Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "earth science" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Graph representation of geological stratum
Autorzy:
Lisowski, P.
Powiązania:
https://bibliotekanauki.pl/articles/184746.pdf
Data publikacji:
2016
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
environmental protection
geophysics
earth science
Opis:
Geology, geophysics and environmental protection sciences provide large amounts of data. These data can be stored in various structures. Most of them are stored in files. It is possible to store these data in databases. One example of databases for earth sciences is a Geokarpat database (Kotlarczyk et al. 1997). This database was developed over many years (Piórkowski & Gajda 2009). Other geological databases are, for example, database MIDAS (MIDAS 2015) and central database of the geological data (CBDG 2015). Solutions listed above are based on the relational data model. This model is not perfect for data analysis, as there are a lot of complicated relationships between the entities (Dominguez-Sal et al. 2010). A typical use of SQL in this case requires the creation of multiple joins and a large amount of calculations. Graph data model is gaining popularity because it allows representation similar to the natural network model of relationships between data (Horzyk 2013). Applications of this model within the earth sciences are extensive, including solutions for GIS systems. One of example graph application is the creation of a virtual generator of the city using database Neo4j (Płuciennik & Płuciennik-Psota 2014). Graph structure reproduces biological structure of memory well (Horzyk 2013). Based on this advantage, there are new opportunities to store and analyze geological data. The use of graphs to record those data enables data analyses in similar manner like in associative neural networks (Horzyk 2013). Geological stratum often has a complex structure, for example: around area of tectonic faults (often multiple faults in history), intrusive rocks in stratum. Possibilities of using graph databases for storing geological data were checked. This study focuses on proposing a graph representation of geological stratum. The proposed graph structure was implemented in the graph database. Presentation of the history of geological stratum in relational databases is difficult. Studies show an example of stratum graph model, which enables data mining of stratum history in easy method, because graph database systems are designed to make search queries to find similarity in data. Additionally, the results of this study demonstrated useful query. Moreover, software and possible methods of construction of graph models were studied. As shown by the results, an analysis of complex models of geological stratum can be less complicated. Research shows that finding dependences in the graph representation of the geological layers can be beneficial in geological analyses.
Źródło:
Geology, Geophysics and Environment; 2016, 42, 1; 92-93
2299-8004
2353-0790
Pojawia się w:
Geology, Geophysics and Environment
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Python library for the Jupyteo IDE Earth observation processing tool enabling interoperability with the QGIS System for use in data science
Autorzy:
Bednarczyk, Michał
Powiązania:
https://bibliotekanauki.pl/articles/2055774.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
Earth observation data processing
IDE
IPython
Jupyter notebook
web processing service
GIS
data science
machine learning
API
Opis:
This paper describes JupyQgis – a new Python library for Jupyteo IDE enabling interoperability with the QGIS system. Jupyteo is an online integrated development environment for earth observation data processing and is available on a cloud platform. It is targeted at remote sensing experts, scientists and users who can develop the Jupyter notebook by reusing embedded open-source tools, WPS interfaces and existing notebooks. In recent years, there has been an increasing popularity of data science methods that have become the focus of many organizations. Many scientific disciplines are facing a significant transformation due to data-driven solutions. This is especially true of geodesy, environmental sciences, and Earth sciences, where large data sets, such as Earth observation satellite data (EO data) and GIS data are used. The previous experience in using Jupyteo, both among the users of this platform and its creators, indicates the need to supplement its functionality with GIS analytical tools. This study analyzed the most efficient way to combine the functionality of the QGIS system with the functionality of the Jupyteo platform in one tool. It was found that the most suitable solution is to create a custom library providing an API for collaboration between both environments. The resulting library makes the work much easier and simplifies the source code of the created Python scripts. The functionality of the developed solution was illustrated with a test use case.
Źródło:
Geomatics and Environmental Engineering; 2022, 16, 1; 117--144
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies