scholarly journals Review of the Progress and Process of Satellite Imagery and Road Databases Conflation

2018 ◽  
Author(s):  
Mohamed Z.Jber ◽  
Dieter Fritsch
Keyword(s):  
Spatial Data ◽  
Road Network ◽  
Feature Matching ◽  
Primary Objective ◽  
Geospatial Data ◽  
Data Sources ◽  
Network Data ◽  
Incremental Updating ◽  
Heterogeneous Nature ◽  
State Of Art

Spatial data conflation plays a fundamental role in in many aspects of modern Geographic Information Systems (GIS) research and development such as geospatial data visualization, incremental updating of databases and disaster evaluation. The primary objective of conflation is to derive valuable information based on the comparison of multiple spatial data sources of homogeneous or heterogeneous nature. This paper reviews the state of art of the concept of conflation, feature matching, the most important progresses of conflation between image and road network data, and the complexity of spatial conflation.

scholarly journals Identifying Complex Junctions in a Road Network

2020 ◽  
Vol 10 (1) ◽  
pp. 4
Author(s):  
Jianting Yang ◽  
Kongyang Zhao ◽  
Muzi Li ◽  
Zhu Xu ◽  
Zhilin Li
Keyword(s):  
Spatial Data ◽  
Road Network ◽  
Structural Complexity ◽  
Network Data ◽  
Map Generalization ◽  
Test Results ◽  
Geometric Boundary ◽  
Road Data ◽  
Complex Junction ◽  
Topological Boundary

Automated generalization of road network data is of great concern to the map generalization community because of the importance of road data and the difficulty involved. Complex junctions are where roads meet and join in a complicated way and identifying them is a key issue in road network generalization. In addition to their structural complexity, complex junctions don’t have regular geometric boundary and their representation in spatial data is scale-dependent. All these together make them hard to identify. Existing methods use geometric and topological statistics to characterize and identify them, and are thus error-prone, scale-dependent and lack generality. More significantly, they cannot ensure the integrity of complex junctions. This study overcomes the obstacles by clarifying the topological boundary of a complex junction, which provides the basis for straightforward identification of them. Test results show the proposed method can find and isolate complex junctions in a road network with their integrity and is able to handle different road representations. The integral identification achieved can help to guarantee connectivity among roads when simplifying complex junctions, and greatly facilitate the geometric and semantic simplification of them.

scholarly journals Development of framework for aggregation and visualization of three-dimensional (3D) spatial data

2021 ◽  
Author(s):  
Mihal Miu ◽  
Xiaokun Zhang ◽  
M. Ali Akber Dewan ◽  
Junye Wang
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Spatial Information ◽  
Three Dimensional ◽  
Geospatial Data ◽  
Third Party ◽  
Data Sources ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Geospatial Information

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

scholarly journals Geospatial Data Processing for Biometeorology. Mapping the Difference of Oxygen in the Atmospheric Surface

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 19
Author(s):  
Benjamin Arroquia-Cuadros ◽  
Ángel Marqués-Mateu ◽  
Laura Sebastiá ◽  
Pablo Fdez-Arroyabe
Keyword(s):  
Data Analysis ◽  
Data Processing ◽  
Spatial Data ◽  
Geospatial Data ◽  
Data Sources ◽  
Sources Of Information ◽  
Data Formats ◽  
Data Files ◽  
The Difference ◽  
Scientific Fields

Biometeorology is the field that relates meteorological and climatic variables with humans, animals and the environment in order to be studied jointly with their geographical distribution. The wide variety of data sources and the highly specialised data formats are fundamental issues for users in this area. This paper presents some preliminary results and several underlying technologies used to create a system to manage spatial data. Some sources of information are presented as a basis for biometeorological studies, together with a procedure for downloading and transforming the datasets. The resulting maps and data files derived from this study are useful for data analysis in other scientific fields.

scholarly journals Positional Accuracy Assessment for Updating Authoritative Geospatial Datasets Based on Open Source Data and Remotely Sensed Images

2020 ◽  
Vol 26 (2) ◽  
pp. 70-84 ◽  
Author(s):  
Mervat S. Jasem ◽  
Odey AL-Hamadani
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Open Source ◽  
Spatial Data ◽  
Road Network ◽  
Data Sources ◽  
National Standard ◽  
Positional Accuracy ◽  
Data Collection Methods ◽  
Collection Methods

OpenStreetMap (OSM) represents the most common example of online volunteered mapping applications. Most of these platforms are open source spatial data collected by non-experts volunteers using different data collection methods. OSM project aims to provide a free digital map for all the world. The heterogeneity in data collection methods made OSM project databases accuracy is unreliable and must be dealt with caution for any engineering application. This study aims to assess the horizontal positional accuracy of three spatial data sources are OSM road network database, high-resolution Satellite Image (SI), and high-resolution Aerial Photo (AP) of Baghdad city with respect to an analogue formal road network dataset obtained from the Mayoralty of Baghdad (MB). The methodology of, U.S. National Standard Spatial Data Accuracy (NSSDA) was applied to measure the degree of agreement between each data source and the formal dataset (MB) in terms of horizontal positional accuracy by computing RMSE and NSSDA values. The study concluded that each of the three data sources does not agree with the MB dataset in both study sites AL-Aadhamiyah and AL-Kadhumiyah in terms of positional accuracy.

scholarly journals Development of framework for aggregation and visualization of three-dimensional (3D) spatial data

2021 ◽  
Author(s):  
Mihal Miu ◽  
Xiaokun Zhang ◽  
M. Ali Akber Dewan ◽  
Junye Wang
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Spatial Information ◽  
Three Dimensional ◽  
Geospatial Data ◽  
Third Party ◽  
Data Sources ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Geospatial Information

