{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:27:03Z","timestamp":1773772023005,"version":"3.50.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Landslides are a type of sudden and highly destructive geological hazard. Traditional detection methods often suffer from delayed response and low efficiency. In recent years, deep learning-based object detection techniques have attracted increasing attention in disaster recognition tasks, particularly transformer-based detection models, which exhibit significant advantages in global feature modeling. However, landslide targets in remote sensing imagery often present challenges such as large-scale variation, blurred boundaries, and texture interference. To address these issues, this study proposes an improved detection algorithm based on the RT-DETR-r18 framework by integrating multiple specialized modules. First, the DDC3 module is designed to enhance the recognition of fine boundaries and local textures, thereby improving the feature extraction capacity of the backbone network. Second, an Efficient Additive Attention (EAA) mechanism is introduced to suppress redundant information and strengthen the model's focus on critical regions, improving detection precision. Finally, the CGAFusion module is employed, which utilizes a triple-attention strategy to collaboratively regulate feature weights. This module enhances the model\u2019s ability to filter salient features while preserving global contextual information, leading to more accurate landslide edge detection. A dual-class dataset comprising landslides and storms is constructed from multi-source imagery for evaluation. The experimental results show that the proposed method outperforms existing models in several dimensions including mAP@0.5 and F1 score, demonstrating strong detection accuracy. Code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/sanyauChenCoder\/Landslide_02.git\" ext-link-type=\"uri\">https:\/\/github.com\/sanyauChenCoder\/Landslide_02.git<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s00371-025-04108-z","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T07:53:03Z","timestamp":1754466783000},"page":"11311-11325","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Advancing landslide recognition through multi-dimensional feature fusion and transformer architectures"],"prefix":"10.1007","volume":"41","author":[{"given":"Cong","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengwei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"issue":"1","key":"4108_CR1","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1038\/s43017-022-00373-x","volume":"4","author":"N Casagli","year":"2023","unstructured":"Casagli, N., Intrieri, E., Tofani, V., et al.: Landslide detection, monitoring and prediction with remote-sensing techniques. Nat. Rev. Earth Environ. 4(1), 51\u201364 (2023)","journal-title":"Nat. Rev. Earth Environ."},{"issue":"1","key":"4108_CR2","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s11069-024-06834-4","volume":"121","author":"S Fan","year":"2025","unstructured":"Fan, S., et al.: ETGC2-net: an enhanced transformer and graph convolution combined network for landslide detection. Nat. Hazards 121(1), 135\u2013160 (2025)","journal-title":"Nat. Hazards"},{"key":"4108_CR3","doi-asserted-by":"publisher","DOI":"10.3389\/feart.2024.1473904","volume":"12","author":"J Ren","year":"2024","unstructured":"Ren, J., et al.: Remote sensing identification of shallow landslide based on improved otsu algorithm and multi feature threshold. Front. Earth Sci. 12, 1473904 (2024)","journal-title":"Front. Earth Sci."},{"key":"4108_CR4","doi-asserted-by":"publisher","DOI":"10.3389\/feart.2023.1182145","volume":"11","author":"X Chen","year":"2023","unstructured":"Chen, X., et al.: Conv-trans dual network for landslide detection of multi-channel optical remote sensing images. Front. Earth Sci. 11, 1182145 (2023)","journal-title":"Front. Earth Sci."},{"issue":"12","key":"4108_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/rs14122884","volume":"14","author":"X Tang","year":"2022","unstructured":"Tang, X., et al.: Automatic detection of coseismic landslides using a new transformer method. Remote Sens. 14(12), 2884 (2022)","journal-title":"Remote Sens."},{"issue":"8","key":"4108_CR6","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.3390\/rs16081362","volume":"16","author":"S Gao","year":"2024","unstructured":"Gao, S., Xi, J., Li, Z., et al.: Optimal and multi-view strategic hybrid deep learning for old landslide detection in the loess plateau, Northwest China. Remote Sens. 16(8), 1362 (2024)","journal-title":"Remote Sens."},{"key":"4108_CR7","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1109\/JSTARS.2023.3332459","volume":"17","author":"Z Li","year":"2023","unstructured":"Li, Z., Li, J., Ren, L., et al.: Transformer-based dual-branch multiscale fusion network for pan-sharpening remote sensing images. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 17, 614\u2013632 (2023)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"4108_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123589","volume":"249","author":"Z Li","year":"2024","unstructured":"Li, Z., Yuan, G., Li, J.: DUCD: deep unfolding convolutional-dictionary network for pansharpening remote sensing image. Expert Syst. Appl. 249, 123589 (2024)","journal-title":"Expert Syst. Appl."