{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T03:35:28Z","timestamp":1775187328621,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation Project of LREIS","award":["YPI007"],"award-info":[{"award-number":["YPI007"]}]},{"name":"Innovation Project of LREIS","award":["42071377"],"award-info":[{"award-number":["42071377"]}]},{"name":"Innovation Project of LREIS","award":["41975183"],"award-info":[{"award-number":["41975183"]}]},{"name":"Innovation Project of LREIS","award":["41875184"],"award-info":[{"award-number":["41875184"]}]},{"name":"Innovation Project of LREIS","award":["42201053"],"award-info":[{"award-number":["42201053"]}]},{"name":"National Natural Science Foundation of China","award":["YPI007"],"award-info":[{"award-number":["YPI007"]}]},{"name":"National Natural Science Foundation of China","award":["42071377"],"award-info":[{"award-number":["42071377"]}]},{"name":"National Natural Science Foundation of China","award":["41975183"],"award-info":[{"award-number":["41975183"]}]},{"name":"National Natural Science Foundation of China","award":["41875184"],"award-info":[{"award-number":["41875184"]}]},{"name":"National Natural Science Foundation of China","award":["42201053"],"award-info":[{"award-number":["42201053"]}]},{"name":"State Key Laboratory of Resources and Environmental Information System","award":["YPI007"],"award-info":[{"award-number":["YPI007"]}]},{"name":"State Key Laboratory of Resources and Environmental Information System","award":["42071377"],"award-info":[{"award-number":["42071377"]}]},{"name":"State Key Laboratory of Resources and Environmental Information System","award":["41975183"],"award-info":[{"award-number":["41975183"]}]},{"name":"State Key Laboratory of Resources and Environmental Information System","award":["41875184"],"award-info":[{"award-number":["41875184"]}]},{"name":"State Key Laboratory of Resources and Environmental Information System","award":["42201053"],"award-info":[{"award-number":["42201053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The comprehensive use of high-resolution remote sensing (HRS) images and deep learning (DL) methods can be used to further accurate urban green space (UGS) mapping. However, in the process of UGS segmentation, most of the current DL methods focus on the improvement of the model structure and ignore the spectral information of HRS images. In this paper, a multiscale attention feature aggregation network (MAFANet) incorporating feature engineering was proposed to achieve segmentation of UGS from HRS images (GaoFen-2, GF-2). By constructing a new decoder block, a bilateral feature extraction module, and a multiscale pooling attention module, MAFANet enhanced the edge feature extraction of UGS and improved segmentation accuracy. By incorporating feature engineering, including false color image and the Normalized Difference Vegetation Index (NDVI), MAFANet further distinguished UGS boundaries. The UGS labeled datasets, i.e., UGS-1 and UGS-2, were built using GF-2. Meanwhile, comparison experiments with other DL methods are conducted on UGS-1 and UGS-2 to test the robustness of the MAFANet network. We found the mean Intersection over Union (MIOU) of the MAFANet network on the UGS-1 and UGS-2 datasets was 72.15% and 74.64%, respectively; outperforming other existing DL methods. In addition, by incorporating false color image in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.64%; by incorporating vegetation index (NDVI) in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.09%; and by incorporating false color image and the vegetation index (NDVI) in UGS-1, the MIOU of MAFANet was improved from 72.15% to 74.73%. Our experimental results demonstrated that the proposed MAFANet incorporating feature engineering (false color image and NDVI) outperforms the state-of-the-art (SOTA) methods in UGS segmentation, and the false color image feature is better than the vegetation index (NDVI) for enhancing green space information representation. This study provided a practical solution for UGS segmentation and promoted UGS mapping.<\/jats:p>","DOI":"10.3390\/rs15235472","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T06:06:23Z","timestamp":1700719583000},"page":"5472","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Yong","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Software, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1766-2820","authenticated-orcid":false,"given":"Zhoupeng","family":"Ren","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Jiaxin","family":"He","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Yingfen","family":"Zhao","sequence":"additional","affiliation":[{"name":"China Centre for Resources Satellite Data and Application, Beijing 100094, China"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Wenjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kuang, W., and Dou, Y. (2020). Investigating the patterns and dynamics of urban green space in China\u2019s 70 major cities using satellite remote sensing. Remote Sens., 12.","DOI":"10.3390\/rs12121929"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.landurbplan.2015.03.014","article-title":"Effect of urban green space changes on the role of rainwater runoff reduction in Beijing, China","volume":"140","author":"Zhang","year":"2015","journal-title":"Landsc. Urban Plan."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"157521","DOI":"10.1016\/j.scitotenv.2022.157521","article-title":"Green space and loneliness: A systematic review with theoretical and methodological guidance for future research","volume":"847","author":"Hartig","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_4","first-page":"489","article-title":"An integrated methodology to assess the benefits of urban green space","volume":"334","author":"Adamec","year":"2004","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/ngeo2985","article-title":"National baselines for the Sustainable Development Goals assessed in the SDG Index and Dashboards","volume":"10","author":"Kroll","year":"2017","journal-title":"Nat. Geosci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4636","DOI":"10.1038\/s41467-022-32258-4","article-title":"Contrasting inequality in human exposure to greenspace between cities of Global North and Global South","volume":"13","author":"Chen","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.landurbplan.2010.12.013","article-title":"Spatial\u2013temporal dynamics of urban green space in response to rapid urbanization and greening policies","volume":"100","author":"Zhou","year":"2011","journal-title":"Landsc. Urban Plan."