A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score.     

Choosing pasture maps: An assessment of pasture land classification definitions and a case study of Brazil

Pasture land occupies extensive areas and is increasingly of interest for sustainable intensification, land use diversification, greenhouse gas emission mitigation, and bioenergy expansion. Accurate maps of pasture and other managed land covers are needed for monitoring, intercomparison, assessing potential uses, and planning. Yet, land maps can be generated from different types of classification datasets – i.e. as a land use or land cover type – as well as different sources. In this study our aim was to assess and compare land use and land cover definitions for pasture, and examine variability in the resulting pasture land classification maps. First, we conducted a review of pasture definitions in commonly used mapping databases. We then performed a case study involving Brazil, a dominant global producer of pasture-based livestock. Six geospatial databases were harmonized and compared to each other and to MODIS land cover for Brazil including the Cerrado and Amazon biomes, which are internationally recognized for their ecological value. Total pasture area estimates for Brazil ranged by a factor greater than four, from about 430,000 km² to over 1.7 million km². Our analysis showed high variability in pasture land maps depending on the definitions, methods and underlying datasets used to generate them. The results are illustrative of a symptomatic problem for all manage land datasets, demonstrating the need for land categories studies and geospatial data resources that fully define land terms and describe measurable management attributes. Additionally, the suitability of individual geospatial datasets for different types of land mapping must be better described and reported. These recommendations would help bring more consistency in the consideration of managed lands in research, reporting, and policy development, as demonstrated here for pasture land using six case study datasets from multiple sources.     

A Space‐Time Raster GIS Data Model for Spatiotemporal Analysis of Vegetation Responses to a Freeze Event

Many past space‐time GIS data models viewed the world mainly from a spatial perspective. They attached a time stamp to each state of an entity or the entire area of study. This approach is less efficient for certain spatio‐temporal analyses that focus on how locations change over time, which require researchers to view each location from a temporal perspective. In this article, we present a data model to organize multi‐temporal remote sensing datasets and track their changes at the individual pixel level. This data model can also integrate raster datasets from heterogeneous sources under a unified framework. The proposed data model consists of several object classes under a hierarchical structure. Each object class is associated with specific properties and behaviors to facilitate efficient spatio‐temporal analyses. We apply this data model to a case study of analyzing the impact of the 2007 freeze in Knoxville, Tennessee. The characteristics of different vegetation clusters before, during, and after the 2007 freeze event are compared. Our findings indicate that the majority of the study area is impacted by this freeze event, and different vegetation types show different response patterns to this freeze.     

ANTARCTIC GEOGRAPHIC DATA INTEGRATION(AGDI) “BRINGING IT ALL TOGETHER”

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Antarctic geographic data integration (AGDI) “Bringing it all together”

As digital data of Antarctica is being collected and created at an ever increasing rate, the usefulness of these data for research, GIS analysis and planning has been increasing. Currently there is no formal standard for these datasets to meet, which causes difficulties in integrating data from different sources. Feedback from scientists and researchers is that in order to perform any kind of GIS analyses on basic and fundamental datasets they need to have access to both: standardised and integrated data (a digital data product) and the ‘raw’ data in localised areas (a data library). Up until now there has been no coordinated approach or program to acquire, or even access fundamental datasets. The Antarctic Geographic Data Integration (AGDI) project is about bringing it all together, that is, to facilitate the availability of integrated fundamental GIS data for Antarctica through both a data product and a data library. This paper describes what AGDI is, why have it, what its aims are, details on the different components of AGDI and the status of various projects involved in the collection of fundamental datasets. It also briefly outlines what may happen to AGDI developments and a future work program.     

