Classification Method for Dongting Lake Wetland Based on Geographic Information

[Objective] This study aimed to improve the accuracy of remote sensing classification for Dongting Lake Wetland.[Method] Based on the TM data and ground GIS information of Donting Lake,the decision tree classification method was established through the expert classification knowledge base.The images of Dongting Lake wetland were classified into water area,mudflat,protection forest beach,Carem spp beach,Phragmites beach,Carex beach and other water body according to decision tree layers.[Result] The accuracy of decision tree classification reached 80.29%,which was much higher than the traditional method,and the total Kappa coefficient was 0.883 9,indicating that the data accuracy of this method could fulfill the requirements of actual practice.In addition,the image classification results based on knowledge could solve some classification mistakes.[Conclusion] Compared with the traditional method,the decision tree classification based on rules could classify the images by using various conditions,which reduced the data processing time and improved the classification accuracy.

Quantitative information measurement and application for machine component classification codes

Information embodied in machine component classification codes has internal relation with the probability distribu- tion of the code symbol. This paper presents a model considering codes as information source based on Shannon’s information theory. Using information entropy, it preserves the mathematical form and quantitatively measures the information amount of a symbol and a bit in the machine component classification coding system. It also gets the maximum value of information amount and the corresponding coding scheme when the category of symbols is fixed. Samples are given to show how to evaluate the information amount of component codes and how to optimize a coding system.

Application of GIS in Ecological Land Type(ELT)mapping--A case in Changbai Mountain area

This paper depicted the physiographic landscape features and natural vegetation situation of study area (the eastern Jilin Province), and expatiates the definition, basic characters and its development of Ecological Land Classification (ELC). Based on the combination of relief map, satellite photography for study area and vegetation inventory data of 480 sample sites, a 5-class and a 15-class ecological land type map was concluded according to 4 important factors including slope, aspect, vegetation and elevation. Ecological Classification System (ECS) is a method to identify, characterize, and map ecosystems. The Ecological Land Type (ELT) was examined and applied initially in eastern Jilin Province.

Evaluating effectiveness of frequency ratio, fuzzy logic and logistic regression models in assessing landslide susceptibility: a case from Rudraprayag district, India

Rudraprayag in Garhwal Himalayan division is one of the most vulnerable districts to landslides in India. Heavy rainfall, steep slope and developmental activities are important factors for the occurrence of landslides in the district. Therefore, specific assessment of landslide susceptibility and its accuracy at regional level is essential for disaster management and proper land use planning. The article evaluates effectiveness of frequency ratio, fuzzy logic and logistic regression models for assessing landslide susceptibility in Rudraprayag district of Uttarakhand state, India. A landslide inventory map was prepared and verified by field data. Fourteen landslide parameters and generated inventory map were utilized to prepare landslide susceptibility maps through frequency ratio, fuzzy logic and logistic regression models. Landslide susceptibility maps generated through these models were classified into very high, high, medium, low and very low categories using natural breaks classification. Receiver operating characteristics(ROC) curve, spatially agreed area approach and seed cell area index(SCAI) method were used to validate the landslide models. Validation results revealed that fuzzy logic model was found to be more effective in assessing landslide susceptibility in the study area. The landslide susceptibility map generated through fuzzy logic model can be best utilized for landslide disaster management and effective land use planning.

Evaluation of Land Use &Land Cover Change Using Multi-Temporal Landsat Imagery: A Case Study Sulaimaniyah Governorate, Iraq

Land use & land cover change detection in rapid growth urbanized area have been studied by many researchers and there are many works on this topic. Commonly, settlement sprawl in area depends on many factors such as eco-nomic prosperity and population growth. Iraq is one of the countries which witnessed rapid development in the settlement area. Remote sensing and geographic information system (GIS) are analytical software technologies to evaluate this familiar worldwide phenomenon. This study illustrates settlement development in Sulaimaniyah Governorate from 2001 to 2017 using Landsat satellite imageries of different periods. All images had been classified using remote sensing software in order to proceed powerful mapping of land use classification. Maximum likelihood method is used in the accurately extracted solution information from geospatial imagery. Landsat images from the study area were categorized into four different classes. These are: forest, vegetation, soil, and settlement. Change detection analysis results illustrate that in the face of an explosive demographic shift in the settlement area where the record + 8.99 percent which is equivalent to 51.80 Km2 over a 16-year period and settlement area increasing from 3.87 percent in 2001 to 12.86 percent in 2017. Accuracy assessment model was used to evaluate (LULC) classified images. Accuracy results show an overall accuracy of 78.83% to 90.09% from 2001 to 2017 respectively while convincing results of Kappa coefficient given between substantial and almost perfect agreements. This study will help decision-makers in urban plan for future city development.

