<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">SSSD</journal-id><journal-title-group><journal-title>Scientific and Social Sustainable Development</journal-title></journal-title-group><issn>3066-8964</issn><eissn>3066-8980</eissn><publisher><publisher-name>Art and Design</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/SSSD.2025200027</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>遥感图像场景下基于深度学习的有色金属低碳循环
利用机制与对策研究</title><url>https://artdesignp.com/journal/SSSD/1/20/10.61369/SSSD.2025200027</url><author>唐雅媛,李星宇</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>1</volume><issue>20</issue><history><date date-type="pub"><published-time>2025-12-28</published-time></date></history><abstract>本文旨在探讨在遥感图像场景下，如何应用深度学习技术构建有色金属低碳循环利用的机制，并提出相应的对策，以应对资源枯竭、环境污染和气候变化等挑战，对促进可持续发展具有重要的理论价值和实践意义。</abstract><keywords>遥感图像,深度学习,有色金属,低碳循环,利用机制,对策</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] 遥感技术基础与应用教程[M]. 北京: 科学出版社, 2018.[2] 深度学习原理与实践[M]. 北京: 清华大学出版社, 2019.[3] 有色金属循环利用技术与发展趋势[M]. 北京: 冶金工业出版社, 2020.[4] 宋仁忠, 郑慧玉, 王党朝, 等. 基于深度学习和高分辨率遥感影像的露天矿地物分类方法[J]. 中国矿业, 2022, 31(7): 102-111.[5] 杨敬增, 池莉. 双碳背景和弹性供应链体系下的 有色金属循环产业链建设[J]. Nonferrous Metals (Mining Section), 2023, 75(1).[6]Li,J.,Zhang,H.,Li, X.,et al. Integrating UAS-based hyperspectral and LiDAR data for mineral mapping in a banded iron formation[J].Neural Computing and Applications, 2024, 36.[7]Ai L, Li J, He Y, et al. Segmentation and Labeling of Polished Section Images Based on Mask R-CNN Algorithm[J].Minerals, 2022, 12(7).[8]Ghaderi, V., Nezhad, R. H. K., Afzal, P., et al. Multifractal modeling and GIS-based analysis of mineral prospectivity in the Toroud-Chahshirin Magnatic Belt, Central Iran[J].Geosystems and Geoenvironment, 2024, 3(1).[9]Nistic&amp;ograve;, N., Lanotte, M., Gargiulo, A., et al. A deep learning approach for semantic segmentation of unprecedented urban areas in view of emergency mapping[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 124.[10]Liu, L., Zhou, J., Jiang, D., et al. Ore-Waste Discrimination Using Superpixel-Based Deep Learning Segmentation Model for Hyperspectral Imaging in an Underground Gold Mine[J].Remote Sensing, 2023, 15(15).[11]Cao, J., Zhang, Z., Zhang, W., et al. A small sample deep learning method for prediction of heavy metal contamination in marine sediments[J].Science of The Total Environment, 2023, 857.[12]Hagemann, S. E., Hough, M. P., Jowitt, S. M., et al. A geodata-science-driven prospectivity model for the Belt and Road Initiative: A new approach to assess the mineral resource potential of global mineral belts[J].Ore Geology Reviews, 2023, 161.[13]Liu, T., Zhang, Z., Ghamisi, P., et al. Spodumene Identification Using Multisource Satellite Imagery and a Lightweight Spectral&amp;ndash;Spatial Model[J].IEEE Transactions on Geoscience and Remote Sensing, 2023, 61.[14]Da Silva, M. B., Del Frari, B. B., Delmonte, M., et al. Conformal prediction for reliable machine learning in mineral processing: a case study on ferrous scrap classification[J].arXiv, 2023.[15]Pinto, D., Basto, M., Silva, G., et al. Assessing ESG Risks in Copper Supply Chains: A Remote Sensing and Machine Learning Approach[J].Resources Policy, 2024, 88.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