A GIS Approach to Estimation of Building Population for Micro-spatial Analysis

Population data used in GIS analyses is generally assumed to be homogeneous and planar (i.e. census tracts, townships or prefectures) due to the public unavailability of building population data. However, information on building population is required for micro-spatial analysis for improved disaster management and emergency preparedness, public facility management for urban planning, consumer and retail market analysis, environment and public health programs and other demographic studies. This article discusses a GIS approach using the Areametric and Volumetric methods for estimating building population based on census tracts and building footprint datasets. The estimated results were evaluated using actual building population data by visual, statistical and spatial means, and validated for use in micro-spatial analysis. We have also implemented a standalone GIS tool (known as 'PopShape GIS') for generating new building footprint with population attribute information based on user-defined criteria.     

Analysis at medium scale of low-resolution DInSAR data in slow-moving landslide-affected areas

Landslide studies over large areas call for multidisciplinary analyses supported by accurate ground displacement measurements. At present, conventional techniques can be valuably complemented by innovative satellite techniques such as Differential SAR Interferometry (DInSAR), furnishing huge amounts of data at competitively affordable costs. This work investigates the remote sensed data potential in landslide studies starting from the awareness of the present constraints of the technique. To this end, with reference to a sample area–within the territory of the National Basin Authority of Liri-Garigliano and Volturno rivers (Central-Southern Italy)–for which detailed base and thematic maps are available, quantitative examples of DInSAR data coverage on both different land-uses and landslide-affected areas are shown. Then, an original tool for “a priori DInSAR landslide visibility zoning” is proposed to address the choice of the most suitable image datasets. Finally, referring to the visible zones, the outcomes of DInSAR data for checking/updating landslide inventory maps at 1:25,000 scale highlight appealing perspectives, also holding the promise of obtaining relevant information in the landslide hazard evaluation.     

Using control data to determine the reliability of volunteered geographic information about land cover

There is much interest in using volunteered geographic information (VGI) in formal scientific analyses. This analysis uses VGI describing land cover that was captured using a web-based interface, linked to Google Earth. A number of control points, for which the land cover had been determined by experts allowed measures of the reliability of each volunteer in relation to each land cover class to be calculated. Geographically weighted kernels were used to estimate surfaces of volunteered land cover information accuracy and then to develop spatially distributed correspondences between the volunteer land cover class and land cover from 3 contemporary global datasets (GLC-2000, GlobCover and MODIS v.5). Specifically, a geographically weighted approach calculated local confusion matrices (correspondences) at each location in a central African study area and generated spatial distributions of user's, producer's, portmanteau, and partial portmanteau accuracies. These were used to evaluate the global datasets and to infer which of them was 'best’ at describing Tree cover at each location in the study area. The resulting maps show where specific global datasets are recommended for analyses requiring Tree cover information. The methods presented in this research suggest that some of the concerns about the quality of VGI can be addressed through careful data collection, the use of control points to evaluate volunteer performance and spatially explicit analyses. A research agenda for the use and analysis of VGI about land cover is outlined.     

The development of spatial decision support system tool for marine spatial planning

A major problem associated with marine spatial planning (MSP) involves the difficult and time-consuming practice of creating a scenario that encompasses complex datasets in near real time via the use of a simple spatial analysis method. Moreover, decision-makers require a reliable, user-friendly system to quickly and accessibly acquire accurate spatial planning information. The development of national spatial data infrastructure (NSDI), which links the spatial data of a nation’s many diverse institutions, may pave the way for the development of a tool that can better utilize spatial datasets, such as a spatial decision support system (SDSS). Thus, this project aimed to develop an SDSS for MSP and to evaluate the feasibility of its integration within the NSDI framework. The seaweed culture was selected as an example due to its economic and technological acceptance by traditional fishers. Additionally, a multi-criteria analysis was used to develop the tool. Furthermore, a feasibility evaluation of its implementation within the NSDI framework was conducted based on the Delphi method. The results of the assessment indicated that the SDSS can be incorporated into the NSDI framework by addressing the policy issue – one map policy, updating custodians’ decree and data, and improve the standard and protocol.     

基于Web地理可视化分析系统的研究与设计

气候研究涉及海量时空数据集,其产生和存储在分布式计算资源中。科学家们需要一个直观且方便地工具去研究分布式时空数据。地理可视化分析工具可直观且方便地访问气候资料、探索各种气候参数之间的关系及交流研究成果。本文对基于Web的地理可视化分析系统的研究与设计做一些探讨,阐述该系统具有以下功能：①互联网上海量数据集管理;②时空数据的2D/3D可视化;③气候研究中各种时空统计分析;④交互式数据分析和知识发现。此外,本文也为管理、分发、分析大数据提供参考。     

Assessing the potential of integrated Landsat 8 thermal bands,with the traditional reflective bands and derived vegetation indices in classifying urban landscapes

Reliable and up-to-date urban land cover information is valuable in urban planning and policy development. Due to the increasing demand for reliable land cover information there has been a growing need for robust methods and datasets to improve the classification accuracy from remotely sensed imagery. This study sought to assess the potential of the newly launched Landsat 8 sensor’s thermal bands and derived vegetation indices in improving land cover classification in a complex urban landscape using the support vector machine classifier. This study compared the individual and combined performance of Landsat 8’s reflective, thermal bands and vegetation indices in classifying urban land use-land cover. The integration of Landsat 8 reflective bands, derived vegetation indices and thermal bands overall produced significantly higher accuracy classification results than using traditional bands as standalone (i.e. overall, user and producer accuracies). An overall accuracy above 89.33% and a kappa index of 0.86, significantly higher than the one obtained with the use of the traditional reflective bands as a standalone data-set and other analysis stages. On average, the results also indicate high producer and user accuracies (i.e. above 80%) for most of the classes with a McNemar’s Z score of 9.00 at 95% confidence interval showing significant improvement compared with classification using reflective bands as standalone. Overall, the results of this study indicate that the integration of the Landsat 8’s OLI and TIR data presents an invaluable potential for accurate and robust land cover classification in a complex urban landscape, especially in areas where the availability of high resolution datasets remains a challenge.     

Important considerations about space‐time data: Modeling,scrutiny, and ratification

Space‐time data are becoming more abundant as time goes by, with hands‐on interest in them becoming more prevalent. These data have a very sensitive ordering in space and time, one that the simplest of recording errors can scramble. These data are also complex, containing both spatial and temporal autocorrelation coupled with their interaction. One goal of many researchers is to disentangle and account for these autocorrelation components in a parsimonious way. This article presents three competing model specifications to achieve this end. In addition, it outlines seven best practices for vetting space‐time datasets. This article cites a publicly available corrupt (containing at least errors of omission) rabies dataset to illustrate how a large volume of potentially valuable data can be rendered meaningless. In addition, it exemplifies postulated contentions about the United States National Cancer Institute Surveillance, Epidemiology, and End Results Program’s 1969–2018 population‐by‐county dataset, a collection of population counts held in high esteem. One major empirical finding is that this particular dataset exhibits traits that may merit remedial revisions action. A key conceptual finding is a suggested set of best practices for space‐time data proofreading. These two findings contribute to an ultimate goal of a large collection of certified open access space‐time datasets supporting repeatable and replicable scientific analyses.     

地表覆盖分类数据对区域森林叶面积指数反演的影响

以江西省吉安市为研究区,将5种全球地表覆盖分类数据(包括美国地质调查局(USGS)、马里兰大学(UMD)和波士顿大学(BU)生成的3套数据和欧洲生成的2套数据)以及由TM影像生成的区域地表覆盖分类数据,分别与MODIS1km反射率资料结合,利用基于4尺度几何光学模型的LAI反演方法生成研究区的LAI。在1km和4km两种尺度上将反演的LAI与TM资料生成的LAI进行比较,评价地表覆盖分类数据对LAI反演结果的影响。结果表明,TM和欧洲太空局的GLOBCOVER地表覆盖分类数据用于反演LAI的结果较好,在1km尺度上,反演的LAI与统计模型估算的TMLAI相关的R2分别为0.44和0.40,在4km尺度上的R2分别为0.57和0.54;其次为波士顿大学的MODIS地表覆盖分类数据,据其反演的LAI与TMLAI相关的R2在1km和4km尺度上分别为0.38和0.51;而马里兰大学的UMD和欧洲的GLC2000地表覆盖分类数据会导致反演的LAI存在较大误差,据其反演的LAI与TMLAI之间的一致性较差,在1km和4km两种尺度上平均偏低20%左右;LAI的反演结果对聚集度系数具有强的敏感性。该研究表明,为了提高区域/全球LAI反演精度,需要有高质量的地表覆盖分类数据。     

