently extract building boundaries in five test scenes from two datasets. The achieved mean accuracies in terms of the F1 score for the first type (and the second type) of the experiment are 96.43 \u00b1 3.34% (95.68 \u00b1 3.22%), 88.60 \u00b1 3.99% (89.06 \u00b1 3.96%), and 91.62 \u00b11.61% (91.47 \u00b1 2.58%) at the scene, object, and pixel levels, respectively. The combined CNN and ACM solutions were shown to be effective at extracting building boundaries from high-resolution optical images and LiDAR data.<\/jats:p>","DOI":"10.3390\/rs10091459","type":"journal-article","created":{"date-parts":[[2018,9,13]],"date-time":"2018-09-13T11:46:04Z","timestamp":1536839164000},"page":"1459","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model"],"prefix":"10.3390","volume":"10","author":[{"given":"Ying","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China"},{"name":"Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China"}]},{"given":"Xinchang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Xiaoyang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou 510060, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1146-4874","authenticated-orcid":false,"given":"Qinchuan","family":"Xin","sequence":"additional","affiliation":[{"name":"Department of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China"},{"name":"Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1080\/01431161.2015.1131868","article-title":"Using point cloud data to identify, trace, and regularize the outlines of buildings","volume":"37","author":"Awrangjeb","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"196","DOI":"10.2174\/187221211797636890","article-title":"New advances in automated urban modelling from airborne laser scanning data","volume":"5","author":"Laefer","year":"2011","journal-title":"Recent Pat. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.5194\/isprsarchives-XL-3-25-2014","article-title":"Automatic building extraction from LiDAR data covering complex urban scenes","volume":"40","author":"Awrangjeb","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_4","first-page":"85","article-title":"Airborne LiDAR acquisition, post-processing and accuracy-checking for a 3D WebGIS of Copan, Honduras","volume":"5","author":"Remondino","year":"2016","journal-title":"J. Archaeol. Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3826","DOI":"10.3390\/rs70403826","article-title":"Building extraction from airborne laser scanning data: An analysis of the state of the art","volume":"7","author":"Tomljenovic","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/BF00133570","article-title":"Snakes: Active contour models","volume":"1","author":"Kass","year":"1988","journal-title":"Int. J. Comput. Vis."},{"key":"ref_7","first-page":"150","article-title":"Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours","volume":"12","author":"Ahmadi","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","unstructured":"Chan, T.F., and Vese, L.A. (2000). Image Segmentation Using Level Sets and the Piecewise-Constant Mumford-Shah Model, Kluwer Academic Publishers. UCLA CAM Report 00-14."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.imavis.2007.07.010","article-title":"A comparative study of deformable contour methods on medical image segmentation","volume":"26","author":"He","year":"2008","journal-title":"Image Vis. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.compenvurbsys.2010.04.006","article-title":"An improved snake model for automatic extraction of buildings from urban aerial images and LiDAR data","volume":"34","author":"Kabolizade","year":"2010","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1080\/01431161.2016.1148283","article-title":"Building extraction in satellite images using active contours and colour features","volume":"37","author":"Liasis","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TIP.2008.2002304","article-title":"Minimization of region-scalable fitting energy for image segmentation","volume":"17","author":"Li","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2014.2312393","article-title":"Automatic construction of 3-D building model from airborne LiDAR data through 2-D snake algorithm","volume":"53","author":"Yan","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bypina, S.K., and Rajan, K. (2015, January 26\u201331). Semi-automatic extraction of large and moderate buildings from very high-resolution satellite imagery using active contour model. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326161"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1080\/17538947.2016.1269841","article-title":"Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm","volume":"10","author":"Dai","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.isprsjprs.2013.10.004","article-title":"Results of the ISPRS benchmark on urban object detection and 3D building reconstruction","volume":"93","author":"Rottensteiner","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2013.12.002","article-title":"Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces","volume":"93","author":"Mongus","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","unstructured":"Toth, C.K., and Shan, J. (2008). Building extraction from LiDAR point clouds based on clustering techniques. Topographic Laser Ranging and Scanning: Principles and Processing, CRC Press."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3716","DOI":"10.3390\/rs6053716","article-title":"Automatic segmentation of raw LiDAR data for extraction of building roofs","volume":"6","author":"Awrangjeb","year":"2014","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"016022","DOI":"10.1117\/1.JRS.10.016022","article-title":"Automatic extraction of building boundaries using aerial LiDAR data","volume":"10","author":"Wang","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1109\/TSMC.1983.6313076","article-title":"Neocognitron: A neural network model for a mechanism of visual pattern recognition","volume":"SMC-13","author":"Fukushima","year":"1983","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_23","unstructured":"Lari, Z., and Ebadi, H. (2007, January 16\u201318). Automatic extraction of building features from high resolution satellite images using artificial neural networks. Proceedings of the ISPRS Conference on Information Extraction from SAR and Optical Data, with Emphasis on Developing Countries, Istanbul, Turkey."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_25","first-page":"58","article-title":"Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping","volume":"34","author":"Turker","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.isprsjprs.2014.04.015","article-title":"Classification of airborne laser scanning data using JointBoost","volume":"100","author":"Guo","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lodha, S.K., Kreps, E.J., Helmbold, D.P., and Fitzpatrick, D.N. (2006, January 14\u201316). Aerial LiDAR Data Classification Using Support Vector Machines (SVM). Proceedings of the 3rd International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT 2006), Chapel Hill, NC, USA.","DOI":"10.1109\/3DPVT.2006.23"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lodha, S.K., Fitzpatrick, D.M., and Helmbold, D.P. (2007, January 21\u201323). Aerial lidar data classification using AdaBoost. Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling, Montreal, QC, Canada.","DOI":"10.1109\/3DIM.2007.10"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.isprsjprs.2015.03.011","article-title":"Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2013.11.001","article-title":"Contextual classification of lidar data and building object detection in urban areas","volume":"87","author":"Niemeyer","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Vakalopoulou, M., Karantzalos, K., Komodakis, N., and Paragios, N. (2015, January 26\u201331). Building detection in very high resolution multispectral data with deep learning features. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326158"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Erhan, D., Szegedy, C., Toshev, A., and Anguelov, D. (arXiv, 2014). Scalable object detection using deep neural networks, arXiv.","DOI":"10.1109\/CVPR.2014.276"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, Y., He, B., Long, T., and Bai, X. (2017, January 23\u201328). Evaluation the performance of fully convolutional networks for building extraction compared with shallow models. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127086"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1109\/TPAMI.2017.2700300","article-title":"Convolutional oriented boundaries: From image segmentation to high-level tasks","volume":"40","author":"Maninis","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","unstructured":"Rupprecht, C., Huaroc, E., Baust, M., and Navab, N. (arXiv, 2016). Deep active contours, arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2005","DOI":"10.1109\/TGRS.2010.2103671","article-title":"A novel edge detection algorithm based on global minimization active contour model for oil slick infrared aerial image","volume":"49","author":"Jing","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","first-page":"112","article-title":"Algorithms for the reduction of the number of points required to represent a digitized line or its caricature","volume":"10","author":"Douglas","year":"1973","journal-title":"Cartogr. Int. J. Geogr. Inf. Geovisual."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/JSTARS.2009.2012488","article-title":"A comparison of evaluation techniques for building extraction from airborne laser scanning","volume":"2","author":"Rutzinger","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/9\/1459\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:20:13Z","timestamp":1760196013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/9\/1459"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,12]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["rs10091459"],"URL":"https:\/\/doi.org\/10.3390\/rs10091459","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9,12]]}}}