},{"key":"4108_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2025.3545472)","author":"Z Li","year":"2025","unstructured":"Li, Z., Gao, Y., Yuan, G., et al.: CDME: Convolutional Dictionary Itrative Model For Pansharpening with Mixture-of-Experts. IEEE Geosci. Remote Sens. Lett. (2025). https:\/\/doi.org\/10.1109\/LGRS.2025.3545472)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"4108_CR10","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection."},{"key":"4108_CR11","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, https:\/\/doi.org\/10.1109\/cvpr.2017.690","DOI":"10.1109\/cvpr.2017.690"},{"key":"4108_CR12","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. Apr. 08, 2018, arXiv: arXiv:1804.02767. https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"4108_CR13","doi-asserted-by":"publisher","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOv4: optimal speed and accuracy of object detection. Apr. 23, 2020, arXiv: arXiv:2004.10934. https:\/\/doi.org\/10.48550\/arXiv.2004.10934","DOI":"10.48550\/arXiv.2004.10934"},{"issue":"1","key":"4108_CR14","doi-asserted-by":"publisher","first-page":"204","DOI":"10.3390\/s24010204","volume":"24","author":"T Wang","year":"2023","unstructured":"Wang, T., Zhai, Y., Li, Y., et al.: Insulator defect detection based on ML-YOLOv5 algorithm. Sensors 24(1), 204 (2023)","journal-title":"Sensors"},{"key":"4108_CR15","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada: IEEE, Jun. 2023, pp. 7464\u20137475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"4108_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-7962-2_39","author":"M Sohan","year":"2024","unstructured":"Sohan, M., Ram, T.S., Rami Reddy, C.V.: A review on YOLOv8 and Its advancements. Algorithms Intell Syst (2024). https:\/\/doi.org\/10.1007\/978-981-99-7962-2_39","journal-title":"Algorithms Intell Syst"},{"key":"4108_CR17","doi-asserted-by":"publisher","unstructured":"Wang, C.-Y., Yeh, I.-H., Liao, H.-Y.M.: YOLOv9: learning what you want to learn using programmable gradient information. Feb. 29, 2024. arXiv: arXiv:2402.13616. https:\/\/doi.org\/10.48550\/arXiv.2402.13616","DOI":"10.48550\/arXiv.2402.13616"},{"key":"4108_CR18","unstructured":"Wang, A., et al.: YOLOv10: Real-time end-to-end object detection. Oct. 30, 2024, arXiv: arXiv:2405.14458."},{"key":"4108_CR19","unstructured":"Khanam, R., Hussain, M.: YOLOv11: an overview of the key architectural enhancements. Oct. 23, (2024). arXiv: arXiv:2410.17725."},{"key":"4108_CR20","unstructured":"Tian, Y., Ye, Q., Doermann, D.: Yolov12: Attention-centric real-time object detectors (2025). arXiv preprint arXiv:2502.12524."},{"key":"4108_CR21","doi-asserted-by":"crossref","unstructured":"He, W., et al.: Object detection for medical image analysis: insights from the RT-DETR model. arXiv preprint arXiv:2501.16469 (2025).","DOI":"10.1145\/3730436.3730506"},{"issue":"1","key":"4108_CR22","doi-asserted-by":"publisher","DOI":"10.3390\/app14010429","volume":"14","author":"N Li","year":"2024","unstructured":"Li, N., et al.: Enhanced YOLOv8 with BiFPN-SimAM for precise defect detection in miniature capacitors. Appl. Sci. 14(1), 429 (2024)","journal-title":"Appl. Sci."},{"key":"4108_CR23","unstructured":"Wei, H., et al.: DWRSeg: rethinking efficient acquisition of multi-scale contextual information for real-time semantic segmentation. arXiv preprint arXiv:2212.01173 (2022)"},{"key":"4108_CR24","doi-asserted-by":"crossref","unstructured":"Ding, X., et al.: UniRepLKNet: a universal perception Large-Kernel ConvNet for audio video point cloud time-series and image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2024)","DOI":"10.1109\/CVPR52733.2024.00527"},{"key":"4108_CR25","doi-asserted-by":"crossref","unstructured":"Shaker, A., et al.: Swiftformer: efficient additive attention for transformer-based real-time mobile vision applications. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2023)","DOI":"10.1109\/ICCV51070.2023.01598"},{"issue":"21","key":"4108_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/su16219232","volume":"16","author":"Y Gu","year":"2024","unstructured":"Gu, Y., et al.: A conditionally parameterized feature fusion U-net for building change detection. Sustainability 16(21), 9232 (2024)","journal-title":"Sustainability"},{"key":"4108_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109788","volume":"143","author":"R Kou","year":"2023","unstructured":"Kou, R., Wang, C., Peng, Z., et al.: Infrared small target segmentation networks: a survey. Pattern Recogn. 143, 109788 (2023)","journal-title":"Pattern Recogn."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04108-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04108-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04108-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T14:02:50Z","timestamp":1758722570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04108-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"references-count":27,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["4108"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04108-z","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,6]]},"assertion":[{"value":"3 July 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2025","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":"Competing interests"}}]}}