HL
Haicheng Liu
Authored
15 records found
Management of large indoor point clouds
An initial exploration
Indoor navigation and visualization become increasingly important nowadays. Meanwhile, the proliferation of new sensors as well as the advancement of data processing provide massive point clouds to model the indoor environment in high accuracy. However, current state-of-the-art s
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nD-PointCloud Data Management
Continuous levels, adaptive histograms, and diverse query geometries
In the Geomatics domain, a point cloud refers to a data set which records the coordinates and other attributes of a huge number of points. Conceptually, each of these attributes can be regarded as a dimension, representing a specific type of information. Apart from routinely conc
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Managing large multidimensional hydrologic datasets
A case study comparing NetCDF and SciDB
Management of large hydrologic datasets including storage, structuring, clustering, indexing, and query is one of the crucial challenges in the era of big data. This research originates from a specific problem: time series extraction at specific locations takes a long time when a
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Managing large multidimensional hydrologic datasets
A case study comparing NetCDF and SciDB
Management of large hydrologic datasets including storage, structuring, clustering, indexing, and query is one of the crucial challenges in the era of big data. This research originates from a specific problem: time series extraction at specific locations takes a long time when a
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Governments use flood maps for city planning and disaster management to protect people and assets. Flood risk mapping projects carried out for these purposes generate a huge amount of modelling results. Previously, data submitted are highly condensed products such as typical f ...
Space Filling Curve (SFC) mapping-based clustering and indexing works effectively for point clouds management and querying. It maps both points and queries into a one-dimensional SFC space so that B+- tree could be utilized. Based on the basic structure, this paper develops a gen
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Point clouds have become one of the most popular sources of data in geospatial fields due to their availability and flexibility. However, because of the large amount of data and the limited resources of mobile devices, the use of point clouds in mobile Augmented Reality applicati
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Efficient spatial queries are frequently needed to extract useful information from massive nD point clouds. Most previous studies focus on developing solutions for orthogonal window queries, while rarely considering the polytope query. The latter query, which includes the widely
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Dramatically increasing collection of point clouds raises an essential demand for highly efficient data management. It can also facilitate modern applications such as robotics and virtual reality. Extensive studies have been performed on point data management and querying, but mo
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Digital elevation models (DEM) are widely used in various distributed
hydrological models. The stream network can be extracted from it so that
runoff routing can be calculated. With the advent of remote sensing and
computing technologies, the computation based on DEM with high
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Drastically increasing production of point clouds as well as modern application fields like robotics and virtual reality raises essential demand for smart and highly efficient data management. Effective tools for the managing and direct use of large point clouds are missing. Curr
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Point cloud is made up of a multitude of three-dimensional (3D) points with one or more attributes attached. Point cloud is the third data paradigm in addition to the well-established object (vector) and gridded (raster) representations, since point cloud data can be directly col
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As an extension to 2D polygonal queries, the nD-polytope queries on point clouds also play a crucial rolein nD GIS applications such as the perspective view selection. This report rst denes the nD-polytopemathematically, and then develops an ecient nD-polytope querying solution b
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In this paper we propose to treat point clouds as a first-class representation (similar to vector or raster representations), with the nD-PointCloud as the solution for this, offering deep integration of space, time and scale. For efficiency rea-sons spatial indexing and clusteri
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Contributed
2 records found
Visualizing the point clouds is an integral part of processing the data, which enables users to explore and interact with the point clouds more intuitively. However, most of the current point cloud renderers are developed in non-immersive environments. In the last few years, some
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This MSc thesis aims to research how to efficiently manage 4D AIS data (Longitude, Latitude, Time, and MMSI (the ID of the vessel)) to do the fast query by using the Space Filling curve in PostgreSQL. The AIS is the Automatic Identification System which is born because the freque
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