{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:27:12Z","timestamp":1781612832214,"version":"3.54.5"},"reference-count":55,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,11]],"date-time":"2021-07-11T00:00:00Z","timestamp":1625961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019890","name":"Centre for Advanced Modelling and Geospatial lnformation Systems, University of Technology Sydney","doi-asserted-by":"publisher","award":["Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)"],"award-info":[{"award-number":["Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)"]}],"id":[{"id":"10.13039\/501100019890","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Urban vegetation mapping is critical in many applications, i.e., preserving biodiversity, maintaining ecological balance, and minimizing the urban heat island effect. It is still challenging to extract accurate vegetation covers from aerial imagery using traditional classification approaches, because urban vegetation categories have complex spatial structures and similar spectral properties. Deep neural networks (DNNs) have shown a significant improvement in remote sensing image classification outcomes during the last few years. These methods are promising in this domain, yet unreliable for various reasons, such as the use of irrelevant descriptor features in the building of the models and lack of quality in the labeled image. Explainable AI (XAI) can help us gain insight into these limits and, as a result, adjust the training dataset and model as needed. Thus, in this work, we explain how an explanation model called Shapley additive explanations (SHAP) can be utilized for interpreting the output of the DNN model that is designed for classifying vegetation covers. We want to not only produce high-quality vegetation maps, but also rank the input parameters and select appropriate features for classification. Therefore, we test our method on vegetation mapping from aerial imagery based on spectral and textural features. Texture features can help overcome the limitations of poor spectral resolution in aerial imagery for vegetation mapping. The model was capable of obtaining an overall accuracy (OA) of 94.44% for vegetation cover mapping. The conclusions derived from SHAP plots demonstrate the high contribution of features, such as Hue, Brightness, GLCM_Dissimilarity, GLCM_Homogeneity, and GLCM_Mean to the output of the proposed model for vegetation mapping. Therefore, the study indicates that existing vegetation mapping strategies based only on spectral characteristics are insufficient to appropriately classify vegetation covers.<\/jats:p>","DOI":"10.3390\/s21144738","type":"journal-article","created":{"date-parts":[[2021,7,11]],"date-time":"2021-07-11T22:16:48Z","timestamp":1626041808000},"page":"4738","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":102,"title":["Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI)"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1704-4670","authenticated-orcid":false,"given":"Arnick","family":"Abdollahi","sequence":"first","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Earth Observation Center, Institute of Climate Change, University Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3390\/rs70101074","article-title":"UAV remote sensing for urban vegetation mapping using random forest and texture analysis","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.rse.2008.10.005","article-title":"Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications","volume":"113","author":"Tooke","year":"2009","journal-title":"Remote Sens Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1080\/01431161.2012.714508","article-title":"Object-based urban vegetation mapping with high-resolution aerial photography as a single data source","volume":"34","author":"Li","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, H., Eziz, A., Xiao, J., Tao, S., Wang, S., Tang, Z., Zhu, J., and Fang, J. (2019). High-resolution vegetation mapping using eXtreme gradient boosting based on extensive features. Remote Sens., 11.","DOI":"10.3390\/rs11121505"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Guan, H., Su, Y., Hu, T., Chen, J., and Guo, Q. (2019). An object-based strategy for improving the accuracy of spatiotemporal satellite imagery fusion for vegetation-mapping applications. Remote Sens., 11.","DOI":"10.3390\/rs11242927"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sharma, R.C., Hara, K., and Tateishi, R. (2017). High-resolution vegetation mapping in japan by combining sentinel-2 and landsat 8 based multi-temporal datasets through machine learning and cross-validation approach. Land Degrad., 6.","DOI":"10.3390\/land6030050"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.rse.2007.02.014","article-title":"Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification","volume":"110","author":"Johansen","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.14358\/PERS.76.10.1159","article-title":"Land cover classification in a complex urban-rural landscape with QuickBird