A Brief Review of 3S Technology Application on Biodiversity Monitoring and Assessment

Remote sensing,geographic information system and GPS(3S)technology have been well recognized as comprehensive,accurate and up-to-date information collection methods,which are increasingly adopted in biodiversity conservation.This review summarizes the application of object-oriented classification methods on biodiversity monitoring projects based on high-resolution remote sensing imagines in China.Biodiversity conservation research based on GIS technology in China is also discussed,with emphasis on the advantages of GIS analysis and modeling function.

TriCh-LKRepNet:融合三通道映射与结构重参数化的大核卷积恶意代码分类网络

随着网络威胁的日益严峻,恶意代码的检测与分类变得尤为关键.传统分析方法依赖手动特征提取,不仅耗时且难以跟上恶意代码的快速变异.相比之下,深度学习技术在恶意代码分类方面展现出巨大潜力.然而,模型复杂度和资源消耗仍是实际部署的难题.本研究提出了TriCh-LKRepNet(Triple-Channel Large Kernel Reparameterisation Network),该网络专注于轻量化设计,旨在确保检测性能的同时降低计算和内存需求.通过提出的三通道映射技术,将恶意代码的多维信息有效转换为图像通道,增强了特征的区分性.结合卷积神经网络(Convolutional Neural Networks,CNN)和Transformer的优势,设计了一个高效的深度学习架构,并通过重参数化技术优化了连接路径,以降低内存消耗并提升运行效率.此外,引入的线性训练时间过参数化和大卷积核技术进一步降低了模型的参数量和计算负担.通过实验证明,TriCh-LKRepNet在提升恶意代码分类精度的同时实现了模型的轻量化,与现有技术相比,展现出更佳的性能和更广泛的应用潜力,特别是在资源受限和需要实时检测的环境中,提供了一种有效的解决方案.

融合异构信息的自动国际疾病分类编码方法

针对自动国际疾病分类(ICD)编码中医学电子健康记录(EHR)的结构多样性以及编码间复杂的关联关系等特点,提出一种融合异构信息的自动ICD编码方法AIC-HI(Automatic ICD Coding integrating Heterogeneous Information)。首先,针对编码任务中结构化编码、半结构化描述、非结构化医学文本这3种异构数据的不同特性设计了多种特征提取器;其次,构建编码知识图谱拟合编码的层次结构关系,将不同分支间关联关系转化为包含头尾编码的三元组;再次,运用表征学习融合编码和描述信息计算标签特征;最后,通过注意力机制提取在非结构化文档中与编码标签最为相关的特征表示。实验结果表明,与次优的基线模型MARN(Multitask bAlanced and Recalibrated Network)相比,AIC-HI在真实临床数据集MIMIC-Ⅲ上所有编码的微观F1值提升了4.3个百分点。

我国基层医疗诊断编码实施现况与优化策略

随着基层医药卫生体制改革的不断深化,基层卫生信息标准化建设愈发被重视。基层医疗诊断编码作为基层卫生信息化建设的重要环节,在提高基层医疗管理效率、促进基层医疗质量控制、有效收集基层医疗数据等方面都起着至关重要的作用。但我国长期以来沿用综合性医院使用的疾病分类作为基层医疗诊断编码使用,不仅导致基层医疗诊断不规范、无法统计的混乱状态,更可能造成基层医疗收集、记录、分析的数据出现缺失和误差。本文通过梳理国内外基层医疗诊断编码发展的历史沿革,指出当前我国基层医疗诊断编码所面临的5个主要问题:各方对基层医疗诊断编码的重视程度不足、缺乏统一的基层医疗诊断编码导致我国基层医疗功能与质量发展受限、我国基层医疗诊断编码与何种国际基层分类匹配存在分歧、基层医疗诊断编码本土化面临的诸多挑战及我国缺乏评价与优化基层医疗诊断编码的人才与工具。并在此基础上,结合国外基层医疗诊断编码的发展过程,提出我国各方应重视基层医疗诊断编码的发展,在进一步推动基层医疗信息建设与国际接轨的同时,基于目前试行的国际疾病分类编码(ICD)-11与基层医疗保健国际分类(ICPC)-3分类尽快制定全国统一的基层医疗诊断编码标准,并通过建立健全的基层医疗诊断编码人才培养体系,为我国基层医疗诊断编码的进一步实施和优化提供保障。

