Scan-Line Methods in Spatial Data Systems

1990 ◽  
Author(s):  
Michael McDonnell
Keyword(s):  
Spatial Data ◽  
Data Systems ◽  
Scan Line

scholarly journals Geographic Information and Geo-visualisation in support of Disaster Resilience

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Edward Kurwakumire ◽  
Paul Muchechetere ◽  
Shelter Kuzhazha ◽  
Guy Blachard Ikokou
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Disaster Preparedness ◽  
Geographic Information ◽  
Public Access ◽  
Data Sets ◽  
Disaster Resilience ◽  
Data Systems ◽  
Information Infrastructures ◽  
Timely Access

<p><strong>Abstract.</strong> Society continues to become more spatially enabled as spatial data becomes increasingly available and accessible. This is partly due to democratisation of data achieved through open access of framework data sets. On the other hand, mobile devices such as smartphones have become more accessible, giving the public access to applications that use spatial data. This has tremendously increased the consumption of spatial data at the level of the general public. Spatial data has a history in planning and decision making as detailed in literature on promises and benefits of geographic information. We extend these promises to sustainability and disaster resilience. It is our belief that geographic information (GI) and geographic information infrastructures (GIIs) contribute positively towards the achievement of sustainability in cities and nations and in disaster resilience. This study carries out a review of geo-visualisation and GI applications in order to determine their suitability and impact in disaster resilience. Real-time GI are significant for cities to ensure sustainability and to increase disaster preparedness. Geographic information infrastructures need to be integrated with BIG data systems to ensure that local government agencies have timely access to real time geographic information so that decisions on sustainability and disaster resilience can be effectively done.</p>

Distributed database system of the New Atlas of Amphibians and Reptiles in Europe: the NA2RE project

2014 ◽  
Vol 35 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Neftalí Sillero ◽  
Marco Amaro Oliveira ◽  
Pedro Sousa ◽  
Fátima Sousa ◽  
Luís Gonçalves-Seco
Keyword(s):  
Spatial Data ◽  
Distributed Databases ◽  
Species Conservation ◽  
Distributed Database ◽  
Database System ◽  
Data Systems ◽  
Web Interface ◽  
Data Infrastructure ◽  
Societas Europaea ◽  
Distributed Database System

The Societas Europaea Herpetologica (SEH) decided in 2006 through its Mapping Committee to implement the New Atlas of Amphibians and Reptiles of Europe (NA2RE: http://na2re.ismai.pt) as a chorological database system. Initially designed to be a system of distributed databases, NA2RE quickly evolved to a Spatial Data Infrastructure, a system of geographically distributed systems. Each individual system has a national focus and is implemented in an online network, accessible through standard interfaces, thus allowing for interoperable communication and sharing of spatial-temporal data amongst one another. A Web interface facilitates the access of the user to all participating data systems as if it were one single virtual integrated data-source. Upon user request, the Web interface searches all distributed data-sources for the requested data, integrating the answers in an always updated and interactive map. This infrastructure implements methods for fast actualisation of national observation records, as well as for the use of a common taxonomy and systematics. Using this approach, data duplication is avoided, national systems are maintained in their own countries, and national organisations are responsible for their own data curation and management. The database could be built with different representation levels and resolution levels of data, and filtered according to species conservation matters. We present the first prototype of NA2RE, composed of the last data compilation performed by the SEH (Sillero et al., 2014). This system is implemented using only open source software: PostgreSQL database with PostGIS extension, Geoserver, and OpenLayers.

scholarly journals Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California

2021 ◽  
Author(s):  
Kostas Alexandridis
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Spatial Data ◽  
Field Data ◽  
Orange County ◽  
Data Systems ◽  
Neural Network Learning ◽  
Stop Sign ◽  
Efficiency And Effectiveness

We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360\textdegree~equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems.

scholarly journals Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California

2021 ◽  
Author(s):  
Kostas Alexandridis
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Spatial Data ◽  
Field Data ◽  
Orange County ◽  
Data Systems ◽  
Neural Network Learning ◽  
Stop Sign ◽  
Efficiency And Effectiveness

We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360\textdegree~equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems.

Tools for the Storage and Analysis of Spatial Big Data

2017 ◽  
Author(s):  
Przemysław Lisowski ◽  
Adam Piórkowski ◽  
Andrzej Lesniak
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
File Systems ◽  
Large Datasets ◽  
Distributed File Systems ◽  
Data Systems ◽  
Data Production ◽  
Spatial Big Data ◽  
Big Data Systems ◽  
Access To Data

Storing large amounts of spatial data in GIS systems is problematic. This problem is growing due to ever- increasing data production from a variety of data sources. The phenomenon of collecting huge amounts of data is called Big Data. Existing solutions are capable of processing and storing large volumes of spatial data. These solutions also show new approaches to data processing. Conventional techniques work with ordinary data but are not suitable for large datasets. Their efficient action is possible only when connected to distributed file systems and algorithms able to reduce tasks. This review focuses on the characteristics of large spatial data and discusses opportunities offered by spatial big data systems. The work also draws attention to the problems of indexing and access to data, and proposed solutions in this area.

scholarly journals Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California

2021 ◽  
Author(s):  
Kostas Alexandridis
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Recognition ◽  
Spatial Data ◽  
Field Data ◽  
Orange County ◽  
Data Systems ◽  
Neural Network Learning ◽  
Stop Sign ◽  
Efficiency And Effectiveness

We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360 degree equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems.

Spatial Data Infrastructures in Morocco (From the Diagnosis to the Success of the Implementation)

2019 ◽  
pp. 64-81
Author(s):  
Elmostaphi Elomari ◽  
Hassan Rhinane
Keyword(s):  
Spatial Data ◽  
Geographic Information ◽  
Spatial Data Infrastructure ◽  
Geographical Information ◽  
Collection Management ◽  
Data Systems ◽  
Data Infrastructure ◽  
Information Governance ◽  
Data Infrastructures ◽  
Current Context

A spatial data infrastructure (SDI) is a platform for coordinating the exchange and sharing of spatial data between several producers or users of spatially referenced data. In Morocco, there is a massive production of spatial data and several generally public administrations are starting to feel the need for geographic information governance through a mechanism of exchange and management of data to optimize their efforts and avoid a redundant production. The purpose of this chapter is to draw up an inventory of the state of the art of geo-spatial data, systems, and tools existing in the central administrations in Morocco in relation with the collection, management, storage, and dissemination of geographical information. Through this study, it was found that the problem is more a question of global governance, and that the current context has assets for the establishment of a spatial data infrastructure in Morocco.

scholarly journals Spatial Data Systems for Transportation Planning

1997 ◽  
Author(s):  
Marc P. Armstrong ◽  
Gerard Rushton ◽  
Jayajit Chakraborty ◽  
Allen Wayne Ibaugh ◽  
Amy J Ruggles
Keyword(s):  
Spatial Data ◽  
Transportation Planning ◽  
Data Systems
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