14,735 research outputs found
Collaborative Mapping of London Using Google Maps: The LondonProfiler
This paper begins by reviewing the ways in which the innovation of Google Maps has transformed our ability to reference and view geographically referenced data. We describe the ways in which the GMap Creator tool developed under the ESRC National Centre for E Social Science programme enables users to ‘mashup’ thematic choropleth maps using the Google API. We illustrate the application of GMap Creator using the example of www.londonprofiler.org, which makes it possible to view a range of health, education and other socioeconomic datasets against a backcloth of Google Maps data. Our conclusions address the ways in which Google Map mashups developed using GMap Creator facilitate online exploratory cartographic visualisation in a range of areas of policy concern
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Artificial neural networks in geospatial analysis
Artificial neural networks are computational models widely used in geospatial analysis for data classification, change detection, clustering, function approximation, and forecasting or prediction. There are many types of neural networks based on learning paradigm and network architectures. Their use is expected to grow with increasing availability of massive data from remote sensing and mobile platforms
An Approach to Publish Spatial Data on the Web: The GeoLinked Data Case
In this paper we report on an ongoing process aimed at publishing hydrographical data on the Web with a Spanish GeoLinked Data Use Case. Moreover, we discuss the process we followed, and propose methodological guidelines for all the activities involved within the process
Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services
One of the most widely-implemented service standards provided by the Open
Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS).
WMS is widely employed globally, but there is limited knowledge of the global
distribution, adoption status or the service quality of these online WMS
resources. To fill this void, we investigated global WMSs resources and
performed distributed performance monitoring of these services. This paper
explicates a distributed monitoring framework that was used to monitor 46,296
WMSs continuously for over one year and a crawling method to discover these
WMSs. We analyzed server locations, provider types, themes, the spatiotemporal
coverage of map layers and the service versions for 41,703 valid WMSs.
Furthermore, we appraised the stability and performance of basic operations for
1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major
reasons for request errors and performance issues, as well as the relationship
between service response times and the spatiotemporal distribution of client
monitoring sites. This paper will help service providers, end users and
developers of standards to grasp the status of global WMS resources, as well as
to understand the adoption status of OGC standards. The conclusions drawn in
this paper can benefit geospatial resource discovery, service performance
evaluation and guide service performance improvements.Comment: 24 pages; 15 figure
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Bridging between sensor measurements and symbolic ontologies through conceptual spaces
The increasing availability of sensor data through a variety of sensor-driven devices raises the need to exploit the data observed by sensors with the help of formally specified knowledge representations, such as the ones provided by the Semantic Web. In order to facilitate such a Semantic Sensor Web, the challenge is to bridge between symbolic knowledge representations and the measured data collected by sensors. In particular, one needs to map a given set of arbitrary sensor data to a particular set of symbolic knowledge representations, e.g. ontology instances. This task is particularly challenging due to the potential infinite variety of possible sensor measurements. Conceptual Spaces (CS) provide a means to represent knowledge in geometrical vector spaces in order to enable computation of similarities between knowledge entities by means of distance metrics. We propose an ontology for CS which allows to refine symbolic concepts as CS and to ground instances to so-called prototypical members described by vectors. By computing similarities in terms of spatial distances between a given set of sensor measurements and a finite set of prototypical members, the most similar instance can be identified. In that, we provide a means to bridge between the real-world as observed by sensors and symbolic representations. We also propose an initial implementation utilizing our approach for measurement-based Semantic Web Service discovery
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