基于本体的土地利用数据综合研究

提出了基于土地利用类型本体的语义相似度计算模型,建立了顾及空间邻近和语义邻近的土地利用数据综合方法,基于本体语义相似度进行图斑融合的加权骨架线剖分。该模型较好地考虑了人的认知特性,改进的土地利用数据综合过程能够更好地反映和概括土地利用特征。实例证明了该方法的有效性。     

利用克隆选择算法构建的土地用途分区模型

采用空间聚类的思想,构建了利用GIS与克隆选择算法的土地用途分区模型,针对模型实现的编码、抗体-抗原亲和度、抗体多样性等关键技术进行了改进。以海南省昌江县乌烈镇为例对模型进行了验证,结果表明,利用克隆选择算法构建的土地用途分区模型能够在多约束下进行土地用途分区,可以实现全局优化,具有稳定、结果可靠等优点。提供的土地用途分区方案科学合理,可以为土地利用规划和土地利用调控和管理提供支持。     

基于本体的土地类别语义相似度综合计算方法

不同领域所采用的土地分类系统,在类别划分及定义方面存在一定的差异,导致不同系统之间的数据存在语义异质性问题,使跨系统数据难以共享和集成。为了从语义层面解决这个问题,以GlobeLand 30土地覆盖分类系统和《土地利用现状分类标准》(GB/T 21010-2017)2个典型分类系统为例,提出了1种基于本体的土地类别语义相似度综合计算方法。使用基于本体属性、基于本体距离和内容、传统综合方法、改进综合方法4种算法,计算了上述2类系统类别间的语义相似度并进行了对比分析。结果表明:4种算法结果的大体变化趋势一致,而本文综合方法的计算值更接近实际情况。该方法为语义异质性的评估提供了一种量化方案,也为跨领域的知识集成与共享提供参考。     

基于本体结构的空间实体语义相似度计算模型

针对GIS领域语义相似度计算模型考虑的因素过于单一、有效性不足等问题。该文在分析现有的语义相似度计算模型的基础上,综合考虑属性相似度与结构相似度指标,同时引入概念间的继承等关系,提出了一种基于本体结构的地理空间实体语义相似度计算模型,在该模型的结构相似度指标中加入了深度、密度两种权重因子。最后,通过对土地利用分类中实体的语义相似度进行实验,说明其计算结果准确可靠,并能体现更多细节差异。     

面向土地用途分区的空间聚类系统设计与实现

传统的土地用途分区方法存在受人为因素影响、效率低下的问题,不利于土地用途分区管制的实施,基于GIS的智能化的土地用途分区系统则可以弥补这一缺陷。本文以GIS相关理论和土地用途分区的需求分析为基础,构建了系统的逻辑框架,研究了数据库、模型库、算法库和知识库的设计方案,提出了土地用途分区过程中的空间数据整合、目标函数和分区模型的选择等问题的解决方案,并实现了面向土地用途分区的空间聚类系统。以典型地区的土地用途分区为例,验证了该空间聚类系统是一种智能、高效、科学的土地用途分区平台。     

基于本体的地理空间实体类型语义相似度计算模型的研究

地理空间实体类型语义相似度对于类型数据库综合具有重要理论意义。本文以本体作为揭示实体深层次语义信息的方法论,提出了符合认知特性的地理空间实体类型语义相似度计算模型,并通过计算土地利用类型语义相似度对模型进行了验证.     

基于ArcGIS的土地用途分区研究

土地用途分区是土地利用总体规划的重要组成部分,是确定土地用途和采取具体土地利用措施的主要依据之一。GIS的空间分析功能可以辅助土地利用变化分析,完成土地适宜性评价以及土地利用结构和布局调整等规划任务,并根据未来土地用途管制要求,划定和调整土地用途分区。文章以葫芦岛市龙港区土地资源作为研究对象,应用GIS的空间叠置统计分...     

顾及通名语义的汉语地名相似度匹配算法

地名匹配是地理信息检索、多源地理空间数据集成及更新中的关键技术问题。本文根据规范汉语地名构词特点,依据地名通名与地名类型的关系,建立规范地名通名语义知识库,并将由其提供的地名语义作为地名相似度匹配的重要指标。针对基于字面和空间数据的地名匹配方法存在的不足,面向规范地名提出一种综合了地名专名字面相似度和地名通名语义相似度两种因素的复合相似度匹配算法模型。该模型模拟人的认知习惯,根据通名语义相似度程度,通过单调函数关系动态设置专名和通名相似度各自的权重值,利用动态加权方法求得复合地名相似度指标。在上述模型基础上,本文提出了汉语地名匹配策略和流程,利用通名蕴含的语义增强汉语地名匹配算法的理论基础和完备性,提高了地名匹配算法准确率。实验结果表明该模型符合认知习惯,验证了该方法的合理性和有效性。     

