{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:21:54Z","timestamp":1780446114250,"version":"3.54.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:00:00Z","timestamp":1735257600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:00:00Z","timestamp":1735257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Science and Technology Plan Project of Sichuan Province","award":["2023YFS0371"],"award-info":[{"award-number":["2023YFS0371"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s00371-024-03755-y","type":"journal-article","created":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T15:59:44Z","timestamp":1735315184000},"page":"5835-5854","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MADNet: cropland change detection network for the complex terrain and dense vegetation hilly region in the Southwestern China"],"prefix":"10.1007","volume":"41","author":[{"given":"Liangjun","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubin","family":"Xi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongliang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,27]]},"reference":[{"key":"3755_CR1","doi-asserted-by":"publisher","first-page":"8853","DOI":"10.1007\/s00371-024-03277-7","volume":"40","author":"H Li","year":"2024","unstructured":"Li, H., Ling, L., Li, Y., Zhang, W.: DFE-Net: detail feature extraction network for small object detection. Vis. Comput. 40, 8853\u20138866 (2024)","journal-title":"Vis. Comput."},{"issue":"6","key":"3755_CR2","doi-asserted-by":"publisher","first-page":"4473","DOI":"10.1007\/s00371-023-03093-5","volume":"40","author":"L Huang","year":"2024","unstructured":"Huang, L., Liao, S., Yang, W.: DC-PSENet: a novel scene text detection method integrating double ResNet-based and changed channels recursive feature pyramid[J]. Vis. Comput. 40(6), 4473\u20134491 (2024)","journal-title":"Vis. Comput."},{"key":"3755_CR3","doi-asserted-by":"publisher","first-page":"3060","DOI":"10.1109\/JSTARS.2023.3255541","volume":"16","author":"Q Shen","year":"2023","unstructured":"Shen, Q., Deng, H., Wen, X., et al.: Statistical texture learning method for monitoring abandoned suburban cropland based on high-resolution remote sensing and deep learning[J]. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 16, 3060\u20133069 (2023)","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"3755_CR4","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1016\/j.isprsjprs.2023.07.001","volume":"202","author":"W Liu","year":"2023","unstructured":"Liu, W., Lin, Y., Liu, W., et al.: An attention-based multiscale transformer network for remote sensing image change detection[J]. ISPRS J. Photogramm. Remote. Sens. 202, 599\u2013609 (2023)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"3","key":"3755_CR5","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1080\/10095020.2022.2085633","volume":"26","author":"T Bai","year":"2023","unstructured":"Bai, T., Wang, L., Yin, D., et al.: Deep learning for change detection in remote sensing: a review[J]. Geo-spatial Inf. Sci. 26(3), 262\u2013288 (2023)","journal-title":"Geo-spatial Inf. Sci."},{"key":"3755_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3374421","volume":"62","author":"J Pan","year":"2024","unstructured":"Pan, J., Bai, Y., Shu, Q., et al.: M-Swin: transformer-based multiscale feature fusion change detection network within cropland for remote sensing images. IEEE Trans. Geosci. Remote Sens. 62, 1\u201316 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3755_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3510781","volume":"62","author":"H Zhang","year":"2024","unstructured":"Zhang, H., Chen, H., Zhou, C., et al.: Bifa: Remote sensing image change detection with bitemporal feature alignment. IEEE Trans. Geosci. Remote Sens. 62, 1\u201317 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3755_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3327139","volume":"61","author":"B Jiang","year":"2023","unstructured":"Jiang, B., Wang, Z., Wang, X., et al.: VcT: Visual change transformer for remote sensing image change detection. IEEE Trans. Geosci. Remote Sens. 61, 1\u201314 (2023). https:\/\/doi.org\/10.1109\/TGRS.2023.3327139","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"3755_CR9","doi-asserted-by":"publisher","first-page":"431","DOI":"10.3390\/electronics11030431","volume":"11","author":"A Goswami","year":"2022","unstructured":"Goswami, A., Sharma, D., Mathuku, H., et al.: Change detection in remote sensing image data comparing algebraic and machine learning methods[J]. Electronics 11(3), 431 (2022)","journal-title":"Electronics"},{"issue":"8","key":"3755_CR10","doi-asserted-by":"publisher","first-page":"9774","DOI":"10.1109\/TPAMI.2023.3237896","volume":"45","author":"W Chen","year":"2023","unstructured":"Wu, C., Du, B., Zhang, L.: Fully convolutional change detection framework with generative adversarial network for unsupervised, weakly supervised and regional supervised change detection. IEEE Trans. Pattern Anal. Mach. Intell. 45(8), 9774\u20139788 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3755_CR11","doi-asserted-by":"publisher","first-page":"153559","DOI":"10.1016\/j.scitotenv.2022.153559","volume":"822","author":"J Wang","year":"2022","unstructured":"Wang, J., Bretz, M., Dewan, M.A.A., et al.: Machine learning in modelling land-use and land cover-change (LULCC): current status, challenges and prospects[J]. Sci. Total Environ. 822, 153559 (2022)","journal-title":"Sci. Total Environ."},{"key":"3755_CR12","first-page":"103294","volume":"118","author":"F Cui","year":"2023","unstructured":"Cui, F., Jiang, J.: MTSCD-Net: a network based on multi-task learning for semantic change detection of bitemporal remote sensing images[J]. Int. J. Appl. Earth Obs. Geoinf. 118, 103294 (2023)","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"3755_CR13","doi-asserted-by":"publisher","first-page":"2559","DOI":"10.1109\/JSTARS.2023.3251962","volume":"16","author":"B Sun","year":"2023","unstructured":"Sun, B., Liu, Q., Yuan, N., et al.: Spectral token guidance transformer for multisource images change detection[J]. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 16, 2559\u20132572 (2023)","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"3755_CR14","first-page":"1","volume":"60","author":"Q Li","year":"2022","unstructured":"Li, Q., Zhong, R., Du, X., et al.: TransUNetCD: a hybrid transformer network for change detection in optical remote-sensing images[J]. IEEE Trans. Geosci. Remote Sens. 60, 1\u201319 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3755_CR15","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.isprsjprs.2021.12.005","volume":"184","author":"Q Zhu","year":"2022","unstructured":"Zhu, Q., Guo, X., Deng, W., et al.: Land-use\/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery[J]. ISPRS J. Photogramm. Remote. Sens. 184, 63\u201378 (2022)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"3755_CR16","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.isprsjprs.2022.02.021","volume":"187","author":"P Chen","year":"2022","unstructured":"Chen, P., Zhang, B., Hong, D., et al.: FCCDN: feature constraint network for VHR image change detection[J]. ISPRS J. Photogramm. Remote. Sens. 187, 101\u2013119 (2022)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"3755_CR17","doi-asserted-by":"publisher","first-page":"0078","DOI":"10.34133\/remotesensing.0078","volume":"3","author":"Q Shi","year":"2023","unstructured":"Shi, Q., He, D., Liu, Z., et al.: Globe230k: A benchmark dense-pixel annotation dataset for global land cover mapping[J]. J. Remote Sens. 3, 0078 (2023)","journal-title":"J. Remote Sens."},{"key":"3755_CR18","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.isprsjprs.2021.10.015","volume":"183","author":"Z Zheng","year":"2022","unstructured":"Zheng, Z., Zhong, Y., Tian, S., et al.: ChangeMask: deep multi-task encoder-transformer-decoder architecture for semantic change detection[J]. ISPRS J. Photogramm. Remote. Sens. 183, 228\u2013239 (2022)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"3755_CR19","first-page":"1","volume":"61","author":"B Yang","year":"2023","unstructured":"Yang, B., Mao, Y., Liu, L., et al.: From trained to untrained: a novel change detection framework using randomly initialized models with spatial\u2013channel augmentation for hyperspectral images[J]. IEEE Trans. Geosci. Remote Sens. 61, 1\u201314 