{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T05:27:10Z","timestamp":1768541230842,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Illegal waste dumping not only encroaches on land resources but also threatens the health of the surrounding residents. The traditional artificial waste monitoring solution requires professional workers to conduct field investigations. This solution not only requires high labor resources and economic costs but also demands a prolonged cycle for updating the monitoring status. Therefore, some scholars use deep learning to achieve automatic waste detection from satellite imagery. However, relevant models cannot effectively capture multi-scale features and enhance key information. To further bolster the monitoring efficiency of urban solid waste, we propose a novel multi-scale context fusion network for solid waste detection in remote sensing images, which can quickly collect waste distribution information in a large-scale range. Specifically, it introduces a new guidance fusion module that leverages spatial attention mechanisms alongside the use of large kernel convolutions. This module helps guide shallow features to retain useful details and adaptively adjust multi-scale spatial receptive fields. Meanwhile, it proposes a novel context awareness module based on heterogeneous convolutions and gating mechanisms. This module can effectively capture richer context information and provide anisotropic features for waste localization. In addition, it also designs an effective multi-scale interaction module based on cross-guidance and coordinate perception. This module not only enhances critical information but also fuses multi-scale semantic features. To substantiate the effectiveness of our approach, we conducted a series of comprehensive experiments on two representative urban waste detection datasets. The outcomes of relevant experiments indicate that our methodology surpasses other deep learning models. As plug-and-play components, these modules can be flexibly integrated into existing object detection frameworks, thereby delivering consistent enhancements in performance. Overall, we provide an efficient solution for monitoring illegal waste dumping, which contributes to promoting eco-friendly development.<\/jats:p>","DOI":"10.3390\/rs16193595","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T09:46:20Z","timestamp":1727343980000},"page":"3595","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-Scale Context Fusion Network for Urban Solid Waste Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4483-9126","authenticated-orcid":false,"given":"Yangke","family":"Li","sequence":"first","affiliation":[{"name":"School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Xinman","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1007\/s43615-024-00346-w","article-title":"Urbanization and benefit of integration circular economy into waste management in Indonesia: A review","volume":"4","author":"Wikurendra","year":"2024","journal-title":"Circ. Econ. Sustain."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"120808","DOI":"10.1016\/j.jclepro.2020.120808","article-title":"Analysis of the factors that affect the production of municipal solid waste in China","volume":"259","author":"Cheng","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_3","first-page":"2049","article-title":"Exploring the motivations and obstacles of the public\u2019s garbage classification participation: Evidence from Sina Weibo","volume":"25","author":"Wu","year":"2023","journal-title":"J. Mater. Cycl. Waste Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102741","DOI":"10.1016\/j.scs.2021.102741","article-title":"Public participation and city sustainability: Evidence from urban garbage classification in China","volume":"67","author":"Kuang","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1177\/0734242X221074116","article-title":"Re-assessing global municipal solid waste generation","volume":"41","author":"Maalouf","year":"2023","journal-title":"Waste Manag. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"17678","DOI":"10.1007\/s11356-023-27670-2","article-title":"Urbanization and solid waste production: Prospects and challenges","volume":"31","author":"Voukkali","year":"2024","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_7","first-page":"227","article-title":"Municipal solid wastes quantification and model forecasting","volume":"9","author":"Teshome","year":"2023","journal-title":"Glob. J. Environ. Sci. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"140573","DOI":"10.1016\/j.jclepro.2024.140573","article-title":"Intelligent X-ray waste detection and classification via X-ray characteristic enhancement and deep learning","volume":"435","author":"Li","year":"2024","journal-title":"J. Clean. Prod."},{"key":"ref_9","first-page":"103651","article-title":"Relation-aware graph convolutional network for waste battery inspection based on X-ray images","volume":"63","author":"Li","year":"2024","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, Y., and Lin, H. (2023). Multi-scale feature interaction network for remote sensing change detection. Remote Sens., 15.","DOI":"10.3390\/rs15112880"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Wang, W., Zhang, W., Yang, L., Wang, J., Ni, H., Guan, T., He, J., Gu, Y., and Tran, N.N. (2023). A multi-feature fusion and attention network for multi-scale object detection in remote sensing images. Remote Sens., 15.","DOI":"10.3390\/rs15082096"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.wasman.2024.01.047","article-title":"Multi-modal deep learning networks for RGB-D pavement waste detection and recognition","volume":"177","author":"Li","year":"2024","journal-title":"Waste Manag."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shang, R., Zhang, J., Jiao, L., Li, Y., Marturi, N., and Stolkin, R. (2020). Multi-scale adaptive feature fusion network for semantic segmentation in remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12050872"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, H., Chang, H., Ma, B., Wang, N., and Chen, X. (2020, January 23\u201328). Dynamic r-cnn: Towards high quality object detection via dynamic training. Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58555-6_16"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhu, C., He, Y., and Savvides, M. (2019, January 16\u201320). Feature selective anchor-free module for single-shot object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00093"},{"key":"ref_17","unstructured":"Yang, Z., Liu, S., Hu, H., Wang, L., and Lin, S. (November, January 27). Reppoints: Point set representation for object detection. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, K., and Lee, H.S. (2020, January 23\u201328). Probabilistic anchor assignment with iou prediction for object detection. Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58595-2_22"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wang, Y., Yang, T., Zhang, X., Cheng, J., and Sun, J. (2021, January 19\u201325). You only look one-level feature. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01284"},{"key":"ref_20","unstructured":"Ge, Z. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_22","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_23","first-page":"103761","article-title":"ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction","volume":"128","author":"Wang","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chang, J., Dai, H., and Zheng, Y. (2024, January 14\u201319). Cag-fpn: Channel self-attention guided feature pyramid network for object detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Seoul, Republic of Korea.","DOI":"10.1109\/ICASSP48485.2024.10448037"},{"key":"ref_25","first-page":"103820","article-title":"MCDNet: Multilevel cloud detection network for remote sensing images based on dual-perspective change-guided and multi-scale feature fusion","volume":"129","author":"Dong","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","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 15th European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, Q.L., and Yang, Y.B. (2021, January 6\u201311). Sa-net: Shuffle attention for deep convolutional neural networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105042","DOI":"10.1016\/j.cageo.2022.105042","article-title":"MLFC-Net: A multi-level feature combination attention model for remote sensing scene classification","volume":"160","author":"Wang","year":"2022","journal-title":"Comput. Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, X., Zhang, J., Shang, X., Hu, Y., Zhang, S., and Wang, J. (2024). A new dual-branch embedded multivariate attention network for hyperspectral remote sensing classification. Remote Sens., 16.","DOI":"10.3390\/rs16112029"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, F., Hu, T., Xia, Y., Ma, B., Sarwar, S., and Zhang, C. (2024). WDFA-YOLOX: A wavelet-driven and feature-enhanced attention YOLOX network for ship detection in SAR images. Remote Sens., 16.","DOI":"10.3390\/rs16101760"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"327","DOI":"10.3390\/rs4020327","article-title":"Vegetation cover analysis of hazardous waste sites in Utah and Arizona using hyperspectral remote sensing","volume":"4","author":"Im","year":"2012","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.procs.2021.05.037","article-title":"Deep learning and remote sensing: Detection of dumping waste using UAV","volume":"185","author":"Youme","year":"2021","journal-title":"Proced. Comput. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Maharjan, N., Miyazaki, H., Pati, B.M., Dailey, M.N., Shrestha, S., and Nakamura, T. (2022). Detection of river plastic using UAV sensor data and deep learning. Remote Sens., 14.","DOI":"10.3390\/rs14133049"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liao, Y.H., and Juang, J.G. (2022). Real-time UAV trash monitoring system. Appl. Sci., 12.","DOI":"10.3390\/app12041838"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/JSTARS.2022.3218958","article-title":"SWDet: Anchor-based object detector for solid waste detection in aerial images","volume":"16","author":"Zhou","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1038\/s41467-023-37136-1","article-title":"Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery","volume":"14","author":"Sun","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 13\u201319). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_39","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully convolutional one-stage object detection. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 16\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_44","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7389","DOI":"10.1109\/TIP.2020.3002345","article-title":"Foveabox: Beyound anchor-based object detection","volume":"29","author":"Kong","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yang, C., Li, Q., Zhao, F., Zha, Z.J., and Wu, F. (2021, January 20\u201324). Disentangle your dense object detector. Proceedings of the 29th ACM International Conference on Multimedia, Virtual.","DOI":"10.1145\/3474085.3475351"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Gao, Y., Scott, M.R., and Huang, W. (2021, January 10\u201317). Tood: Task-aligned one-stage object detection. Proceedings of the IEEE International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, Y., Dayoub, F., and Sunderhauf, N. (2021, January 19\u201325). Varifocalnet: An iou-aware dense object detector. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.00841"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ying, Z., Zhou, J., Zhai, Y., Quan, H., Li, W., Genovese, A., Piuri, V., and Scotti, F. (2024). Large-scale high-altitude UAV-based vehicle detection via pyramid dual pooling attention path aggregation network. IEEE Trans. Intell. Transp. Syst.","DOI":"10.1109\/TITS.2024.3396915"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1109\/TIP.2024.3364495","article-title":"Toward generalized few-shot open-set object detection","volume":"33","author":"Su","year":"2024","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3595\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:04:07Z","timestamp":1760112247000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3595"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,26]]},"references-count":52,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16193595"],"URL":"https:\/\/doi.org\/10.3390\/rs16193595","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,26]]}}}