Review of smartphone applications for geoscience: current status, limitations, and future perspectives

Lee, Sang-Ho; Suh, Jangwon; Choi, Yosoon

doi:10.1007/s12145-018-0343-9

Cited by 26 publications

(12 citation statements)

References 26 publications

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“…From the orderly and methodical recording of data it is possible to construct stereograms to verify the accuracy of the measurements made, mainly when developing data surveys of lineations related to planes, as is the case with structural data of slickenlines in faults or brittle shear zones, although there is software that allows minimizing the error by approximation to the nearest degree in office data processing, as is the case of Tectonics. The use of mobile devices with different operating systems (iOS or Android) for structural data acquisition facilitates the recording and visualization of orientation measurements of geological structures (Allmendinger et al, 2017;Novakova and Pavlis, 2017;Lee et al, 2018;Whitmeyer et al, 2019), and additionally allows many measurements to be taken quickly (Allmendinger, 2017;. In some cases, these devices offer additional advantages in terms of accuracy in gauging the structural data and identifying the locations of field stations where the structural data measurements are taken using the positioning systems included in mobile devices which can be visualized on digital mapping platforms (e.g., Google Earth).…”

Section: Discussionmentioning

confidence: 99%

“…It is also possible to obtain the location of the points or stations where the structural data are taken on digital map platforms, by means of positioning systems included in mobile devices, and their visualization is possible in viewers, such as Google Earth, or apps compatible with geographic information systems, such as Mobile Data Collection, CartoDruid, MAPit, Nextgis and Qfield for Qgis, and ArcGIS Explorer and Collector for ArcGIS. Describing each of the aforementioned applications is beyond the scope of this article, but the interested reader is referred to Allmendinger et al (2017), Novakova and Pavlis (2017), Lee et al (2018), and Whitmeyer et al (2019), among others, as well as the respective descriptions provided by the developers.…”

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confidence: 99%

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Graphical representation of structural data in the field: A methodological proposal for application in deformed areas

Isaza¹,

Cárdenas²,

Cetina³

et al. 2021

Bol.geol.

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The description of the fabric elements represented by the linear and planar structures present at different scales is a key component of fieldwork. A scheme is proposed for the systematic registration of planes and lineations, coded as S (planar surfaces), F (folds), and L (lineations), among others, that allows for the orderly storage of the measurements taken. This scheme includes information related to the kinematics, the kinematic indicators, and the certainty or reliability ascribed to the assigned movement. In the fieldwork, the graphic representation of the structural measures in modified projection nets includes concentric circles for each dip. Direct drawing of the outcrop data is undertaken, dispensing with the use of tracing or transparent paper. The stereograms resulting from the graphic representation in the modified Wulff stereographic projection net, and the modified Schmidt equal-area net, can be complemented by rose diagrams for visualization of the spatial ordering. During field campaigns in the outcrops, it is essential to visualize the spatial orientation of the data in the diagrams to determine the main structural trends, the vergence, the kinematic nature of faults and shear zones, paleo-stress tensors, and to differentiate structural domains, among others. This information supports the reconstruction of geological and tectonic history and the establishment of relationships between the different geological processes.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Graphical representation of structural data in the field: A methodological proposal for application in deformed areas

Isaza¹,

Cárdenas²,

Cetina³

et al. 2021

Bol.geol.

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“…There are some traits of geoscience development that restrict the applicability of fundamental algorithms for knowledge discovery: (1) inherent challenges of geoscience processes, (2) limitation of geoscience data collection, and (3) uncertainty in samples and ground truth. 88 , 89 , 90 Amorphous boundaries generally exist in geoscience objects between space and time that are not as well defined as objects in other fields. Geoscience phenomena are also significantly multivariate, obey nonlinear relationships, and exhibit spatiotemporal structure and non-stationary characteristics.…”

Section: Ai In Geosciencementioning

confidence: 99%

Artificial intelligence: A powerful paradigm for scientific research

et al. 2021

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Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

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“…Crucial for these purposes is the availability of recent mobile devices (tablets and smartphones) with adequate hardware and software capabilities to support the operation of GIS-based applications for field data collection. The recent models include many sensors (e.g., cameras, magnetometer, accelerometer, gyroscope, clinometer, and GPS) and therefore can be used as an efficient tool for geological surveys in the field offering substantial enhancements for collecting more accurate data [13,14].…”

Section: Introductionmentioning

confidence: 99%

Geospatial Information Technologies for Mobile Collaborative Geological Mapping: The Italian CARG Project Case Study

Gencarelli

Voltolina

Hammouti

et al. 2022

IJGI

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A collaborative open-source IT infrastructure is designed and implemented to optimize the process of geological field data collection, integration, validation, and sharing. Firstly, field data collection is carried out by multiple users using free and open-source GIS-based tools for mobile devices according to a predefined database structure; then, data integration is automatically performed in a central server, where the collected geological information is stored and validated; finally, data are shared over the Internet, providing users with up-to-date information. The IT infrastructure is currently being employed to accomplish surveys for the realization of the “Brescia” geological map within the New Geological Map of Italy, scale 1:50.000 (CARG Project). Users are only required to run the field data collection application on their mobile devices, add different geometric features to predefined thematic layers and fill in the dialogue forms with the required information to store the new structured and georeferenced data in the central database. The major advantage of the proposed IT infrastructure consists of guaranteeing the operational continuity between field surveys and the finalization of geological or geothematic maps leveraging field data collection tools that are operational both online and offline to ensure the overall system resilience.

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Review of smartphone applications for geoscience: current status, limitations, and future perspectives

Cited by 26 publications

References 26 publications

Graphical representation of structural data in the field: A methodological proposal for application in deformed areas

Graphical representation of structural data in the field: A methodological proposal for application in deformed areas

Artificial intelligence: A powerful paradigm for scientific research

Geospatial Information Technologies for Mobile Collaborative Geological Mapping: The Italian CARG Project Case Study

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