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3755_CR20","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.isprsjprs.2022.08.012","volume":"193","author":"S Tian","year":"2022","unstructured":"Tian, S., Zhong, Y., Zheng, Z., et al.: Large-scale deep learning based binary and semantic change detection in ultra high resolution remote sensing imagery: from benchmark datasets to urban application[J]. ISPRS J. Photogramm. Remote. Sens. 193, 164\u2013186 (2022)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"3755_CR21","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/TNNLS.2022.3172183","volume":"35","author":"LT Luppino","year":"2022","unstructured":"Luppino, L.T., Hansen, M.A., Kampffmeyer, M., et al.: Code-aligned autoencoders for unsupervised change detection in multimodal remote sensing images[J]. IEEE Trans. Neural Netw. Learn. Syst. 35, 60 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3755_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3335454","volume":"61","author":"F Luo","year":"2023","unstructured":"Luo, F., Zhou, T., Liu, J., et al.: Multiscale diff-changed feature fusion network for hyperspectral image change detection[J]. IEEE Trans. Geosci. Remote Sens. 61, 1\u201313 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3755_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-024-02030-w","volume":"132","author":"Y Qin","year":"2024","unstructured":"Qin, Y., Zhao, N., Yang, J., et al.: UrbanEvolver: function-aware urban layout regeneration[J]. Int. J. Comput. Vision 132, 1\u201320 (2024)","journal-title":"Int. J. Comput. Vision"},{"key":"3755_CR24","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TMM.2021.3120873","volume":"25","author":"X Lin","year":"2021","unstructured":"Lin, X., Sun, S., Huang, W., et al.: EAPT: efficient attention pyramid transformer for image processing[J]. IEEE Trans. Multimedia 25, 50\u201361 (2021)","journal-title":"IEEE Trans. Multimedia"},{"key":"3755_CR25","first-page":"1","volume":"61","author":"S Fang","year":"2023","unstructured":"Fang, S., Li, K., Li, Z.: Changer: Feature interaction is what you need for change detection. IEEE Trans. Geosci. Remote Sens. 61, 1\u201311 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"3755_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10095020.2022.2128902","volume":"27","author":"Q Zhu","year":"2024","unstructured":"Zhu, Q., Guo, X., Li, Z., et al.: A review of multi-class change detection for satellite remote sensing imagery[J]. Geo-spatial Inf. Sci. 27(1), 1\u201315 (2024)","journal-title":"Geo-spatial Inf. Sci."},{"issue":"S2","key":"3755_CR27","first-page":"1005","volume":"50","author":"T Li","year":"2023","unstructured":"Li, T., et al.: Detection of Farmland Change Based on Unified Attention Fusion Network[J]. Comput. Sci. 50(S2), 1005\u20131010 (2023). ((in Chinese))","journal-title":"Comput. Sci."},{"key":"3755_CR28","doi-asserted-by":"publisher","first-page":"13680","DOI":"10.1109\/JSEN.2023.3271391","volume":"23","author":"C Xu","year":"2023","unstructured":"Xu, C., Ye, Z., Mei, L., et al.: Cross-attention guided group aggregation network for cropland change detection[J]. IEEE Sens. J. 23, 13680 (2023)","journal-title":"IEEE Sens. J."},{"key":"3755_CR29","doi-asserted-by":"crossref","unstructured":"Wu, Z., Chen, Y., Meng, X., et al.: SwinUCDNet: A UNet-like Network With Union Attention for Cropland Change Detection of Aerial Images. In: 2023 30th International Conference on Geoinformatics. IEEE, pp. 1\u20137 (2023)","DOI":"10.1109\/Geoinformatics60313.2023.10247705"},{"key":"3755_CR30","doi-asserted-by":"publisher","first-page":"4297","DOI":"10.1109\/JSTARS.2022.3177235","volume":"15","author":"M Liu","year":"2022","unstructured":"Liu, M., Chai, Z., Deng, H., et al.: A CNN-transformer network with multiscale context aggregation for fine-grained cropland change detection[J]. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 15, 4297\u20134306 (2022)","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"3755_CR31","unstructured":"Zhang, X., Liu, C., Yang, D., et al.: RFAConv: Innovating Spatial Attention and Standard Convolutional Operation. arXiv 2023[J]. arXiv preprint arXiv:2304.03198."},{"key":"3755_CR32","doi-asserted-by":"publisher","first-page":"121352","DOI":"10.1016\/j.eswa.2023.121352","volume":"236","author":"KW Lau","year":"2024","unstructured":"Lau, K.W., Po, L.M., Rehman, Y.A.U.: Large separable kernel attention: rethinking the large kernel attention design in cnn[J]. Expert Syst. Appl. 236, 121352 (2024)","journal-title":"Expert Syst. Appl."},{"key":"3755_CR33","doi-asserted-by":"crossref","unstructured":"Ouyang, D., He, S., Zhang, G., et al.: Efficient multi-scale attention module with cross-spatial learning [C]. ICASSP 2023\u20132023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 1\u20135 (2023)","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"3755_CR34","doi-asserted-by":"crossref","unstructured":"Liu, W., Lu, H., Fu, H., et al.: Learning to Upsample by Learning to Sample[C]. iN: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6027\u20136037 (2023)","DOI":"10.1109\/ICCV51070.2023.00554"},{"issue":"9","key":"3755_CR35","doi-asserted-by":"publisher","first-page":"2228","DOI":"10.3390\/rs14092228","volume":"14","author":"G Wang","year":"2022","unstructured":"Wang, G., et al.: A network combining a transformer and a convolutional neural network for remote sensing image change detection. Remote Sens. 14(9), 2228 (2022). https:\/\/doi.org\/10.3390\/rs14092228","journal-title":"Remote Sens."},{"key":"3755_CR36","first-page":"1140","volume":"35","author":"MH Guo","year":"2022","unstructured":"Guo, M.H., Lu, C.Z., Hou, Q., et al.: Segnext: rethinking convolutional attention design for semantic segmentation[J]. Adv. Neural. Inf. Process. Syst. 35, 1140\u20131156 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3755_CR37","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., et al.: SegFormer: simple and efficient design for semantic segmentation with transformers[J]. Adv. Neural. Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3755_CR38","doi-asserted-by":"publisher","first-page":"5635010","DOI":"10.1109\/TGRS.2022.3227098","volume":"60","author":"H Chen","year":"2022","unstructured":"Chen, H., Pu, F., Yang, R., Tang, R., Xu, X.: RDP-Net: region detail preserving network for change detection. IEEE Trans. Geosci. Remote Sens. 60, 5635010 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3755_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3335359","volume":"61","author":"T Lei","year":"2023","unstructured":"Lei, T., Geng, X., Ning, H., Lv, Z., Gong, M., Jin, Y., Nandi, A.K.: Ultralightweight spatial\u2013spectral feature cooperation network for change detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. 61, 1\u201314 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3755_CR40","doi-asserted-by":"publisher","first-page":"928","DOI":"10.3390\/rs15040928","volume":"15","author":"H Yang","year":"2023","unstructured":"Yang, H., Chen, Y., Wu, W., Pu, S., Wu, X., Wan, Q., Dong, W.: A lightweight siamese neural network for building change detection using remote sensing images. Remote Sens. 15, 928 (2023)","journal-title":"Remote Sens."},{"key":"3755_CR41","first-page":"5602015","volume":"62","author":"J Ma","year":"2024","unstructured":"Ma, J., Duan, J., Tang, X., Zhang, X., Jiao, L.: EATDer: edge-assisted adaptive transformer detector for remote sensing change detection. IEEE Trans. Geosci. Remote Sens. 62, 5602015 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"3755_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","volume":"60","author":"H Chen","year":"2021","unstructured":"Chen, H., Qi, Z., Shi, Z.: Remote sensing image change detection with transformers[J]. IEEE Trans. Geosci. Remote Sens. 60, 1\u201314 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03755-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03755-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03755-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:50:57Z","timestamp":1747385457000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03755-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,27]]},"references-count":42,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["3755"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03755-y","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,27]]},"assertion":[{"value":"7 December 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}