{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T01:51:04Z","timestamp":1768009864532,"version":"3.49.0"},"reference-count":62,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Shaanxi Province in China","award":["2021JQ-209"],"award-info":[{"award-number":["2021JQ-209"]}]},{"name":"Natural Science Foundation of Shaanxi Province in China","award":["2020JQ-313"],"award-info":[{"award-number":["2020JQ-313"]}]},{"name":"Natural Science Foundation of Shaanxi Province in China","award":["JB210210"],"award-info":[{"award-number":["JB210210"]}]},{"name":"Natural Science Foundation of Shaanxi Province in China","award":["XJS210216"],"award-info":[{"award-number":["XJS210216"]}]},{"name":"Central Universities","award":["2021JQ-209"],"award-info":[{"award-number":["2021JQ-209"]}]},{"name":"Central Universities","award":["2020JQ-313"],"award-info":[{"award-number":["2020JQ-313"]}]},{"name":"Central Universities","award":["JB210210"],"award-info":[{"award-number":["JB210210"]}]},{"name":"Central Universities","award":["XJS210216"],"award-info":[{"award-number":["XJS210216"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the process of increasing urbanization, there is great significance in obtaining urban change information by applying land cover change detection techniques. However, these existing methods still struggle to achieve convincing performances and are insufficient for practical applications. In this paper, we constructed a new data set, named Wenzhou data set, aiming to detect the land cover changes of Wenzhou City and thus update the urban expanding geographic data. Based on this data set, we provide a new self-attention and convolution fusion network (SCFNet) for the land cover change detection of the Wenzhou data set. The SCFNet is composed of three modules, including backbone (local\u2013global pyramid feature extractor in SLGPNet), self-attention and convolution fusion module (SCFM), and residual refinement module (RRM). The SCFM combines the self-attention mechanism with convolutional layers to acquire a better feature representation. Furthermore, RRM exploits dilated convolutions with different dilation rates to refine more accurate and complete predictions over changed areas. In addition, to explore the performance of existing computational intelligence techniques in application scenarios, we selected six classical and advanced deep learning-based methods for systematic testing and comparison. The extensive experiments on the Wenzhou and Guangzhou data sets demonstrated that our SCFNet obviously outperforms other existing methods. On the Wenzhou data set, the precision, recall and F1-score of our SCFNet are all better than 85%.<\/jats:p>","DOI":"10.3390\/rs14235969","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T07:01:30Z","timestamp":1669618890000},"page":"5969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Self-Attention and Convolution Fusion Network for Land Cover Change Detection over a New Data Set in Wenzhou, China"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0315-0605","authenticated-orcid":false,"given":"Yiqun","family":"Zhu","sequence":"first","affiliation":[{"name":"Wenzhou Institute of Geotichnical Investigation and Surveying Co., Ltd., Wenzhou 325002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1166-094X","authenticated-orcid":false,"given":"Guojian","family":"Jin","sequence":"additional","affiliation":[{"name":"Wenzhou Institute of Geotichnical Investigation and Surveying Co., Ltd., Wenzhou 325002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1394-4724","authenticated-orcid":false,"given":"Tongfei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7693-051X","authenticated-orcid":false,"given":"Hanhong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9768-516X","authenticated-orcid":false,"given":"Mingyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9917-1250","authenticated-orcid":false,"given":"Shuang","family":"Liang","sequence":"additional","affiliation":[{"name":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3257-6667","authenticated-orcid":false,"given":"Jieyi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8861-4709","authenticated-orcid":false,"given":"Linqi","family":"Li","sequence":"additional","affiliation":[{"name":"Wenzhou Institute of Geotichnical Investigation and Surveying Co., Ltd., Wenzhou 325002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.scitotenv.2019.04.088","article-title":"Spatiotemporal patterns and characteristics of remotely sensed region heat islands during the rapid urbanization (1995\u20132015) of Southern China","volume":"674","author":"Yu","year":"2019","journal-title":"Sci. Total. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, F., Zhang, X., Murayama, Y., and Morimoto, T. (2020). Impacts of land cover\/use on the urban thermal environment: A comparative study of 10 megacities in China. Remote Sens., 12.","DOI":"10.3390\/rs12020307"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/S0034-4257(97)00112-0","article-title":"A comparison of four algorithms for change detection in an urban environment","volume":"63","author":"Ridd","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112993","DOI":"10.1016\/j.rse.2022.112993","article-title":"Graph-based block-level urban change detection using Sentinel-2 time series","volume":"274","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1109\/JSTARS.2012.2201135","article-title":"Multitemporal spaceborne SAR data for urban change detection in China","volume":"5","author":"Ban","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","first-page":"1","article-title":"Spatial-Spectral Attention Network Guided With Change Magnitude Image for Land Cover Change Detection Using Remote Sensing Images","volume":"60","author":"Lv","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","first-page":"1","article-title":"Sparse-constrained adaptive structure consistency-based unsupervised image regression for heterogeneous remote-sensing change detection","volume":"60","author":"Sun","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MGRS.2021.3088865","article-title":"Land cover change detection techniques: Very-high-resolution optical images: A review","volume":"10","author":"Lv","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Viana, C.M., Gir\u00e3o, I., and Rocha, J. (2019). Long-term satellite image time-series for land use\/land cover change detection using refined open source data in a rural region. Remote Sens., 11.","DOI":"10.3390\/rs11091104"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107598","DOI":"10.1016\/j.patcog.2020.107598","article-title":"Nonlocal patch similarity based heterogeneous remote sensing change detection","volume":"109","author":"Sun","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/36.843009","article-title":"Automatic analysis of the difference image for unsupervised change detection","volume":"38","author":"Bruzzone","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lv, Z., Liu, T., Zhang, P., Atli Benediktsson, J., and Chen, Y. (2018). Land cover change detection based on adaptive contextual information using bi-temporal remote sensing images. Remote Sens., 10.","DOI":"10.20944\/preprints201804.0377.v1"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ban, Y., and Yousif, O. (2016). Change detection techniques: A review. Multitemporal Remote Sens., 19\u201343.","DOI":"10.1007\/978-3-319-47037-5_2"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4124","DOI":"10.1109\/JSTARS.2017.2712119","article-title":"Multiscale morphological compressed change vector analysis for unsupervised multiple change detection","volume":"10","author":"Liu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/LGRS.2016.2536058","article-title":"Strategies combining spectral angle mapper and change vector analysis to unsupervised change detection in multispectral images","volume":"13","author":"Zhuang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10199","DOI":"10.1109\/JSTARS.2021.3115481","article-title":"Diagnostic analysis on change vector analysis methods for LCCD using remote sensing images","volume":"14","author":"ZhiYong","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised change detection in satellite images using principal component analysis and k-means clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"34425","DOI":"10.1109\/ACCESS.2019.2892648","article-title":"Novel land cover change detection method based on K-means clustering and adaptive majority voting using bitemporal remote sensing images","volume":"7","author":"Lv","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shao, P., Shi, W., He, P., Hao, M., and Zhang, X. (2016). Novel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithm. Remote Sens., 8.","DOI":"10.3390\/rs8030264"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1109\/TGRS.2008.916643","article-title":"A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure","volume":"46","author":"Bovolo","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","first-page":"1","article-title":"Landslide Inventory Mapping on VHR Images via Adaptive Region Shape Similarity","volume":"60","author":"Lv","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.proenv.2011.12.037","article-title":"Change detection using change vector analysis from Landsat TM images in Wuhan","volume":"11","author":"Xiaolu","year":"2011","journal-title":"Procedia Environ. Sci."},{"key":"ref_25","first-page":"839","article-title":"Land use and land cover change detection through remote sensing & GIS technology: Case study of Pathankot and Dhar Kalan Tehsils, Punjab","volume":"1","author":"Singh","year":"2011","journal-title":"Int. J. Geomat. Geosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.quaint.2020.04.048","article-title":"Qualitative and quantitative analysis of topographically derived CVA algorithms using MODIS and Landsat-8 data over Western Himalayas, India","volume":"575","author":"Singh","year":"2021","journal-title":"Quat. Int."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.isprsjprs.2006.09.004","article-title":"Multiple support vector machines for land cover change detection: An application for mapping urban extensions","volume":"61","author":"Nemmour","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1109\/LGRS.2020.2998684","article-title":"Local histogram-based analysis for detecting land cover change using VHR remote sensing images","volume":"18","author":"Lv","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","first-page":"1","article-title":"Landslide Inventory Mapping Method Based on Adaptive Histogram-Mean Distance with Bitemporal VHR Aerial Images","volume":"19","author":"Liu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/MGRS.2021.3063465","article-title":"Change detection from very-high-spatial-resolution optical remote sensing images: Methods, applications, and future directions","volume":"9","author":"Wen","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shafique, A., Cao, G., Khan, Z., Asad, M., and Aslam, M. (2022). Deep learning-based change detection in remote sensing images: A review. Remote Sens., 14.","DOI":"10.3390\/rs14040871"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4257","DOI":"10.1109\/TNNLS.2021.3056238","article-title":"Commonality Autoencoder: Learning Common Features for Change Detection from Heterogeneous Images","volume":"33","author":"Wu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_34","first-page":"1","article-title":"A Spectral and Spatial Attention Network for Change Detection in Hyperspectral Images","volume":"60","author":"Gong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TGRS.2020.2996064","article-title":"Iterative