Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although la...Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.展开更多
空间活动日益频繁,目标解体和碰撞生成大量空间碎片可能引发灾难性后果,因此对空间目标进行监测与表征变得尤为重要.空间目标的姿态、形状、材质等特性信息对于目标识别、碰撞规避和主动清除具有重要意义.对空间态势感知领域的重要学术...空间活动日益频繁,目标解体和碰撞生成大量空间碎片可能引发灾难性后果,因此对空间目标进行监测与表征变得尤为重要.空间目标的姿态、形状、材质等特性信息对于目标识别、碰撞规避和主动清除具有重要意义.对空间态势感知领域的重要学术会议AMOS(Advanced Maui Optical and Space Surveillance Technologies)近年来文集中的相关技术论文进行系统分析,涵盖地基观测数据在空间目标表征中的应用,从姿态估计、形状估计到姿态演化以及机器学习辅助决策等,研究结果可为空间目标综合分析提供丰富的技术手段和估计方法,并为未来空间目标表征技术发展提供了有价值的参考.此外,针对特性估计相关数据日益丰富、反演算法愈发成熟的现状和趋势,提出中国应该建立体系化的空间目标特性估计机制新思路.展开更多
Detection of ore waste is crucial for achieving automation in mineral metallurgy production.However,deep learning-based target detection algorithms still face several challenges in iron waste screening,including poor ...Detection of ore waste is crucial for achieving automation in mineral metallurgy production.However,deep learning-based target detection algorithms still face several challenges in iron waste screening,including poor lighting conditions in underground mining environments,dust disturbances,platform vibrations during operation,and limited resources for large-scale computing equipment.These factors contribute to extended computation times and unsatisfactory detection accuracy.To address these challenges,this paper proposes an ore waste detection algorithm based on an improved version of YOLOv5.To enhance feature extraction capabilities,the RepLKNet module is incorporated into the YOLOv5 backbone and neck networks.This module enhances the deformation information of feature extraction with the maximum effective Receptive Field to increase the model's accuracy.The Normalizationbased Attention Module(NAM)was introduced to enhance the attention mechanism by focusing on the most relevant features.This improves accuracy in detecting objects against noisy or unclear backgrounds,thereby further enhancing detection performance while reducing model parameters.Additionally,the loss function is optimized to constrain angular deviation using the SIOU loss function,which prevents the training frame from drifting during training and enhances convergence speed.To validate the performance of the proposed method,we tested it using a self-constructed dataset comprising 1,328 images obtained from the crushing station at Jinchuan Group's No.2 mine.The results indicate that,compared to YOLOv5s on the self-constructed dataset,the proposed algorithm achieves an 18.3%improvement in mAP(0.5),a 54%reduction in FLOPs,and a 52.53%decrease in model parameters.The effectiveness and superiority of the proposed algorithm are demonstrated through case studies and comparative analyses.展开更多
郯庐断裂带中段安丘-莒县断裂(F_(5))为中国东部重要的地震活动断裂,其最南段展布于淮河—女山湖之间,长约20km,最新活动时代为全新世早期。针对F_(5)断裂淮河—女山湖段的前期工作侧重于在不同地段开挖探槽以揭示断裂的最新活动时代、...郯庐断裂带中段安丘-莒县断裂(F_(5))为中国东部重要的地震活动断裂,其最南段展布于淮河—女山湖之间,长约20km,最新活动时代为全新世早期。针对F_(5)断裂淮河—女山湖段的前期工作侧重于在不同地段开挖探槽以揭示断裂的最新活动时代、结构特征及运动性质,并报道了零星的古地震事件。本次在该段选取关键地段开挖探槽并结合前期探槽资料,开展了古地震事件的综合对比研究;通过测量探槽附近断层陡坎的高度,并结合相关地层测年数据,计算了断裂的垂直滑动速率;基于该段古地震研究成果,结合其他学科资料,分析了苏皖交界地区的地震危险性。研究表明:(1)F_(5)断裂淮河—女山湖段中更新世晚期以来至少发生过5次古地震事件,厘定的最近2次事件的年代为20.36~(18.7±0.3) ka BP和10.92~7.83ka BP;(2)F_(5)断裂淮河—女山湖段紫阳山一带的垂直滑动速率约为0.05mm/a,陡山一带的垂直滑动速率约为0.07mm/a,该段整体属于弱活动断层;(3)F_(5)断裂泗洪—明光段为历史地震地表破裂空段,最近1次古地震的离逝时间较长,现今小地震不活跃,闭锁程度较高,易于应力积累,存在发生7级及以上强震的危险性。展开更多
基金supported by the Key Program of National Natural Science Foundation of China(Grant No.41930650)Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42301310).
