Rule-based classification of high-resolution imagery over urban areas in New York City

This paper describes the integration of results from different feature extraction algorithms using spectral and spatial attributes to detect specific urban features. Methodology includes segmentation of IKONOS data, computing attributes for creating image objects and classifying the objects with fuzzy logic and rule-based algorithms. Previous research reported low class accuracies for two specific classes – dark and grey roofs. A modified per-field approach was employed to extract urban features. New rule-sets were used on image objects having similar or near-similar spectral and spatial characteristics. Different algorithms using spectral and spatial attributes were developed to extract specific urban features from a time-series of Multi-Spectral Scanner (MSS) (4 m × 4 m) IKONOS data. The modified approach resulted in a remarkable improvement in the accuracy of classes that registered low spectral seperability and therefore low accuracy. The spectral and spatial based classification model may be useful in mapping heterogeneous and spectrally similar urban features.     

GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning

Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain.     

A Patch‐based Cellular Automaton for Simulating Land‐use Changes at Fine Spatial Resolution

While cellular automata have become popular tools for modeling land‐use changes, there is a lack of studies reporting their application at very fine spatial resolutions (e.g. 5 m resolution). Traditional cell‐based CA do not generate reliable results at such resolutions because single cells might only represent components of land‐use entities (i.e. houses or parks in urban residential areas), while recently proposed entity‐based CA models usually ignore the internal heterogeneity of the entities. This article describes a patch‐based CA model designed to deal with this problem by integrating cell and object concepts. A patch is defined as a collection of adjacent cells that might have different attributes, but that represent a single land‐use entity. In this model, a transition probability map was calculated at each cell location for each land‐use transition using a weight of evidence method; then, land‐use changes were simulated by employing a patch‐based procedure based on the probability maps. This CA model, along with a traditional cell‐based model were tested in the eastern part of the Elbow River watershed in southern Alberta, Canada, an area that is under considerable pressure for land development due to its proximity to the fast growing city of Calgary. The simulation results for the two models were compared to historical data using visual comparison, K_simulation indices, and landscape metrics. The results reveal that the patch‐based CA model generates more compact and realistic land‐use patterns than the traditional cell‐based CA. The K_simulation values indicate that the land‐use maps obtained with the patch‐based CA are in higher agreement with the historical data than those created by the cell‐based model, particularly regarding the location of change. The landscape metrics reveal that the patch‐based model is able to adequately capture the land‐use dynamics as observed in the historical data, while the cell‐based CA is not able to provide a similar interpretation. The patch‐based approach proposed in this study appears to be a simple and valuable solution to take into account the internal heterogeneity of land‐use classes at fine spatial resolutions and simulate their transitions over time.     

动态分段技术在北京水务普查中的应用研究

动态分段技术是一种新的线性特征的动态分析、显示和绘图技术。本文对动态分段数据模型进行探讨,提出一套基于动态分段技术构建河流数据模型的实现方法,它解决了传统数据及属性管理和分析中一对多、线性度量以及分段数据等难题,为水务普查成果数据的管理和分析提供了一定借鉴意义。     

一种基于属性水平版本结构的时空数据模型

以属性水平版本结构为理论基础,在空间数据表中,面状地物类表和线状地物类表是有效时间(Tv)和数据库时间(Td)的载体,事务时间反映版本创建时间(Tc),并将结点和弧段的版本改变记录到它们所属的面状地物类表中;属性数据表中,区分记录属性的连续变化与不连续变化。数据模型既体现了版本信息和时间要素,又减少了数据冗余。     

顾及格网属性分级与空间关联的人口空间化方法

现有人口空间化方法多基于行政单元构建回归模型并分配格网单元人口,但分析单元的尺度差异引发模型迁移问题。同时,格网特征建模仅考虑格网自身属性,导致格网间空间关联被人为割裂。为此,基于随机森林模型提出一种顾及格网属性分级与空间关联的人口空间化方法。该方法在格网特征建模中:（1）基于自然断点法构造建筑区类别约束的夜间灯光分级特征,并在行政单元尺度统计各等级网格占比作为训练输入,以减小模型跨尺度误差;（2）利用核密度估计刻画邻域兴趣点（point of interest, POI）对当前格网人口分布的影响及距离衰减效应;（3）基于叠置分析统计不同类型建筑区轮廓包含的各类POI数量,提升特征建模精细度。选取武汉市作为实验区域,在街道尺度与WorldPop、GPW及中国公里网格人口数据集进行对比验证方法的有效性。结果表明,该方法的平均绝对值误差仅为对比数据集的1/6~1/3。此外,还探讨了特征构成、格网大小及核密度带宽对精度的影响。     

面向对象时空数据模型的研究

时空数据模型是建立TGIS的基础。首先指出了建立时空数据模型中应该考虑的问题,同时指出面向对象时空数据模型的优越性;然后提出了面向对象的时空数据模型。指出地理世界抽象是由时空属性对象组成,每个时空属性对象由空间、属性、时间3部分组成。系统分析了时空属性对象各个组成部分之间的关系并通过UML进行了表达。最后,以地籍管理中的宗地为例说明了该数据模型的实现。     

面向对象时空数据模型的研究

时空数据模型是建立TGIS的基础.首先指出了建立时空数据模型中应该考虑的问题,同时指出面向对象时空数据模型的优越性;然后提出了面向对象的时空数据模型.指出地理世界抽象是由时空属性对象组成,每个时空属性对象由空间、属性、时间3部分组成.系统分析了时空属性对象各个组成部分之间的关系并通过UML进行了表达.最后,以地籍管理中的宗地为例说明了该数据模型的实现.     

Modeling Urban Land Use Changes Using Support Vector Machines

Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation‐based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM‐based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.     

Understanding the interplay between bus,metro, and cab ridership dynamics in Shenzhen,China

The most common mass transit modes in metropolitan cities include buses, subways, and taxicabs, each of which contribute to an interconnected complex network that delivers urban dwellers to their destinations. Understanding the intertwined usages of these three transit modes at different places and time allows for better sensing of urban mobility and the built environment. In this article, we leverage a comprehensive data collection of bus, metro, and taxicab ridership from Shenzhen, China to unveil the spatio‐temporal interplay between different mass transit modes. To achieve this goal, we develop a novel spectral clustering framework that imposes spatio‐temporal similarities between mass transit mode usage in urban space and differentiates urban spaces associated with distinct ridership patterns of mass transit modes. Five resulting categories of urban spaces are identified and interpreted with auxiliary knowledge of the city's metro network and land‐use functionality. In general, different categorized urban spaces are associated with different accessibility levels (such as high‐, medium‐, and low‐ranked) and different urban functionalities (such as residential, commercial, leisure‐dominant, and home–work balanced). The results indicate that different mass transit modes cooperate or compete based on demographic and socioeconomic attributes of the underlying urban environments. Our proposed analytical framework provides a novel and effective way to explore the mass transit system and the functional heterogeneity in cities. It demonstrates great potential for assisting policymakers and municipal managers in optimizing public transportation facility allocation and city‐wide daily commuting distribution.     

Exploring the effectiveness of location‐based social media in modeling user activity space: A case study of Weibo

Location‐based social media (LBSM) has been widely utilized to supplement traditional survey methods in modeling human activity patterns. However, there has not been sufficient study to assess the reliability of these data in deriving human movement. This research aims to evaluate how data collection duration and sample sizes affect the reliability of LBSM data in activity modeling based on two indicators: radius of gyration (ROG) and entropy. We use a linear regression model with logarithmic transformation to approximate how the magnitude of each indicator changes with different data collection durations—from 1 to 12 months. The results indicate that both ROG and entropy increase when the amount of data increases. However, the rate of increase slows down and approaches zero eventually. We also approximated the limit values and verified that with 12‐month data, we are at approximately >95% magnitude of the limit values for both indicators in all three cities. The clustering analysis also demonstrated that there are outlier users who exhibit distinct patterns. This case study focuses on three Chinese cities (Beijing, Shanghai, and Guangzhou) and provides a useful reference to explore the balance point between data effectiveness and an appropriate sample size from LBSM data.     