非物质文化遗产项目编码标准研究

非遗项目编码是实现非遗信息标准化的一项基础性工作,也是非物质文化遗产数字化保护体系中的基本一环。非遗项目编码好比居民身份证,每个非遗项目都有与之一一对应的身份信息。只有对非遗项目进行编码,才能快速准确地定位到相应的非遗项目信息。对非遗项目进行编码,不仅可以为非遗数据库、应用系统开发奠定坚实的基础,同时可以解决数据库开发时非遗项目与非遗传承人之间如何精准关联的问题,更好地促进我国非遗数字化保护标准体系建设。本文对非遗项目编码标准的基本概念、信息分类、编码方式等问题进行了研究,并提出了一些初步设想。

基于空间归属关系下的木构古建筑构件信息分类编码研究

古建筑构件的信息编码是实现信息共享和实施信息系统的基础,可为古建筑数字化保护工作提供支撑。目前古建筑构件分类方式多参考现代建筑构件的分类方法,不能契合古建筑构件构造特点。通过对我国木构古建筑相关文献的分析与现状的调研,进行了木构古建筑构件信息分类编码体系的研究。研究表明,我国传统木构建筑具有“三段式”的整体特点,以“间”为单位、强调轴线对称的平面布局特点以及造型、空间布局、构件构造相统一的体系特点,进一步从木构古建筑特点中发掘出构件与空间归属关系。基于此提出了“三分”构件分类原则并设计了构件位置码制订规则。结合实例对提出的传统木构建筑构件分类方法进行了说明和运用。该构件信息分类编码体系具有较高的适应性、可扩延性以及兼容性,可为实现各地区传统木构建筑构件信息标准化以及古建筑数字化保护工作奠定基础。

公路工程量清单计价信息模型分类编码研究

目前国内公路工程量清单计价信息化的主流方式有三级清单、0#台账和识图算量3种方式,但是相关产品自动化程度不够完善,需要较多的人工干预,实际应用有较多的局限性。通过研究公路工程量清单计价信息模型分类编码规则,搭建编码与项目前后期造价及工程量元素映射通道,在此基础上实现计算机对各阶段造价自动转换、自动编制预算、将编码融入设计日常工作并自动提取信息等实际应用,从而实现“智能化造价”。

高速公路BIM模型分类和编码研究

随着国内对BIM技术的广泛使用于各种建设项目中(如房屋建造、道路修建、铁道铺设等),特别是在高速公路建设的规划设计到运行维护全过程都有了它的身影。然而,由于现行基础设施项目建设的管理方式仍存不足之处——例如缺乏清晰的目标设定,工作效率低下导致资源消耗过大等问题仍然普遍出现。这些问题的根源在于现有项目的管理工作流程还不能满足现代化要求,尤其是数字化水平较低且数据孤岛现象明显的问题日益突出。因此需要进一步探讨如何通过建立完善的信息化BIM模型信息分类和编码标准来解决上述难题。

长距离输水工程数字孪生模型数据融合及编码技术研究

针对长距离输水工程在数字孪生工程数据底板建设过程中GIS与BIM等多源数据融合及编码体系不完善的问题,通过分析长距离输水工程设施数字孪生模型建立方法和编码特点,开展BIM模型和倾斜摄影三维实景模型建立与融合方法、水工设施KKS编码方法和基于Dynamo的快速编码技术的研究。研究结果及工程应用表明,基于Cesium的BIM模型和倾斜摄影模型融合能够实现精细化三维场景模型构建和长距离输水工程数据信息共享。将KKS编码拓展到水工设施编码,形成的数字化编码库,有利于水工设施与机电设备编码的高效和统一管理。利用Dynamo自动编码技术,可快速实现设施设备快速编码,提升了长距离输水工程设施运维管理的数字化与智能化程度,为后续数字孪生延伸应用奠定基础。