training sample expansion to increase and balance the accuracy of land classification from VHR imagery","volume":"59","author":"Lv","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wu, Y., Mu, G., Qin, C., Miao, Q., Ma, W., and Zhang, X. (2020). Semi-supervised hyperspectral image classification via spatial-regulated self-training. Remote Sens., 12.","DOI":"10.3390\/rs12010159"},{"key":"ref_37","first-page":"1","article-title":"Two-Path Aggregation Attention Network with Quad-Patch Data Augmentation for Few-Shot Scene Classification","volume":"60","author":"Gong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, J., Lin, S., Ding, L., and Bruzzone, L. (2020). Multi-scale context aggregation for semantic segmentation of remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12040701"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jiang, H., Peng, M., Zhong, Y., Xie, H., Hao, Z., Lin, J., Ma, X., and Hu, X. (2022). A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14071552"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shi, W., Zhang, M., Zhang, R., Chen, S., and Zhan, Z. (2020). Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhao, J., Gong, M., Liu, J., and Jiao, L. (2014, January 6\u201311). Deep learning to classify difference image for image change detection. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889510"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/LGRS.2018.2889307","article-title":"Landslide inventory mapping from bitemporal images using deep convolutional neural networks","volume":"16","author":"Lei","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","unstructured":"Daudt, R.C., Le Saux, B., and Boulch, A. (2018, January 7\u201310). Fully convolutional siamese networks for change detection. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set","volume":"57","author":"Ji","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shen, L., Lu, Y., Chen, H., Wei, H., Xie, D., Yue, J., Chen, R., Lv, S., and Jiang, B. (2021). S2Looking: A satellite side-looking dataset for building change detection. Remote Sens., 13.","DOI":"10.3390\/rs13245094"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4816","DOI":"10.1109\/JSTARS.2021.3077545","article-title":"AGCDetNet: An attention-guided network for building change detection in high-resolution remote sensing images","volume":"14","author":"Song","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","first-page":"1","article-title":"Building Change Detection for VHR Remote Sensing Images via Local\u2013Global Pyramid Network and Cross-Task Transfer Learning Strategy","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","article-title":"Change detection based on deep siamese convolutional network for optical aerial images","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yang, L., Chen, Y., Song, S., Li, F., and Huang, G. (2021). Deep Siamese networks based change detection with remote sensing images. Remote Sens., 13.","DOI":"10.3390\/rs13173394"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","article-title":"A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images","volume":"166","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5891","DOI":"10.1109\/TGRS.2020.3011913","article-title":"SemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images","volume":"59","author":"Peng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"108717","DOI":"10.1016\/j.patcog.2022.108717","article-title":"HFA-Net: High frequency attention siamese network for building change detection in VHR remote sensing images","volume":"129","author":"Zheng","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_55","first-page":"1","article-title":"SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images","volume":"19","author":"Fang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","article-title":"Remote sensing image change detection with transformers","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.isprsjprs.2021.10.015","article-title":"ChangeMask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection","volume":"183","author":"Zheng","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","first-page":"1","article-title":"SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhou, S., Dong, Z., and Wang, G. (2022). Machine-Learning-Based Change Detection of Newly Constructed Areas from GF-2 Imagery in Nanjing, China. Remote Sens., 14.","DOI":"10.3390\/rs14122874"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Pan, X., Ge, C., Lu, R., Song, S., Chen, G., Huang, Z., and Huang, G. (2022, January 19\u201322). On the integration of self-attention and convolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52688.2022.00089"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Shao, Z., Tang, P., Wang, Z., Saleem, N., Yam, S., and Sommai, C. (2020). BRRNet: A fully convolutional neural network for automatic building extraction from high-resolution remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12061050"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lebedev, M., Vizilter, Y.V., Vygolov, O., Knyaz, V., and Rubis, A.Y. (2018, January 4\u20137). Change detection in remote sensing images using conditional adversarial networks. Proceedings of the ISPRS TC II Mid-Term Symposium \u201cTowards Photogrammetry 2020\u201d, Riva del Garda, Italy.","DOI":"10.5194\/isprs-archives-XLII-2-565-2018"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/5969\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:26:45Z","timestamp":1760146005000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/5969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,25]]},"references-count":62,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14235969"],"URL":"https:\/\/doi.org\/10.3390\/rs14235969","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,25]]}}}