Maintaining accurate,current, rural road network data: An extraction and updating routine using RapidEye,participatory GIS and deep learning

Accurate and current road network data is fundamental to land management and emergency response, yet challenging to produce for unpaved roads in rural and forested regions using traditional cartographic approaches. Automatic extraction of roads from satellite imagery using deep learning is a promising alternative gaining increasing attention, however most efforts have focused on urban paved roads and used very high spatial resolution imagery, which is less frequently available for rural regions. Additionally, road extraction routines still struggle to produce a fully-connected, vectorized road network. In this study covering a large forested area in Western Canada, we developed and evaluated a routine to automatically extract unpaved road pixels using a convolutional neural network (CNN), and then used the CNN outputs to update a pre-existing government road network and evaluate if and how it would change. To cover the large spatial extent mapped in this study, we trained the routine using moderately high-resolution satellite imagery from the RapidEye constellation and a ground-truth dataset collected with smartphones by organizations already operating and driving in the region. Performance of the road extraction was comparable to results achieved by others using very high-resolution imagery; recall accuracy was 89–97%, and precision was 85–91%. Using our approach to update the pre-existing road network would result in both removals and additions to the network, totalling over 1250 km, or about 20 % of the roads previously in the network. We discuss how road density estimates in the study area would change using this updated network, and situate these changes within the context of ongoing efforts to conserve grizzly bears, which are listed as a Threatened species in the region. This study demonstrates the potential of remote sensing to maintain current and accurate rural road networks in dynamic forest landscapes where new road construction is prevalent, yet roads are also frequently de-activated, reclaimed or otherwise not maintained.     

论中国在WTO框架下应对非市场经济的策略

针对“非市场经济”问题，分析了其国际法依据，提出在WTO框架下中国政府应采取积极应对的有效策略。如何在非市场经济地位的问题上趋利避害．利用世界贸易组织规则和争端解决机制争取市场经济待遇，是我国应对国外强烈的反倾销攻势的关键性问题。     

气动潜孔锤技术在矿山事故钻孔救援中的应用

为解决矿山应急救援钻孔作业过程中井涌井漏事故预警预测困难等问题,建立了基于机器学习的钻进过程井涌井漏事故预警预测模型。首先对井涌井漏事故发生初期时的钻进参数进行事故表征参数分析;其次对事故表征参数进行数据清洗处理,在此基础上,通过XGBoost事故诊断预警模型对井涌井漏事故进行早期诊断识别;随后建立PSO-LSTM事故发展预测模型,对事故发生后的孔底压力参数发展趋势进行预测,提前掌握钻进事故发展状态;最后通过实际钻进数据对预警预测模型的有效性进行验证。结果表明：XGBoost事故诊断预警模型能根据总池体积、立管压力、出入口流量差和动力头负荷这4种钻进参数的异常变化,快速准确诊断钻进过程中的井涌井漏事故;PSO-LSTM事故发展状态预测模型能充分学习孔底压力参数发展规律,综合E_MAP, E_MA, E_RMS and R², the prediction performance of the PSO-LSTM models is the best compared with BP, RNN and SVM, capable of accurately predicting the development trend of the downhole pressure after the accident, thereby knowing about the severity and development situation of kick and lost circulation accidents. Generally, the research results enrich the early warning and prediction methods of kicks and lost circulation accidents in the drilling process, improve the reliability of surface rescue in mine accident, and have a reference and guiding effect on accident control during the emergency rescue drilling of mine.     

Application of deep learning technique to the sea surface height prediction in the South China Sea

A deep-learning-based method, called ConvLSTMP3, is developed to predict the sea surface heights(SSHs).ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal features of SSHs, in which the spatial features are "learned" by convolutional operations while the temporal features are tracked by long short term memory(LSTM). Trained by a reanalysis dataset of the South China Sea(SCS), ConvLSTMP3 is applied to the SSH prediction in a region of the SCS east off Vietnam coast featured with eddied and offshore currents in summer. Experimental results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4% averaged over a 15-d prediction period. In particular,ConvLSTMP3 shows a better performance in predicting the temporal evolution of mesoscale eddies in the region than a full-dynamics ocean model. Given the much less computation in the prediction required by ConvLSTMP3,our study suggests that the deep learning technique is very useful and effective in the SSH prediction, and could be an alternative way in the operational prediction for ocean environments in the future.     