土地利用现状图斑的自动综合

本文针对土地利用现状数据中图斑的自动综合,采用语义与空间相结合的方法,分析了影响图斑综合的因素,根据这些因素的影响对土地利用多边形进行合并、融合及聚合操作。并以长沙市宁乡县的土地利用数据为例进行实验,结果表明,该方法较好地保持了宁乡县综合前后各地类的结构布局,能够较好地反映土地利用现状布局,节省了人机交互合并的工作量。     

基于本体属性的基础地理信息概念语义相似性计算模型

语义相似性对于知识自动共享与集成起着非常重要的作用。在许多地理信息的应用领域,通常直接将分类体系作为领域(或任务)本体,并基于此计算概念间的语义距离以实现相似度计算。该方法虽然能够快速、简便地计算概念间的语义相似度,但是有时却因为分类体系的改变而造成相同概念间的相似度产生差异,甚至可能是错误的计算结果。本文面向基础地理信息领域,利用属性枚举方法表达概念的本质语义特征,从基础地理信息概念的内涵出发,提出基于本体属性的语义相似性计算模型。该模型将每个概念表达为本体属性集合,利用相关本体属性的相似性,结合权重信息计算概念的相似性。最后从基础地理信息概念中提取出100组样本,计算概念间的语义相似度并验证基于本体属性模型的有效性。实验结果表明基于本体属性的模型能更合理地计算出基础地理信息概念的相似度。     

一种深度卷积神经网络土地利用场景照片的分类方法

国土调查多角度实景举证照片具有视角多、分辨率高、层次丰富和剖面清晰的特点,透视且细致地刻画了土地利用图斑赋存状况和场景,弥补了遥感影像单一天顶视角的不足。本文基于语义分割提出了一种深度卷积神经网络（DCNN）实景照片土地利用场景分类方法,多语义标记照片场景,语义组合智能判定照片土地利用类别。该方法成功地应用在第三次国土调查照片自动核查工作中,减轻了人工判读工作量,提高了土地利用场景自动识别的精度。     

Implementation of 3D spatial planning through the integration of the standards

To plan future land use, zoning plans (i.e., spatial plans) are prepared to get the most out of land for both the public and the government. These plans manifest which facilities can be built and where they can be built on land based on defined requirements such as building height and road length. The Land Administration Domain Model (LADM) is well-known and widely used standard for describing Rights, Restrictions, and Responsibilities (RRRs) with respect to land and buildings. The next version of the standard will contain the Spatial Plan Information (SPI) part to enable better land-use planning. Three-dimensional (3D) land-use planning has gained attention to delineate detailed requirements inclusively and allow different spatial analysis that provides a basis for decisions in the planning. Data standards pertaining to 3D geoinformation are vital to put into practice 3D spatial planning. To this extent, CityJSON is proposed for the effortless and efficient use of 3D city models. This article thus first aims to extend the CityJSON schema based on the proposed SPI part of the LADM such that it allows modeling, storing, visualizing, and utilizing the features and attributes required for the implementation of 3D spatial planning. Then, the usability of the proposed extension schema is demonstrated by the real-world use cases that benefit from the exemplary CityJSON files that are created based on approved zoning plans in the country. The results of this study show that there is an important opportunity coming from the integration of international standards that enables semantic information along with their spatial counterparts within 3D spatial planning.     

Exploring land use policy scenarios with the use of a cellular automata-based model: urban sprawl containment and sustainable development in Thessaloniki

This study addresses the issue of urban sprawl through the application of a cellular automata (CA)-based model in the area of Thessaloniki, Greece. The model integrates a multiple regression model at the regional level with a CA model at the local level. New urban land is allocated in a disaggregated field of land units (cells) taking into account a wide range of data. Particular emphasis is placed on the way zoning regulations and land availability data are inserted into the model, so that alternative land use policy scenarios could be examined. Thessaloniki, a typical Mediterranean city, is used as a case study. The model is used to compare two scenarios of urban growth up to year 2030; the first one assuming a continuation of existing trends, whereas the second one assuming the enactment of various land use zoning regulations in order to contain urban sprawl.     