A methodology for least-squares local quasi-geoid modelling using a noisy satellite-only gravity field model

The paper is about a methodology to combine a noisy satellite-only global gravity field model (GGM) with other noisy datasets to estimate a local quasi-geoid model using weighted least-squares techniques. In this way, we attempt to improve the quality of the estimated quasi-geoid model and to complement it with a full noise covariance matrix for quality control and further data processing. The methodology goes beyond the classical remove–compute–restore approach, which does not account for the noise in the satellite-only GGM. We suggest and analyse three different approaches of data combination. Two of them are based on a local single-scale spherical radial basis function (SRBF) model of the disturbing potential, and one is based on a two-scale SRBF model. Using numerical experiments, we show that a single-scale SRBF model does not fully exploit the information in the satellite-only GGM. We explain this by a lack of flexibility of a single-scale SRBF model to deal with datasets of significantly different bandwidths. The two-scale SRBF model performs well in this respect, provided that the model coefficients representing the two scales are estimated separately. The corresponding methodology is developed in this paper. Using the statistics of the least-squares residuals and the statistics of the errors in the estimated two-scale quasi-geoid model, we demonstrate that the developed methodology provides a two-scale quasi-geoid model, which exploits the information in all datasets.     

A Back‐Propagation Neural Network‐Based Approach for Multi‐Represented Feature Matching in Update Propagation

Spatial data infrastructures, which are characterized by multi‐represented datasets, are prevalent throughout the world. The multi‐represented datasets contain different representations for identical real‐world entities. Therefore, update propagation is useful and required for maintaining multi‐represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real‐world entities and the detection of changes in updated datasets. Using polygon features of settlements as examples, this article addresses these key problems and proposes an approach for multi‐represented feature matching based on spatial similarity and a back‐propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in contrast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one‐to‐one matching but also to one‐to‐many and many‐to‐many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to demonstrate the weight differences in different data contexts and to measure the performance of the BPNN‐based feature matching approach.     

A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis

OpenStreetMap (OSM) is one of the most popular examples of a Volunteered Geographic Information (VGI) project. In the past years it has become a serious alternative source for geodata. Since the quality of OSM data can vary strongly, different aspects have been investigated in several scientific studies. In most cases the data is compared with commercial or administrative datasets which, however, are not always accessible due to the lack of availability, contradictory licensing restrictions or high procurement costs. In this investigation a framework containing more than 25 methods and indicators is presented, allowing OSM quality assessments based solely on the data's history. Without the usage of a reference data set, approximate statements on OSM data quality are possible. For this purpose existing methods are taken up, developed further, and integrated into an extensible open source framework. This enables arbitrarily repeatable intrinsic OSM quality analyses for any part of the world.     

New Data and Approaches for Urban Analysis: Modelling Residential Densities

The potential of Remote Sensing (RS) data for understanding urban environments is best unlocked by augmenting satellite information with ancillary sources of data. Integrating urban RS with new, often commercial, sources of socio-economic data offers the prospect of creating vastly enhanced models of the form and functioning of urban settlements. This paper takes some steps towards that end-goal by considering the contribution that four different datasets make to generating improved models of residential densities for a study region of Bristol, England. These datasets, available in the UK, are: the 1991 Census; a satellite image; the Ordnance Survey GB's Address-Point, and Code-Point products. The authors argue that closer RS-GIS integration will create new opportunities for modelling and understanding urban systems. Such models are considered a necessary pre-requisite for effective and sustainable management of urban environments.