{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:06:46Z","timestamp":1783814806379,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T00:00:00Z","timestamp":1680912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171113"],"award-info":[{"award-number":["42171113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271112"],"award-info":[{"award-number":["42271112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XNBS1903"],"award-info":[{"award-number":["XNBS1903"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Doctoral Research Fund of Shandong Jianzhu University","award":["42171113"],"award-info":[{"award-number":["42171113"]}]},{"name":"Doctoral Research Fund of Shandong Jianzhu University","award":["42271112"],"award-info":[{"award-number":["42271112"]}]},{"name":"Doctoral Research Fund of Shandong Jianzhu University","award":["XNBS1903"],"award-info":[{"award-number":["XNBS1903"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely and rapidly mapping impervious surface area (ISA) and monitoring its spatial-temporal change pattern can deepen our understanding of the urban process. However, the complex spectral variability and spatial heterogeneity of ISA caused by the increased spatial resolution poses a great challenge to accurate ISA dynamics monitoring. This research selected Jinan City as a case study to boost ISA mapping performance through integrating the dual-attention CBAM module, SE module and focal loss function into the Deeplabv3+ model using Sentinel-2 data, and subsequently examining ISA spatial-temporal evolution using the generated annual time-series ISA data from 2017 to 2021. The experimental results demonstrated that (a) the improved Deeplabv3+ model achieved satisfactory accuracy in ISA mapping, with Precision, Recall, IoU and F1 values reaching 82.24%, 92.38%, 77.01% and 0.87, respectively. (b) In a comparison with traditional classification methods and other state-of-the-art deep learning semantic segmentation models, the proposed method performed well, qualitatively and quantitatively. (c) The time-series analysis on ISA distribution revealed that the ISA expansion in Jinan City had significant directionality from northeast to southwest from 2017 to 2021, with the number of patches as well as the degree of connectivity and aggregation increasing while the degree of fragmentation and the complexity of shape decreased. Overall, the proposed method shows great potential in generating reliable times-series ISA data and can be better served for fine urban research.<\/jats:p>","DOI":"10.3390\/rs15081976","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:19:54Z","timestamp":1681096794000},"page":"1976","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Monitoring Impervious Surface Area Dynamics in Urban Areas Using Sentinel-2 Data and Improved Deeplabv3+ Model: A Case Study of Jinan City, China"],"prefix":"10.3390","volume":"15","author":[{"given":"Jiantao","family":"Liu","sequence":"first","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunting","family":"Liu","sequence":"additional","affiliation":[{"name":"Jinan Real Estate Measuring Institute, Jinan 250001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0448-3463","authenticated-orcid":false,"given":"Xiaoqian","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Applied Arts and Science, Beijing Union University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1016\/j.asr.2021.03.039","article-title":"Investigation the Seasonality Effect on Impervious Surface Detection from Sentinel-1 and Sentinel-2 Images Using Google Earth Engine","volume":"68","author":"Todar","year":"2021","journal-title":"Adv. Space Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, W. (2019). Mapping Urban Impervious Surfaces by Using Spectral Mixture Analysis and Spectral Indices. Remote Sens., 12.","DOI":"10.3390\/rs12010094"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1080\/15481603.2017.1282414","article-title":"Semi-Automatic Mapping of Anthropogenic Impervious Surfaces in an Urban\/Suburban Area Using Landsat 8 Satellite Data","volume":"54","author":"Piyoosh","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"016502","DOI":"10.1117\/1.JRS.13.016502","article-title":"Enhanced Normalized Difference Index for Impervious Surface Area Estimation at the Plateau Basin Scale","volume":"13","author":"Chen","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tang, F., and Xu, H. (2017). Impervious Surface Information Extraction based on Hyperspectral Remote Sensing Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9060550"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, J., Li, Y., Zhang, Y., and Liu, X. (2023). Large-Scale Impervious Surface Area Mapping and Pattern Evolution of the Yellow River Delta Using Sentinel-1\/2 on the GEE. Remote Sens., 15.","DOI":"10.3390\/rs15010136"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-021-09321-6","article-title":"Fusion of Sentinel-1 and Sentinel-2 Data in Mapping the Impervious Surfaces at City Scale","volume":"193","author":"Shrestha","year":"2021","journal-title":"Environ. Monit. Assess."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hu, B., Xu, Y., Huang, X., Cheng, Q., Ding, Q., Bai, L., and Li, Y. (2021). Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10080533"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5763","DOI":"10.1080\/01431161.2017.1346403","article-title":"Subpixel Land-Cover Classification for Improved Urban Area Estimates Using Landsat","volume":"38","author":"MacLachlan","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12459","DOI":"10.3390\/rs70912459","article-title":"Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data","volume":"7","author":"Guo","year":"2015","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S0034-4257(02)00136-0","article-title":"Estimating Impervious Surface Distribution by Spectral Mixture Analysis","volume":"84","author":"Wu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2017.08.036","article-title":"An Evaluation of Monthly Impervious Surface Dynamics by Fusing Landsat and MODIS Time Series in the Pearl River Delta, China, from 2000 to 2015","volume":"201","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"17168","DOI":"10.3390\/rs71215863","article-title":"A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area","volume":"7","author":"Liu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"034515","DOI":"10.1117\/1.JRS.14.034515","article-title":"Subpixel Impervious Surface Estimation in the Nansi Lake Basin Using Random Forest Regression Combined with GF-5 Hyperspectral Data","volume":"14","author":"Liu","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.5194\/essd-12-1625-2020","article-title":"Development of a Global 30 m Impervious Surface Map Using Multisource and Multitemporal Remote Sensing Datasets with the Google Earth Engine Platform","volume":"12","author":"Zhang","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.11834\/jrs.20210382","article-title":"Research Process and Trend of High-Resolution Remote Sensing Imagery Intelligent Interpretation","volume":"25","author":"Zhang","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"McGlinchy, J., Johnson, B., Muller, B., Joseph, M., and Diaz, J. (August, January 28). Application of UNet Fully Convolutional Neural Network to Impervious Surface Segmentation in Urban Environment from High Resolution Satellite Imagery. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900453"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Parekh, J.R., Poortinga, A., Bhandari, B., Mayer, T., Saah, D., and Chishtie, F. (2021). Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13163166"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban Land-Use Mapping Using a Deep Convolutional Neural Network with High Spatial Resolution Multispectral Remote Sensing Imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, J., Feng, Q., Wang, Y., Batsaikhan, B., Gong, J., Li, Y., Liu, C., and Ma, Y. (2020). Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9090527"},{"key":"ref_22","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_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_27","first-page":"1","article-title":"DENet: Double-Encoder Network with Feature Refinement and Region Adaption for Terrain Segmentation in Polsar Images","volume":"60","author":"Zeng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"1","article-title":"MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images","volume":"19","author":"Li","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.isprsjprs.2020.09.025","article-title":"Identifying and Mapping Individual Plants in a Highly Diverse High-Elevation Ecosystem Using UAV Imagery and Deep Learning","volume":"169","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.isprsjprs.2021.02.018","article-title":"Sentinel SAR-Optical Fusion for Crop Type Mapping Using Deep Learning and Google Earth Engine","volume":"175","author":"Adrian","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Akhoondzadeh, M., Amani, M., and Mahdavi, S. (2021). Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform. Remote Sens., 13.","DOI":"10.3390\/rs13020220"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Feng, Q., Yang, J., Zhu, D., Liu, J., Guo, H., Bayartungalag, B., and Li, B. (2019). Integrating Multitemporal Sentinel-1\/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta. Remote Sens., 11.","DOI":"10.3390\/rs11091006"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, T., Su, J., Xu, Z., Luo, Y., and Li, J. (2021). Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier. Appl. Sci., 11.","DOI":"10.3390\/app11020543"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 Data for Land Cover\/Use Mapping: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.landurbplan.2016.03.009","article-title":"Mapping Urban Impervious Surface with Dual-Polarimetric SAR Data: An Improved Method","volume":"151","author":"Zhang","year":"2016","journal-title":"Landsc. Urban Plan."},{"key":"ref_36","first-page":"412","article-title":"Semantic Segmentation Method of Road Scene Based on Deeplabv3+ and Attention Mechanism","volume":"12","author":"Yanqiong","year":"2021","journal-title":"J. Meas. Sci. Instrum."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Azad, R., Asadi-Aghbolaghi, M., Fathy, M., and Escalera, S. (2020, January 23\u201328). Attention deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-66415-2_16"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Feng, Q., Zhu, D., Yang, J., and Li, B. (2019). Multisource Hyperspectral and Lidar Data Fusion for Urban Land-Use Mapping Based on a Modified Two-Branch Convolutional Neural Network. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010028"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1109\/TNSRE.2020.2973434","article-title":"Epileptic Seizure Detection in EEG Signals Using a Unified Temporal-Spectral Squeeze-and-Excitation Network","volume":"28","author":"Li","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Nam, H., Ha, J.-W., and Kim, J. (2017, January 21\u201327). Dual Attention Networks for Multimodal Reasoning and Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.232"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1109\/TNNLS.2020.3006524","article-title":"Scene Segmentation with Dual Relation-Aware Attention Network","volume":"32","author":"Fu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Lin, Z.Q., Bidart, R., Hu, X., Daya, I.B., Li, Z., Zheng, W.-S., Li, J., and Wong, A. (2020, January 13\u201319). Squeeze-and-Attention Networks for Semantic Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01308"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_47","first-page":"57","article-title":"Comparison of Machine Learning Methods for Land Use\/Land Cover Classification in the Complicated Terrain Regions","volume":"34","author":"Gu","year":"2019","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2410","DOI":"10.1080\/01431161.2018.1483090","article-title":"Land-Cover Classification Using GF-2 Images and Airborne Lidar Data Based on Random Forest","volume":"40","author":"Wu","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","first-page":"113","article-title":"Landscape Transform and Spatial Metrics for Mapping Spatiotemporal Land Cover Dynamics Using Earth Observation Data-Sets","volume":"32","author":"Singh","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Aksu, G.A., Ta\u011f\u0131l, \u015e., Musao\u011flu, N., Canatano\u011flu, E.S., and Uzun, A. (2022). Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape. Sustainability, 14.","DOI":"10.3390\/su142316030"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.asr.2018.09.009","article-title":"Degradation Monitoring in Silvo-Pastoral Systems: A Case Study of the Mediterranean Region of Turkey","volume":"63","author":"Ozcan","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0169-2046(02)00005-1","article-title":"Applying Landscape Ecological Concepts and Metrics in Sustainable Landscape Planning","volume":"59","author":"Leitao","year":"2002","journal-title":"Landsc. Urban Plan."},{"key":"ref_53","first-page":"102794","article-title":"Multi-Modal Fusion of Satellite and Street-View Images for Urban Village Classification Based on a Dual-Branch Deep Neural Network","volume":"109","author":"Chen","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/1976\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:12:36Z","timestamp":1760123556000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/1976"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,8]]},"references-count":53,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15081976"],"URL":"https:\/\/doi.org\/10.3390\/rs15081976","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,8]]}}}