SCOPED‐W: SCalable Online Platform for extracting Environmental Data and Water‐related model outputs

This article presents SCOPED, an innovative approach for extracting environmental data using OGC services. In the field of water resource management, SCOPED‐W (‘W’ for ‘Water’) is a method that was developed in the framework of EU/FP7 IASON and EOPOWER projects. This platform supports the collection of data required to build a Soil and Water Assessment Tool (SWAT) model and the uptake, spatialization and dissemination of raw data generated from the outputs of different SWAT models for the Black Sea region. Scientists are documenting the data served by the platform in ISO standardized metadata to support informed use. SCOPED‐W primarily targets the community of SWAT users in the Black Sea region but it can easily be replicated in other geographical areas. Additionally, the SCOPED approach is based on data interoperability that makes it fully compatible with other domains of application as demonstrated here with three original use cases. The article also highlights the benefits of the approach for the GEO community and discusses future improvements for supporting integration with other platforms such as UNEP Live.     

A UML‐based Representation of Spatio‐Temporal Evolution in Road Network Data

Geographic features change over time, this change being the result of some kind of event. Most database systems used in GIS are relational in nature, capturing change by exhaustively storing all versions of data, or updates replace previous versions. This stems from the inherent difficulty of modelling geographic objects and associated data in relational tables, and this is compounded when the necessary time dimension is introduced to represent how these objects evolve. This article describes an object‐oriented (OO) spatio‐temporal conceptual data model called the Feature Evolution Model (FEM), which can be used for the development of a spatio‐temporal database management system (STDBMS). Object versioning techniques developed in the fields of Computer Aided Design (CAD) and engineering design are utilized in the design. The model is defined using the Unified Modelling Language (UML), and exploits the expressiveness of OO technology by representing both geographic entities and events as objects. Further, the model overcomes the limitations inherent in relational approaches in representing aggregation of objects to form more complex, compound objects. A management object called the evolved feature maintains a temporally ordered list of references to features thus representing their evolution. The model is demonstrated by its application to road network data.     

Combining Metric Aerial Photography and Near‐Infrared Videography to Define Within‐Field Soil Sampling Frameworks

Abstract

This paper investigates the combination of metric aerial photography and near‐infrared (NIR) videography data to improve the design of field‐survey sampling frameworks. Spatial data collection can contribute up to 80% of the cost of deploying a Geographic Information System (GIS) based Decision Support System (DSS). The use of remotely sensed information, field survey using differential Global Positioning System (dGPS) and geostatistical interpolation methods maximises data quality for a given rate of sampling.

Medium‐format colour aerial photography and NIR videography were orthorectified to the national map base and mosaiced using ERDAS Imagine. The green and red layers of the aerial photography were combined with the NIR videography to form a false‐colour composite image. Two sampling strategies were tested. The first stratified sampling on a per field basis, creating four points per hectare, randomly located within each field. The second strategy used the remotely sensed information to identify within‐field variability classes for each field, using red‐green difference or normalised difference vegetation index (NDVI) models. These variability classes were used as a sub‐stratification framework with each class sampled at the same rate of 4 per hectare. For both strategies the sample points were generated within ESRI ArcView and were located in the field using dGPS. Maps of stone content were created using geostatistical methods and validated against samples collected on a 100 metre grid. It was concluded that combining the two image sources to create a within‐field stratification framework improved the precision of the results obtained from field‐survey.     