基于BERT—BiLSTM—CRF模型的中文岩石描述文本命名实体与关系联合提取

地质调查正在从“数字化”走向“智能化”,需要在大数据思维的指导下,面向非结构化数据开展机器阅读和地质知识的自动提取。地学命名实体和关系联合提取是当前研究的难点和核心。本文采用基于大规模预训练中文语言模型的BERT—BiLSTM—CRF方法开展岩石描述文本命名实体与关系联合提取。首先,通过收集数字地质填图工作中的剖面测量和路线地质观测数据,建立岩石描述语料;然后,在岩石学理论指导下分析岩石知识组成,完成岩石知识图谱命名实体与关系的模式设计,标注岩石语料;最后,开展岩石描述语料知识提取的深度学习训练和消融试验对比。试验结果显示,大规模预训练中文语言模型（BERT）对岩石描述语料知识提取具有较高的适用性。推荐的BERT—BiLSTM—CRF模型方法对岩石命名实体与关系联合提取的准确率（F1值）为91.75%,对岩石命名实体识别的准确率（F1值）为97.38%。消融试验证明基于BERT的词嵌入层对岩石描述知识提取的性能提升影响显著,双向长短时记忆网络模型层（BiLSTM Layer）能提升实体关系联合提取性能。     

文化产业与城市经济发展关系实证研究——以长沙市为例

发展文化产业是社会主义市场经济对文化发展的必然要求,又是人们消费需求结构变化的必然结果。在国民经济结构调整和转型中,文化产业已成为新的经济增长点和新兴产业。近年来,长沙市文化产业发展迅速,带动了城市经济发展,提升了产业经济结构。为实现长沙市文化产业发展目标,打造中部的区域性文化中心城市,还需进一步深化文化体制改革,抓好文化产业园区建设,加强文化创意人才队伍建设,优化文化产业发展环境。     

中国海洋经济地域差异及演化过程分析

本文在分析国内外区域海洋经济差异研究进展的基础上,以沿海11个省、自治区、直辖市为研究的基本空间单元,以人均海洋产业产值为测度区域海洋经济差异的变量指标,利用基尼系数和塞尔指数来解释1996~2005年我国海洋经济差异的总体水平和产业结构的动态演化特征,对区域海洋经济差异的构成进行来源分解。并分析空间差异的成因和作用机制。     

干旱区绿洲持续农业与农村发展评价指标体系初步研究--以新疆吐鲁番绿洲为例

构建五套可以反映和评价干旱区绿洲农业与农村经济可持续发展态势的评价指标体系,客观反映、适时监测及预测干旱区绿洲ＳＡＲＤ态势,为区域宏观决策提供依据,是干旱区持续农业与农村发展研究的难点之一。本文以新疆吐鲁番绿洲为例,构建干旱区绿洲ＳＡＲＤ评价指标体系,评价结果是吐鲁番地区农业与农村经济发展均为弱可持续状态,并指出其制约因素和潜力。     

基于多层反卷积网络的SAR图像分类

针对传统特征提取方法不能提取目标高层结构特征的问题,提出了一种基于软概率的池化方法,结合多层反卷积网络,学习目标的高层结构特征,并将其用于合成孔径雷达(SAR)图像分类。首先对SAR图像进行子块划分,然后对每个子块进行基于多层反卷积网络的特征编码,学习出不同层次上的图像特征,最后将该特征用于支持向量机(SVM)分类器,实现SAR图像的分类。在国内首批SAR数据上的实验表明,该算法获得了较高的分类准确率。     

基于深度学习的视频识别及动态监测技术应用

基于国内外城市轨道交通建设工程安全管理及信息化建设现状,本文分析了当前轨道交通建设过程中安全管理面临的难点问题,论述基于Faster R-CNN的施工现场视频识别方法,并引入图像空间特征,提高了施工现场人员不安全行为的识别率;采用基于Apriori关联分析算法构建施工环境、设备动态监测与隐患数据间的潜在强关联关系的分析处理流程,提高了施工现场隐患的发现与处置效率。在此基础上提出轨道交通建设安全管理平台总体架构,重点介绍了安全风险隐患管理、视频监测目标识别等在内的安全管理核心模块的应用实践。应用成果表明,本文算法与平台可有效提高施工现场安全信息处理效率,降低安全事故总体发生率,为城市轨道交通建设安全管理提供有效技术支撑。