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

scholarly journals Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Data Sources ◽  
Communist Regime ◽  
Corine Land Cover ◽  
Cropland Area ◽  
Cropland Abandonment ◽  
Multi Temporal

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

The 3rd ACM SIGSPATIAL International Workshop on Geospatial Simulation

2021 ◽  
Vol 12 (3) ◽  
pp. 11-14
Author(s):  
Joon-Seok Kim ◽  
Taylor Anderson ◽  
Ashwin Shashidharan ◽  
Andreas Züfle
Keyword(s):  
Spatial Data ◽  
International Workshop ◽  
Data Sources ◽  
Relative Location ◽  
Technological Changes ◽  
Research Opportunities ◽  
New Methodologies ◽  
New Research

Space has long been acknowledged by researchers as a fundamental constraint which shapes our world. As technological changes have transformed the very concept of distance, the relative location and connectivity of geospatial phenomena have remained stubbornly significant in how systems function. At the same time, however, technology has advanced the science of geospatial simulation to bear on our understanding of how such systems work. While previous generations of scientists and practitioners were unable to gather spatial data or to incorporate it into models at any meaningful scale, new methodologies and data sources are becoming increasingly available to researchers, developers, users, and practitioners. These developments present new research opportunities for geospatial simulation.

scholarly journals Waterlogging Risk Assessment: An Undervalued Disaster Risk in Coastal Urban Community of Chattogram, Bangladesh

Earth ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 151-173
Author(s):  
Md. Rezuanul Islam ◽  
Debasish Roy Raja
Keyword(s):  
Spatial Data ◽  
Risk Index ◽  
Structural Data ◽  
Economic Loss ◽  
Primary Objective ◽  
Social Infrastructure ◽  
Simple Method ◽  
City Area ◽  
Inventory Map ◽  
The City

In recent years, rainfall-induced waterlogging has become a common hazard in the highly urbanized coastal city of Chattogram, Bangladesh, resulting in a high magnitude of property damage and economic loss. Therefore, the primary objective of this research was to prepare a waterlogging inventory map and understand the spatial variations of the risk by means of hazard intensity, exposure, and vulnerability of waterlogging. In this research, the inventory map and factors influencing waterlogging hazards were determined from a participatory survey, and other spatial data, including land elevation, population, and structural data, were collected from secondary sources. The analytical hierarchy process was applied to measure the hazard intensity, and the exposure and vulnerability were estimated by overlaying the spatial data onto the hazard intensity map. A total of 58 locations were identified as waterlogging affected, which covered ~8.42% of the city area. We showed that ~3.03% of the city area was greatly vulnerable to waterlogging in terms of their social, infrastructure, critical facilities, economic, and environmental vulnerabilities. The obtained waterlogging risk index map suggested that ~2.71% of the study area was at very high risk, followed by moderate (~0.15%), low (~3.89%), and very low (~1.67%). The risk analysis presented in this study was a simple method that can be applied to assess the relative risk of waterlogging in different regions, and the results were applicable to the prevention and mitigation of waterlogging for Chattogram City.

A Framework of Local Geospatial Data Infrastructure for Sustainable Urban Development

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
Azman Ariffin ◽  
Nabila Ibrahim ◽  
Ghazali Desa ◽  
Uznir Ujang ◽  
Hishamuddin Mohd Ali ◽  
...  
Keyword(s):  
Urban Development ◽  
Spatial Data ◽  
Geospatial Data ◽  
Sustainable Urban Development ◽  
Data Infrastructure ◽  
Shape Category ◽  
Key Dimensions ◽  
Data Layers ◽  
Conceptual Data

This paper addresses the need to develop a Local Geospatial Data Infrastructure (LGDI) for sustainable urban development. This research will highlight the effective and efficient framework for the development of local infrastructure. This paper presents a framework (a combination of domain based and goal based frameworks) for developing a Local Geospatial Data Infrastructure. The basis of this research is on a case study conducted in a Malaysian city. The main focus of the case study was on measuring and assessing sustainability. Six conceptual frameworks were produced based on 6 key dimensions of sustainability. The developed framework consists of 6 conceptual data models and 6 conceptual data structures. It was concluded that 30 spatial data layers are needed of which 12 data layers are categorized as point shape, 17 data layers are categorized as polygon shape and 1 data layer as line shape category.

scholarly journals WPS ENABLED SDI: AN OPEN SOURCE APPROACH TO PROVIDE GEOPROCESSING IN WEB ENVIRONMENT

2019 ◽  
Vol IV-5/W2 ◽  
pp. 119-126
Author(s):  
A. K. Tripathi ◽  
S. Agrawal ◽  
R. D. Gupta
Keyword(s):  
Open Source ◽  
Spatial Data ◽  
Geospatial Data ◽  
Distributed Data ◽  
Data Infrastructure ◽  
Web Environment ◽  
Local Resources ◽  
Processing Service ◽  
Server Architecture ◽  
The Web

Abstract. Sharing and management of geospatial data among different communities and users is a challenge which is suitably addressed by Spatial Data Infrastructure (SDI). SDI helps people in the discovery, editing, processing and visualization of spatial data. The user can download the data from SDI and process it using the local resources. However, large volume and heterogeneity of data make this processing difficult at the client end. This problem can be resolved by orchestrating the Web Processing Service (WPS) with SDI. WPS is a service interface through which geoprocessing can be done over the internet. In this paper, a WPS enabled SDI framework with OGC compliant services is conceptualized and developed. It is based on the three tier client server architecture. OGC services are provided through GeoServer. WPS extension of GeoServer is used to perform geospatial data processing and analysis. The developed framework is utilized to create a public health SDI prototype using Open Source Software (OSS). The integration of WPS with SDI demonstrates how the various data analysis operations of WPS can be performed over the web on distributed data sources provided by SDI.

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