Topic
Spatial ETL
About: Spatial ETL is a research topic. Over the lifetime, 12 publications have been published within this topic receiving 45 citations.
Papers
TL;DR: It is concluded that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.
Abstract: This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial data for 3D modelling. In our data-driven approach, we use a spatial ETL solution to reconstruct a 3D building model from a dense image matching point cloud which was obtained beforehand from UAV imagery. The results are 3D building models in a semantic vector format consistent with the OGC CityGML standard, Level of Detail 2 (LOD2). The approach has been tested on selected buildings in a simple semi-urban area. We conclude that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.
21 citations
13 May 2016
TL;DR: This work proposes a multi-agent-based solution to adequately schedule and balance the processing activities over the grid while allowing a joint use of real-time and archive data for personalized reporting and visualization of services envisioned to the decision-makers who are using the same CPS application.
Abstract: Thanks to their spatially distributed sensors, cyber-physical system (CPS) applications are currently collecting large amounts of heterogeneous data. When it comes to allowing several decision-makers to collaboratively plan their actions, these applications need appropriate tools for an efficient storage, analysis, and visualization of the available data. Spatial data warehouses (SDWs) have proven their efficiency in carrying out these operations. However, because of the increasing volumes of data, the commonly used spatial extract-transform-load (SETL) process generally fails to update the SDW within acceptable timeframes. In order to solve this problem, we propose to perform the SETL tasks in a distributed, parallel manner by means of a grid of computing resources. In addition to being the unique solution that uses grid computing for the SETL process of SDWs, our solution makes use of cloud computing techniques to shorten the spatial data processing time and reduce resource consumption. To meet our goals, we propose a multi-agent-based solution to adequately schedule and balance the processing activities over the grid while allowing a joint use of real-time and archive data for personalized reporting and visualization of services envisioned to the decision-makers who are using the same CPS application.
19 citations
Journal Article•
TL;DR: The authors present a geoscience spatial data warehouse architecture that conforms to China's national conditions and have five levels, i.e. the data source, spatial ETL, spatial data storage, application service based on SOA and client application, and a three-level physical deployment scheme.
Abstract: The authors took the geoscience spatial data warehouse as a scheme of data integration in order to integrate multi-source, heterogeneous and disperse geological data of China and provide effective data for resource assessmentThey for the first time present a geoscience spatial data warehouse architecture that conforms to China's national conditions and have five levels,iethe data source, spatial ETL,spatial data storage,application service based on SOA and client applicationThe authors designed a three-level(state,ad- ministrative regions and provinces)physical deployment scheme for the geoscience spatial data warehouse system according to the ad- ministration regions of China's geological work and distribution of dataIt can realize the objectives of geoscience data integration Research results show that this is a complete and feasible geoscience data integration scheme that conforms to the actual situation of geoscience of China
3 citations
1 Oct 2018
TL;DR: A spatial rule processing engine that performs spatial objectification based on non - spatial information types in useful information based on user-defined rules and table-related information is proposed.
Abstract: Recently, spatial information has been increasing in many kinds of producers such as SNS (Social Network Service), news, and portal site. Researches are also being actively conducted to analyze useful information in various kinds of spatial information. There are various ways to analyze such useful information, such as trend analysis, hot spot analysis, and emotion analysis. However, non-spatial information in useful information should be classified as simple characters or converted into spatial objects. In this paper, we propose a spatial rule processing engine that performs spatial objectification based on non - spatial information types in useful information. The proposed spatial rule processing engine first judges whether a schema is retained according to various types of spatial information inputted. And creates a new schema and a spatial table according to the user-defined rule and table-related information. The information generated by the user-defined rules and related information is sent to the distributed spatial ETL processing engine. And extract, transforms, and stores them in Hadoop linked by each generation event.
2 citations
TL;DR: A spatial ETL tool that allows interoperability between spatial and non-spatial data is presented in this article, and a more detailed definition of the ETL process is introduced to acquaint the reader with the FME Desktop tool.
Abstract: A spatial ETL tool that allows interoperability between
spatial and non-spatial data is presented in this article.
The primary goal of the tool is to provide spatial data
processing and transformation among various data
formats. This is made possible by the ETL process,
which extracts, transforms and loads data. The use of
spatial data has become significant in everyday life,
because only correctly applying the data enables users
to extract the true value spatial data offers. The main
purpose of this article is to demonstrate the capability
and usability of the spatial ETL tool, in order to
introduce a more detailed definition of the ETL
process to acquaint the reader with the FME Desktop
tool, and to demonstrate the applicability of the tool
in two case studies. In the first case, a unified spatial
data warehouse is built from non-homogeneous data
warehouses in order to assess the impacts and effects
the geological basis had on the amount of damage to
buildings in the 2004 earthquake. The second case
demonstrates how the spatial ETL tool can be used to
inform locals of predicted spatial changes in the area.
The flexibility and the efficiency of the spatial ETL tool
are successfully demonstrated in both cases; ETL turns
out to be a robust tool for editing and analysing data.
2 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2020 | 1 |
| 2019 | 1 |
| 2018 | 1 |
| 2017 | 1 |
| 2016 | 2 |
| 2015 | 1 |