A RESTful proxy and data model for linked sensor data

Abstract

The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration. So far, sensor data are stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies toward their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (1) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (2) how to establish meaningful links between those data sets, that is, observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGC's Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.     

A tree counting algorithm for precision agriculture tasks

Abstract

This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data. Depending on site-specific pruning practices, the morphologic characteristics of tree crowns may generate one or more brightness peaks (tree top) on the imagery. To optimize tree counting and to minimize typical background noises from orchards (i.e. bare soil, weeds, and man-made objects), a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands (asymmetrical smoothing filter, local minimum filter, mask layer, and spatial aggregation operator). System performance was evaluated through objective criteria, showing consistent results in fast capturing tree position for precision agriculture tasks.     

A novel method for discovering spatio-temporal clusters of different sizes,shapes, and densities in the presence of noise

The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery. In this paper, a novel spatio-temporal clustering method based on spatio-temporal shared nearest neighbors (STSNN) is proposed to detect spatio-temporal clusters of different sizes, shapes, and densities in spatio-temporal databases with a large amount of noise. The concepts of windowed distance and shared nearest neighbor are utilized to define a novel spatio-temporal density for a spatio-temporal entity with definite mathematical meanings. Then, the density-based clustering strategy is employed to uncover spatio-temporal clusters. The spatio-temporal clustering algorithm developed in this paper is easily implemented and less sensitive to density variation among spatio-temporal entities. Experiments are undertaken on several simulated data-sets to demonstrate the effectiveness and advantage of the STSNN algorithm. Also, the real-world applications on two seismic databases show that the STSNN algorithm has the ability to uncover foreshocks and aftershocks effectively.     

Geoprocessing in Cloud Computing platforms – a comparative analysis

Abstract

The emergence of Cloud Computing technologies brings a new information infrastructure to users. Providing geoprocessing functions in Cloud Computing platforms can bring scalable, on-demand, and cost–effective geoprocessing services to geospatial users. This paper provides a comparative analysis of geoprocessing in Cloud Computing platforms – Microsoft Windows Azure and Google App Engine. The analysis compares differences in the data storage, architecture model, and development environment based on the experience to develop geoprocessing services in the two Cloud Computing platforms; emphasizes the importance of virtualization; recommends applications of hybrid geoprocessing Clouds, and suggests an interoperable solution on geoprocessing Cloud services. The comparison allows one to selectively utilize Cloud Computing platforms or hybrid Cloud pattern, once it is understood that the current development of geoprocessing Cloud services is restricted to specific Cloud Computing platforms with certain kinds of technologies. The performance evaluation is also performed over geoprocessing services deployed in public Cloud platforms. The tested services are developed using geoprocessing algorithms from different vendors, GeoSurf and Java Topology Suite. The evaluation results provide a valuable reference on providing elastic and cost-effective geoprocessing Cloud services.     

Data democracy – increased supply of geospatial information and expanded participatory processes in the production of data

The global landscape in the supply, co-creation and use of geospatial data is changing very rapidly with new satellites, sensors and mobile devices reconfiguring the traditional lines of demand and supply and the number of actors involved. In this paper we chart some of these technology-led developments and then focus on the opportunities they have created for the increased participation of the public in generating and contributing information for a wide range of uses, scientific and non. Not all this information is open or geospatial, but sufficiently large portions of it are to make it one of the most significant phenomena of the last decade. In fact, we argue that while satellite and sensors have exponentially increased the volumes of geospatial information available, the participation of the public is transformative because it expands the range of participants and stakeholders in society using and producing geospatial information, with opportunities for more direct participation in science, politics and social action.     

Steerable filtering in interactive tracing of archaeological linear features using digital true colour aerial images

In this study, a semi-automatic approach to support archaeological line tracing is proposed. The suggested procedure is based on colour and texture information derived from orthorectified RGB digital aerial data and consists of four steps: (1) line sketching; (2) steerable filtering; (3) objects selection; and (4) straight line fitting and vectorisation. Good results were observed by evaluating the algorithm according to trace visibility and integrity, global difficulty and level of feature extraction. Further reliability tests were performed to study poor data initialisation and different annual seasonal land use at the same site.     

Service-oriented infrastructure for flood mapping using optical and SAR satellite data

In this paper, we present the service-oriented infrastructure within the Wide Area Grid project that was carried out within the Working Group on Information Systems and Services of the Committee on Earth Observation Satellites. The developed infrastructure integrates services and computational resources of several regional and national Grid systems: Ukrainian Academician Grid (with satellite data processing Grid segment, UASpaceGrid) and Grid system at the Center on Earth Observation and Digital Earth of Chinese Academy of Sciences. The study focuses on integrating geo-information services on flood mapping provided by Ukrainian and Chinese entities to benefit from information acquired from multiple sources. We also describe services for workflow automation and management in Grid environment and provide an example of workflow automation for generating flood maps from optical and synthetic-aperture radar satellite imagery. We also discuss issues of enabling trust for the infrastructure using certificates and reputation-based model. Applications of utilizing the developed infrastructure for operational flood mapping in Ukraine and China are given as well.     

The scheme for the database building and updating of 1∶10 000 digital elevation models

The National Bureau of Surveying and Mapping of China has planned to speed up the development of spatial data infrastructure (SDI) in the coming few years. This SDI consists of four types of digital products, i. e., digital orthophotos, digital elevation models, digital line graphs and digital raster graphs. For the DEM, a scheme for the database building and updating of 1∶10 000 digital elevation models has been proposed and some experimental tests have also been accomplished. This paper describes the theoretical (and/or technical) background and reports some of the experimental results to support the scheme. Various aspects of the scheme such as accuracy, data sources, data sampling spatial resolution, terrain modeling, data organization, etc are discussed.     

Computational complexity of spatial reasoning with directional relationship

The property of NP-completeness of topologic spatial reasoning problem has been proved. According to the similarity of uncertainty with topologic spatial reasoning, the problem of directional spatial reasoning should be also an NP-complete problem. The proof for the property of NP-completeness in directional spatial reasoning problem is based on two important transformations. After these transformations, a spatial configuration has been constructed based on directional constraints, and the property of NP-completeness in directional spatial reasoning has been proved with the help of the consistency of the constraints in the configuration.     

An optimum vehicular path algorithm for traffic network based on hierarchical spatial reasoning

Human beings’ intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms. It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic, so as to improve running efficiency and suitability of shortest path algorithm for traffic network. The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path. It is argued that the shortest path, no matter distance shortest or time shortest, is usually not the favorite of drivers in practice. Some factors difficult to expect or quantify influence the drivers’ choice greatly. It makes the drivers prefer choosing a less shortest, but more reliable or flexible path to travel on. The presented optimum path algorithm, in addition to the improvement of the running efficiency of shortest path algorithms up to several times, reduces the emergence of those factors, conforms to the intellection characteristic of human beings, and is more easily accepted by drivers. Moreover, it does not require the completeness of networks in the lowest hierachy and the applicability and fault tolerance of the algorithm have improved. The experiment result shows the advantages of the presented algorithm. The authors argued that the algorithm has great potential application for navigation systems of large-scale traffic networks.