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104080","DOI":"10.1016\/j.landusepol.2019.104080","article-title":"Changing urban green spaces in Shanghai: Trends, drivers and policy implications","volume":"87","author":"Wu","year":"2019","journal-title":"Land Use Policy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113058","DOI":"10.1016\/j.rse.2022.113058","article-title":"Cross-sensor domain adaptation for high spatial resolution urban land-cover mapping: From airborne to spaceborne imagery","volume":"277","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-010-1715-x","article-title":"NDVI indicated characteristics of vegetation cover change in China\u2019s metropolises over the last three decades","volume":"179","author":"Sun","year":"2011","journal-title":"Environ. Monit. Assess."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1038\/514434c","article-title":"Open access to Earth land-cover map","volume":"514","author":"Jun","year":"2014","journal-title":"Nature"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Huang, C., Yang, J., and Jiang, P. (2018). Assessing impacts of urban form on landscape structure of urban green spaces in China using Landsat images based on Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10101569"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.ufug.2013.10.002","article-title":"The temporal trend of urban green coverage in major Chinese cities between 1990 and 2010","volume":"13","author":"Yang","year":"2014","journal-title":"Urban For. Urban Green."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, C., Yang, J., Lu, H., Huang, H., and Yu, L. (2017). Green spaces as an indicator of urban health: Evaluating its changes in 28 mega-cities. Remote Sens., 9.","DOI":"10.3390\/rs9121266"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.isprsjprs.2021.03.016","article-title":"A global context-aware and batch-independent network for road extraction from VHR satellite imagery","volume":"175","author":"Zhu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., and Zhang, L. (2021, January 10\u201317). Cvt: Introducing convolutions to vision transformers. Proceedings of the International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_19","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 Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, W., Yue, A., Shi, W., Ji, J., and Deng, R. (2019, January 5\u20137). An automatic extraction architecture of urban green space based on DeepLabv3plus semantic segmentation model. Proceedings of the International Conference on Image, Vision and Computing (ICIVC), Xiamen, China.","DOI":"10.1109\/ICIVC47709.2019.8981007"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111322","DOI":"10.1016\/j.rse.2019.111322","article-title":"Land-cover classification with high-resolution remote sensing images using transferable deep models","volume":"237","author":"Tong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112590","DOI":"10.1016\/j.rse.2021.112590","article-title":"A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities","volume":"264","author":"Cao","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112515","DOI":"10.1016\/j.rse.2021.112515","article-title":"Built-up area mapping in China from GF-3 SAR imagery based on the framework of deep learning","volume":"262","author":"Wu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_28","unstructured":"Duta, I.C., Liu, L., Zhu, F., and Shao, L. (2020). Pyramidal convolution: Rethinking convolutional neural networks for visual recognition. arXiv."},{"key":"ref_29","unstructured":"Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1080\/01431160500168686","article-title":"An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data","volume":"26","author":"Tucker","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., and Yang, K. (2018, January 18\u201322). Denseaspp for semantic segmentation in street scenes. Proceedings of the Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8\u201314). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, H., Xiong, P., Fan, H., and Sun, J. (2019, January 16\u201320). Dfanet: Deep feature aggregation for real-time semantic segmentation. Proceedings of the Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00975"},{"key":"ref_35","unstructured":"Li, G., Yun, I., Kim, J., and Kim, J. (2019). Dabnet: Depth-wise asymmetric bottleneck for real-time semantic segmentation. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mehta, S., Rastegari, M., Shapiro, L., and Hajishirzi, H. (2019, January 16\u201320). Espnetv2: A light-weight, power efficient, and general purpose convolutional neural network. Proceedings of the Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00941"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, F., Chen, Y., Li, Z., Hong, Z., Liu, J., Ma, F., Han, J., and Ding, E. (2019, January 16\u201320). Acfnet: Attentional class feature network for semantic segmentation. Proceedings of the International Conference on Computer Vision, Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00690"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/TITS.2017.2750080","article-title":"Erfnet: Efficient residual factorized convnet for real-time semantic segmentation","volume":"19","author":"Romera","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 16\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_40","first-page":"103514","article-title":"Multi-scale Feature Fusion and Transformer Network for urban green space segmentation from high-resolution remote sensing images","volume":"124","author":"Cheng","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1080\/17538947.2022.2159080","article-title":"Semantic segmentation for remote sensing images based on an AD-HRNet model","volume":"15","author":"Yang","year":"2022","journal-title":"Int. J. Digit. Earth"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/JSTARS.2019.2961634","article-title":"Multilabel remote sensing image retrieval based on fully convolutional network","volume":"13","author":"Shao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"555","DOI":"10.5194\/essd-15-555-2023","article-title":"UGS-1m: Fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework","volume":"15","author":"Shi","year":"2023","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"108422","DOI":"10.1016\/j.patcog.2021.108422","article-title":"Neighborhood linear discriminant analysis","volume":"123","author":"Zhu","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"119937","DOI":"10.1016\/j.eswa.2023.119937","article-title":"Large margin distribution multi-class supervised novelty detection","volume":"224","author":"Zhu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"113547","DOI":"10.1016\/j.rse.2023.113547","article-title":"Addressing validation challenges for TROPOMI solar-induced chlorophyll fluorescence products using tower-based measurements and an NIRv-scaled approach","volume":"290","author":"Du","year":"2023","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5472\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:28:13Z","timestamp":1760131693000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,23]]},"references-count":46,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15235472"],"URL":"https:\/\/doi.org\/10.3390\/rs15235472","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,23]]}}}