imagery","volume":"76","author":"Lu","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5006","DOI":"10.3390\/rs5105006","article-title":"Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture","volume":"5","author":"Honkavaara","year":"2013","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.3390\/rs3112529","article-title":"Multispectral remote sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments","volume":"3","author":"Laliberte","year":"2011","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2006.09.005","article-title":"Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil","volume":"106","author":"Powell","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.isprsjprs.2008.03.003","article-title":"Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses","volume":"63","author":"Aguilar","year":"2008","journal-title":"ISPRS J. Photogramm."},{"key":"ref_13","first-page":"204","article-title":"Analyzing fine-scale wetland composition using high resolution imagery and texture features","volume":"23","author":"Szantoi","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.compenvurbsys.2007.10.001","article-title":"Classification of the wildland\u2013urban interface: A comparison of pixel-and object-based classifications using high-resolution aerial photography","volume":"32","author":"Cleve","year":"2008","journal-title":"Comput. Environ. Urban. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"799","DOI":"10.14358\/PERS.72.7.799","article-title":"Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery","volume":"72","author":"Yu","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"114908","DOI":"10.1016\/j.eswa.2021.114908","article-title":"Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images","volume":"176","author":"Abdollahi","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2003.09.007","article-title":"Object-based classification of remote sensing data for change detection","volume":"58","author":"Walter","year":"2004","journal-title":"ISPRS J. Photogramm."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.landurbplan.2006.11.009","article-title":"Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery","volume":"81","author":"Mathieu","year":"2007","journal-title":"Landsc. Urban. Plan."},{"key":"ref_20","first-page":"235","article-title":"Object-oriented mapping of urban trees using Random Forest classifiers","volume":"26","author":"Puissant","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1080\/01431160902882603","article-title":"Object-oriented method for urban vegetation mapping using IKONOS imagery","volume":"31","author":"Zhang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhao, X., Zhang, X., Wu, D., and Du, X. (2019). Long time series land cover classification in China from 1982 to 2015 based on Bi-LSTM deep learning. Remote Sens., 11.","DOI":"10.3390\/rs11141639"},{"key":"ref_23","first-page":"S27","article-title":"Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms","volume":"12","author":"Otukei","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"R\u00e9jichi, S., and Cha\u00e2bane, F. (2011). SVM spatio-temporal vegetation classification using HR satellite images. Sensors, Systems, and Next-Generation Satellites, SPIE.","DOI":"10.1117\/12.898256"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wei, W., Polap, D., Li, X., Wo\u017aniak, M., and Liu, J. (2018). Study on remote sensing image vegetation classification method based on decision tree classifier. IEEE Symposium Series on Computational Intelligence (SSCI), IEEE.","DOI":"10.1109\/SSCI.2018.8628721"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2012.05.015","article-title":"Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery","volume":"124","author":"Zhang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2013.05.001","article-title":"Urban vegetation classification: Benefits of multitemporal RapidEye satellite data","volume":"136","author":"Tigges","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.rse.2004.01.016","article-title":"Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data","volume":"90","author":"Boles","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lv, Z., Hu, Y., Zhong, H., Wu, J., Li, B., and Zhao, H. (2010, January 23\u201324). Parallel k-means clustering of remote sensing images based on mapreduce. Proceedings of the International Conference on Web Information Systems and Mining, Sanya, China.","DOI":"10.1007\/978-3-642-16515-3_21"},{"key":"ref_30","first-page":"211","article-title":"An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery","volume":"50","author":"Anchang","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.asoc.2015.01.037","article-title":"Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images","volume":"30","author":"Iounousse","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_32","first-page":"1","article-title":"A survey of machine learning for big data processing","volume":"2016","author":"Qiu","year":"2016","journal-title":"EURASIP J. Adv. Signal. Process."