An Urban Open Space Extraction Method: Combining Spectral and Geometric Characteristics

This paper introduces an advanced method based on remote sensing and Geographic Information System for urban open space extraction combining spectral and geometric characteristics. From both semantic and remote sensing perspectives, a hybrid hierarchy structure and class organization of open space are issues and mapped from one to another. Based on per-pixel and segmentation mechanism separately, two classification approaches are performed. Owing to prior of spatial aggregation and spectral contribution, the segmentation-based classification exhibits its superiority over a pixel-based classification. Finally a GIS-based post procedure is hired to eliminate some unsuitable open space components in both spatial and numerical constraints on the one hand, and separate open space some fabrics from fused remote sensing classes by defining their Shape Index on the other hand. The case study of Beer Sheva based on ASTER data proves this method is a feasible way for open space extraction.

Classification Method Research to Remote Sensing Images

With rapid development of remote sensing technology, the resolution of remote sensing images is increasingly improved; then people can extract more useful data and information from these images. Thus, an important information extraction method from remote sensing images - image classification, becomes more and more important. Based on phenopthase and band composition characteristics, this paper firstly discusses the important role of background parameters in remote sensing images classification; then based on geographical infomation system technology, the computerized automatic classification to high-medium-low-yield croplands in Dingxiang County of Shanxi Province in rotate sensing images has been carried out by using eompound layers classification method of multi-thematic information; compared the classification result to the visual interpretation results, the accuracy increases from 70% to above 90%.

测绘地理信息元数据自动化检查的设计与实践

测绘地理信息元数据是一种描述测绘地理信息数据的数据,包含数据的表示、覆盖范围、质量、空间和时间模式、空间参考系和分发等信息,覆盖项目设计、数据生产、质量检查、项目验收全流程。作为一项重要的成果数据,其对数据的生产、说明、使用和分析管理有重要的指导作用。如果元数据不精确,则使用相关数据时就会存在质量问题。测绘地理信息元数据生产中,相关标准与具体项目需求和成果需求相结合时通常会存在一定差异,因此不易实现质量检查的自动化。目前其质量检查多采用人工检查的方式,检查效率及准确度受人为影响。本文在分析其内容和特点的基础上,将其分为四类,结合四类元数据的特点提出了以模板为基础的自动化检查方法,在基础性地理国情监测、基础测绘成果、数字高程模型成果检查等项目中得到了实践应用,对提高测绘地理信息元数据检查的效率及准确率有一定意义。

基于API序列的可解释恶意代码检测方法

针对基于API序列的恶意代码检测方法中,深度学习方法特征可解释性差,传统机器学习方法依赖人工设计特征以及忽视数据间时序特性等问题,从时序分类的角度,提出一种基于API序列的可解释恶意代码检测方法。将恶意代码动态API调用序列转换为熵时间序列;使用时间序列分类中的shapelet方法提取具有辨别性的特征;使用多种分类器构造检测模型。实验结果表明,该方法能够自主学习具有辨别性的时序特征,能够在兼具高准确率的同时提供模型的可解释性分类依据。

抽水蓄能电站工程造价信息分类编码研究

在总结水电工程信息分类编码研究现状的基础上,基于水电工程造价形成规律,结合抽水蓄能电站枢纽建筑物布置特点及工程建设实际情况,对抽水蓄能电站工程造价信息分类编码进行深入研究,系统提出工程造价信息分类编码的编码原则、编码结构及方案。研究成果对于抽水蓄能电站工程造价信息分类标准体系的建立,以及具体项目的造价信息分类编码应用具有一定的参考意义。

BIM模型信息分类编码分析

BIM信息编码的重点不在于代码本身,而在于分类,因为不同角色对BIM模型信息的分类方式决定了信息编码所采用的格式。基于此,文章首先介绍树状分类法、面分类法和混合分类法三种信息分类的方式,然后提出层次码、组合码和复合码三种编码方式,最后通过案例说明如何实现多个软件之间的数据传递,并将自定义分类编码附加在Revit的对象属性,从而向项目参建单位提供同一套分类编码,指导其开展协同工作,保障信息的有效传递。