文摘Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.
文摘空间活动日益频繁,目标解体和碰撞生成大量空间碎片可能引发灾难性后果,因此对空间目标进行监测与表征变得尤为重要.空间目标的姿态、形状、材质等特性信息对于目标识别、碰撞规避和主动清除具有重要意义.对空间态势感知领域的重要学术会议AMOS(Advanced Maui Optical and Space Surveillance Technologies)近年来文集中的相关技术论文进行系统分析,涵盖地基观测数据在空间目标表征中的应用,从姿态估计、形状估计到姿态演化以及机器学习辅助决策等,研究结果可为空间目标综合分析提供丰富的技术手段和估计方法,并为未来空间目标表征技术发展提供了有价值的参考.此外,针对特性估计相关数据日益丰富、反演算法愈发成熟的现状和趋势,提出中国应该建立体系化的空间目标特性估计机制新思路.
基金supported by the Department of science and technology of Shaanxi Province(NO.2023-ZDLGY-24).
文摘Detection of ore waste is crucial for achieving automation in mineral metallurgy production.However,deep learning-based target detection algorithms still face several challenges in iron waste screening,including poor lighting conditions in underground mining environments,dust disturbances,platform vibrations during operation,and limited resources for large-scale computing equipment.These factors contribute to extended computation times and unsatisfactory detection accuracy.To address these challenges,this paper proposes an ore waste detection algorithm based on an improved version of YOLOv5.To enhance feature extraction capabilities,the RepLKNet module is incorporated into the YOLOv5 backbone and neck networks.This module enhances the deformation information of feature extraction with the maximum effective Receptive Field to increase the model's accuracy.The Normalizationbased Attention Module(NAM)was introduced to enhance the attention mechanism by focusing on the most relevant features.This improves accuracy in detecting objects against noisy or unclear backgrounds,thereby further enhancing detection performance while reducing model parameters.Additionally,the loss function is optimized to constrain angular deviation using the SIOU loss function,which prevents the training frame from drifting during training and enhances convergence speed.To validate the performance of the proposed method,we tested it using a self-constructed dataset comprising 1,328 images obtained from the crushing station at Jinchuan Group's No.2 mine.The results indicate that,compared to YOLOv5s on the self-constructed dataset,the proposed algorithm achieves an 18.3%improvement in mAP(0.5),a 54%reduction in FLOPs,and a 52.53%decrease in model parameters.The effectiveness and superiority of the proposed algorithm are demonstrated through case studies and comparative analyses.
文摘郯庐断裂带中段安丘-莒县断裂(F_(5))为中国东部重要的地震活动断裂,其最南段展布于淮河—女山湖之间,长约20km,最新活动时代为全新世早期。针对F_(5)断裂淮河—女山湖段的前期工作侧重于在不同地段开挖探槽以揭示断裂的最新活动时代、结构特征及运动性质,并报道了零星的古地震事件。本次在该段选取关键地段开挖探槽并结合前期探槽资料,开展了古地震事件的综合对比研究;通过测量探槽附近断层陡坎的高度,并结合相关地层测年数据,计算了断裂的垂直滑动速率;基于该段古地震研究成果,结合其他学科资料,分析了苏皖交界地区的地震危险性。研究表明:(1)F_(5)断裂淮河—女山湖段中更新世晚期以来至少发生过5次古地震事件,厘定的最近2次事件的年代为20.36~(18.7±0.3) ka BP和10.92~7.83ka BP;(2)F_(5)断裂淮河—女山湖段紫阳山一带的垂直滑动速率约为0.05mm/a,陡山一带的垂直滑动速率约为0.07mm/a,该段整体属于弱活动断层;(3)F_(5)断裂泗洪—明光段为历史地震地表破裂空段,最近1次古地震的离逝时间较长,现今小地震不活跃,闭锁程度较高,易于应力积累,存在发生7级及以上强震的危险性。
