Multi-resolution seamless image database

This paper presents the basic concepts and principles, data structure and high efficient spatial index for multi-resolution image database. The database is characterized by arrangement of multi-resource image data and seamless mosaic, distribution-based storage and management, integration with other spatial database software such as GeoStar and GeoGrid developed by Wuhan Technical University of Surveying and Mapping.     

Development of a data mining method for land control

Land resources are facing crises of being misused, especially for an intersection area between town and country, and land control has to be enforced. This paper presents a development of data mining method for land control. A vector-match method for the prerequisite of data mining i. e., data cleaning is proposed, which deals with both character and numeric data via vectorizing character-string and matching number. A minimal decision algorithm of rough set is used to discover the knowledge hidden in the data warehouse. In order to monitor land use dynamically and accurately, it is suggested to set up a real-time land control system based on GPS, digital photogrammetry and online data mining. Finally, the means is applied in the intersection area between town and country of Wuhan city, and a set of knowledge about land control is discovered.     

Data model and visualization of 3D city landscape based on integrated databases

Data acquisition and modeling are the two important, difficult and costful aspects in a Cybercity project. 2D-GIS is mature and can manage a lot of spatial data. Thus 3D-GIS should make the best of data and technology of 2D-GIS. Construction of a useful synthetic environment requires integration of multiple types of information like DEM, texture images and 3D representation of objects such as buildings. In this paper, the method for 3D city landscape data model and visualization based on integrated databases is presented. Since, the data volume of raster are very huge, special strategies (for example, pyramid gridded method) must be adopted in order to manage raster data efficiently. Three different methods of data acquisition, the proper data structure and a simple modeling method are presented as well. At last, a pilot project of Shanghai Cybercity is illustrated.     

Accuracy analysis for SST gravity field model in China

Taking China as the region for test the potential of the new satellite gravity technique, satelliteto-satellite tracking for improving the accuracy of regional gravity field model is studied. With WDM94 as reference, the gravity anomaly residuals of three models, the latest two GRACE global gravity field model (EIGEN_GRACE02S, GGM02S) and EGM96, are computed and compared. The causes for the differences among the residuals of the three models are discussed. The comparison between the residuals shows that in the selected region, EIGEN_GRACE02S or GGM02S is better than EGM96 in lower degree part (less than 110 degree). Additionally, through the analysis of the model gravity anomaly residuals, it is found that some systematic errors with periodical properties exist in the higher degree part of EIGEN and GGM models, the results can also be taken as references in the validation of the SST gravity data.     

Space,time, and situational awareness in natural hazards: a case study of Hurricane Sandy with social media data

ABSTRACT

Various methods have been developed to investigate the geospatial information, temporal component, and message content in disaster-related social media data to enrich human-centric information for situational awareness. However, few studies have simultaneously analyzed these three dimensions (i.e. space, time, and content). With an attempt to bring a space–time perspective into situational awareness, this study develops a novel approach to integrate space, time, and content dimensions in social media data and enable a space–time analysis of detailed social responses to a natural disaster. Using Markov transition probability matrix and location quotient, we analyzed the Hurricane Sandy tweets in New York City and explored how people’s conversational topics changed across space and over time. Our approach offers potential to facilitate efficient policy/decision-making and rapid response in mitigations of damages caused by natural disasters.     

Point grid map: a new type of thematic map for statistical data associated with geographic points

ABSTRACT

Social, economic, and environmental statistical data associated with geographic points are currently globally available in large amounts. When conventional thematic maps, such as proportional symbol maps or point diagram maps, are used to represent these data, the maps appear cluttered if the point data volumes are relatively large or cover a relatively dense region. To overcome these limitations, we propose a new type of thematic map for statistical data associated with geographic points: the point grid map. In a point grid map, an input point data set is transformed into a grid in which each point is represented by a square grid cell of equal size while preserving the relative position of each point, which leads to a clear and uncluttered appearance, and the grid cells can be shaded or patterned with symbols or diagrams according to the attributes of the points. We present an algorithm to construct a point grid map and test it with several simulated and real data sets. Furthermore, we present some variants of the point grid map.     

Error analysis of the 3D similarity coordinate transformation

The 3D similarity coordinate transformation with the Gauss–Helmert error model is investigated. The first-order error analysis of an analytical least-squares solution to this problem is developed in detail. While additive errors are assumed in the translation and scale estimates, a 3 × 1 multiplicative error vector is defined to effectively parameterize the rotation matrix estimation error. The propagation of the errors in the coordinate measurements to the errors in the estimated transformation parameters is derived step-by-step, and the formulae for calculating the variance–covariance matrix of the estimated parameters are presented.     

High-Resolution Forecast Models of Water Vapor Over Mountains: Comparison With MERIS and Meteosat Data

Propagation delay due to variable tropospheric water vapor (WV) is one of the most intractable problems for radar interferometry, particularly over mountains. The WV field can be simulated by an atmospheric model, and the difference between the two fields is used to correct the radar interferogram. Here, we report our use of the U.K. Met Office Unified Model in a nested mode to produce high-resolution forecast fields for the 3-km-high Mount Etna volcano. The simulated precipitable-water field is validated against that retrieved from the Medium-Resolution Imaging Spectrometer (MERIS) radiometer on the Envisat satellite, which has a resolution of 300 m. Two case studies, one from winter (November 24, 2004) and one from summer (June 25, 2005), show that the mismatch between the model and the MERIS fields ( rms = 1.1 and 1.6 mm, respectively) is small. One of the main potential sources of error in the models is the timing of the WV field simulation. We show that long-wavelength upper tropospheric troughs of low WV could be identified in both the model output and Meteosat WV imagery for the November 24, 2004 case and used to choose the best time of model output.     

Signal acquisition of Luojia-1A low earth orbit navigation augmentation system with software defined receiver

Low Earth Orbit (LEO) satellite navigation signal can be used as an opportunity signal in the case of a Global Navigation Satellite System (GNSS) outage, or as ...     

Exploring Real‐Time Geoprocessing in Cloud Computing: Navigation Services Case Study

Today, many real‐time geospatial applications (e.g. navigation and location‐based services) involve data‐ and/or compute‐intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real‐time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real‐time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data‐ and/or compute‐intensive geospatial applications, current cloud platforms require improvements and special tools for handling real‐time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real‐time geoprocessing in clouds.