Improving Representation of Land‐use Maps Derived from Object‐oriented Image Classification

Object‐oriented (OO) image analysis provides an efficient way to generate vector‐format land‐cover and land‐use maps from remotely sensed images. Such image‐derived vector maps, however, are generally presented with congested and twisted polygons with step‐like boundaries. They include unclassified polygons and polygons with geometric conflicts such as unreadable small areas and narrow corridors. The complex and poorly readable representations usually make such maps not comply well with the Gestalt principle of cartography. This article describes a framework designed to improve the representation by resolving these problematic polygons. It presents a polygon similarity model integrating semantic, geometric and spectral characteristics of the image‐derived polygons to eliminate small and unclassified polygons. In addition, an outward‐inward‐buffering approach is presented to resolve the narrow‐corridor conflicts of a polygon and improve its overall appearance. A case study demonstrates that the implementation of the framework reduces the number of the polygons by 32% and the length of the polygon boundaries by 20%. At the same time, it does not cause distinct changes the distribution of land‐use types (less than 0.05%) and the overall accuracy (decreased only 0.02%) as compared with the original image‐derived land‐use maps. We conclude that the presented framework and models effectively improve the overall representation of image‐derived maps without distinct changes in their semantic characteristics and accuracy.     

基于ArcObjects的土地利用时空分析系统研究

随着愈加快速的土地资源开发,土地利用时空变化研究已全面启动,本文结合ArcObjects组件、Access数据库和VC 工具开发了一个土地利用时空分析系统,实现了土地利用空间数据建库、地图查询分析、土地利用时空模型解算等功能。为土地利用时空研究提供功能集成的数据运算与图形处理平台,也为土地利用规划的制定及其修编等工作提供了技术支持。     

Air photo approach to inventory of urban land use in central area case study — Kanpur

The case study of Kanpur demonstrates the use of air photo approach to collect physical data, mainly landuse in central area. The study reveals that very less percentage of vacant land exists in central area for future development. Though this area consist CBD, even then commercial landuse accounts to be only 10% of total area. Selective landuse inventory analysis was carried out for the landuse categories, commercial, industrial and vacant. Reliability of aerial photo-interpretation of over all landuse map found to be 91–95% and each sub-class of landuse reliability for omission and commission error found to be within limits for planning and design purpose.     

Adaptive Zoning for Transport Mode Choice Modeling

Adaptive zoning is a recently introduced method for improving computer modeling of spatial interactions and movements in the transport network. Unlike traditional zoning, where geographic locations are defined by one single universal plan of discrete land parcels or ‘zones’ for the study area, adaptive zoning establishes a compendium of different zone plans, each of which is applicable to one journey origin or destination only. These adaptive zone plans are structured to represent strong spatial interactions in proportionately more detail than weaker ones. In recent articles, it has been shown that adaptive zoning improves, by a large margin, the scalability of models of spatial interaction and road traffic assignment. This article confronts the method of adaptive zoning with an application of the scale and complexity for which it was intended, namely an application of mode choice modeling that at the same time requires a large study area and a fine‐grained zone system. Our hypothesis is that adaptive zoning can significantly improve the accuracy of mode choice modeling because of its enhanced sensitivity to the geographic patterns and scales of spatial interaction. We test the hypothesis by investigating the performance of three alternative models: (1) a spatially highly detailed model that is permissible to the maximum extent by available data, but requires a high computational load that is generally out of reach for rapid turnaround of policy studies; (2) a mode choice model for the same area, but reducing the computational load by 90% by using a traditional zone system consisting of fewer zones; and (3) a mode choice model that also reduces the computational load by 90%, but based on adaptive zoning instead. The tests are carried out on the basis of a case study that uses the dataset from the London Area Transport Survey. Using the first model as a benchmark, it is found that for a given computational load, the model based on adaptive zoning contains about twice the amount of information of the traditional model, and model parameters on adaptive zoning principles are more accurate by a factor of six to eight. The findings suggest that adaptive zoning has a significant potential in enhancing the accuracy of mode choice modeling at the city or city‐region scale.     

RADARSAT影像在土地利用动态监测中的应用

为了改变以往土地管理部门对土地管理的传统方式,节省在常规管理中耗费的大量人力和物力,遥感技术作为新兴发展的科学技术,已应用于动态监测土地利用的变化。本文将具体论述如何利用微波遥感技术监测土地利用的动态变化。     

Selection of spectral band combination for land cover/land use classification using a brightness value overlapping index (BVOI)

A simply defined, accurate and efficient criterion of selecting a spectral-band combination for improved land use/land cover classification using remote sensing data is discussed. Results indicate that Brightness Value Overlapping Index (BVOI) is very effective in measuring the degree of overlap in brightness values among land cover types and in selecting suitable spectral-band combination for landuse classification. The results of BVOI are also compared with the results of another band-combination selecting index - Optimum Index Factor (OIF).