Harnessing spatio‐temporal patterns in data for nominal attribute imputation

Missing data in Volunteered Geographic Information (VGI) are an unavoidable consequence of data collection by non‐experts, guided by only vague and informal mapping guidelines. While various Missing Value Imputation (MVI) techniques have been proposed as data cleansing strategies, they have primarily targeted numerical data attributes in non‐spatial databases. There remains a significant gap in methods for imputing nominal attribute values (e.g., Street Name) in map databases. Here, we present an imputation algorithm called the Membership Imputation Algorithm (MIA), targeting spatial databases and enabling imputation of nominal values in spatially referenced records. By targeting membership classes of spatial objects, MIA harnesses spatio‐temporal characteristics of data and proposes efficient heuristics to impute the class name (i.e., a membership). Experimental results show that the proposed algorithm is able to impute the membership with high levels of accuracy (over 94%) when assigning Street Name(s), across highly diverse regional contexts. MIA is effective in challenging spatial contexts such as street intersections. Our research serves as a first step in highlighting the effectiveness of spatio‐temporal measures as a key driver for nominal imputation techniques.     

基于GIS的教育资源数据管理系统设计与实现——以苏州市为例

城市教育是社会公众基本的保障性资源,其空间布局的合理配置对于公众享受公平的教育资源具有重要意义,并体现了教育均衡发展的理念.地理信息系统(GIS)具有空间数据采集、管理和分析等重要功能,已经广泛应用于经济社会领域中的各个方面,在研究教育资源空间分布,促进教育事业蓬勃发展等方面具有强大优势.本文以苏州市教育资源、公共设施...     

时间拓扑关系在单个要素历史管理中的应用

当前的GIS大多基于图层的技术实现历史变化信息的管理,不具有存储单个要素时空属性的机制。对此,改进了ISO的时间模型,并提出了一种时间拓扑关系模型,目的是便于对单个要素的历史信息进行有效的管理。实验表明,该模型可以实现单个要素的历史信息查询。     

Rule‐based Integration of Remotely‐sensed Data and GIS for Land Cover Mapping in NE Costa Rica

Abstract

A classification method was developed for mapping land cover in NE Costa Rica at a regional scale for spatial input to a biogeochemical model (CENTURY). To distinguish heterogeneous cover types, unsupervised classifications of Landsat Thematic Mapper data were combined with ancillary and derived data in an iterative process. Spectral classes corresponding to ground control types were segregated into a storage raster while ambiguous pixels were passed through a set of rules to the next stage of processing. Feature sets were used at each step to help sort spectral classes into land cover classes. The process enabled different feature sets to be used for different types while recognizing that spectral classification alone was not sufficient for separating cover types that were defined by heterogeneity. Spectral data included the TM reflective bands, principal components and the NDVI. Ancillary data included GIS coverages of swamp extents, banana plantation boundaries and river courses. Derived data included neighborhood variety and majority measures that captured texture. The final map depicts 18 land cover types and captures the general patterns found in the region. Some confusion still exists between closely related types such as pasture with different amounts of tree cover.     

基于移动LiDAR点云数据的室内全要素三维重建

精确的室内三维模型在古遗产保护、公共场所的精细化管理和室内导航等领域有着重要作用,而激光雷达正被逐步应用到室内三维重建中。文中采用移动激光雷达对室内所有附属设施进行高精度激光扫描,得到融合CCD影像色彩信息的彩色点云数据。给出室内全要素三维重建的技术流程,并利用Autodesk CAD等三维建模平台对点云数据进行有效处理,最终得到高精度且带有大地坐标的室内三维模型。实验结果表明,采用移动激光雷达能快速获取高精度的室内三维点云数据。通过文中提出的流程,可快速得到真三维、真尺寸、真纹理的室内全要素模型。     

K-T变换的林学意义及其在森林蓄积量估算中的应用

本文讨论了K-T变换在林学方面的意义,它可以用于森林信息分析。就K-T的特征空间而言,其林学意义是很明确的,森林具有较高的绿度、湿度和较低的亮度,它们在光谱空间具有相对稳定的位置,利用这一特殊性质,即可用反射光谱描述森林的林学意义及其环境特征。 本文还用K-T变换特性构造了浙江省临安县森林蓄积量的遥感估算模型,它在结构上考虑了林学和环境特征,从而使模型具有一定的物理含义。模型经试验,其结果较为理想,对主要立地类型、其平均森林蓄积量估算精度达到90％左右,可以用于我国南方山区。