},{"key":"ref_33","first-page":"34","article-title":"Potential and problems of multi-scale segmentation methods in remote sensing","volume":"6","author":"Schiewe","year":"2001","journal-title":"GeoBIT GIS"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1109\/JPROC.2012.2229082","article-title":"Feature mining for hyperspectral image classification","volume":"101","author":"Jia","year":"2013","journal-title":"Proc. IEEE."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/LGRS.2006.877949","article-title":"Logistic regression for feature selection and soft classification of remote sensing data","volume":"3","author":"Cheng","year":"2006","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, X., Luo, P., Loy, C.-C., and Tang, X. (2015, January 7\u201313). Semantic image segmentation via deep parsing network. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.162"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., and Alamri, A.M. (2020). An ensemble architecture of deep convolutional segnet and unet networks for building semantic segmentation from high-resolution aerial images. Geocarto Int., 1\u201313.","DOI":"10.1080\/10106049.2020.1856199"},{"key":"ref_38","unstructured":"Antwarg, L., Miller, R.M., Shapira, B., and Rokach, L. (2019). Explaining Anomalies Detected by Autoencoders Using SHAP. arXiv."},{"key":"ref_39","unstructured":"Lundberg, S., and Lee, S.-I. (2017). A unified approach to interpreting model predictions. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Matin, S.S., and Pradhan, B. (2021). Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI). Sensors, 21.","DOI":"10.3390\/s21134489"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.isprsjprs.2018.11.011","article-title":"Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings","volume":"147","author":"Chen","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hassanein, M., Lari, Z., and El-Sheimy, N. (2018). A new vegetation segmentation approach for cropped fields based on threshold detection from hue histograms. Sensors, 18.","DOI":"10.3390\/s18041253"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lin, F., Zhang, D., Huang, Y., Wang, X., and Chen, X. (2017). Detection of corn and weed species by the combination of spectral, shape and textural features. Sustainability, 9.","DOI":"10.3390\/su9081335"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lan, Z., and Liu, Y. (2018). Study on multi-scale window determination for GLCM texture description in high-resolution remote sensing image geo-analysis supported by GIS and domain knowledge. ISPRS Int. J. Geo Inf., 7.","DOI":"10.3390\/ijgi7050175"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Zhang, R., Wang, S., and Wang, F. (2018). Feature selection method based on high-resolution remote sensing images and the effect of sensitive features on classification accuracy. Sensors, 18.","DOI":"10.3390\/s18072013"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/7195432","article-title":"Classification of Very High Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks","volume":"2018","author":"Sameen","year":"2018","journal-title":"J. Sens."},{"key":"ref_47","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"64381","DOI":"10.1109\/ACCESS.2021.3075951","article-title":"Improving road semantic segmentation using generative adversarial network","volume":"9","author":"Abdollahi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"Murugan, P. (2018). Implementation of Deep Convolutional Neural Network in Multi-Class Categorical Image Classification. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"179424","DOI":"10.1109\/ACCESS.2020.3026658","article-title":"VNet: An end-to-end fully convolutional neural network for road extraction from high-resolution remote sensing data","volume":"8","author":"Abdollahi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_51","unstructured":"Chen, S. (2021). Interpretation of multi-label classification models using shapley values. arXiv."},{"key":"ref_52","unstructured":"Shrikumar, A., Greenside, P., and Kundaje, A. (2017, January 6\u201311). Learning important features through propagating activation differences. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). Why should i trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"110927","DOI":"10.1016\/j.engstruct.2020.110927","article-title":"Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach","volume":"219","author":"Mangalathu","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"101039","DOI":"10.1016\/j.ecoinf.2019.101039","article-title":"Shapley additive explanations for NO2 forecasting","volume":"56","author":"Aznarte","year":"2020","journal-title":"Ecol. Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4738\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:29:00Z","timestamp":1760164140000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4738"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,11]]},"references-count":55,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21144738"],"URL":"https:\/\/doi.org\/10.3390\/s21144738","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,11]]}}}