EG09: Artificial Intelligence Potential in Digitizing Old and Raw Geodata

Khalid ElHaj; Dalal Alshamsi; Abdel Azim Ebraheem; Ahmed Sefelnasr

doi:10.1190/iceg2023-009.1

EG09: Artificial Intelligence Potential in Digitizing Old and Raw Geodata

Khalid ElHaj, Dalal Alshamsi, Abdel Azim Ebraheem, Ahmed Sefelnasr

Department of Geoscience

Research output: Contribution to conference › Paper › peer-review

Abstract

This research article describes a project aimed at automating the digitization, manipulation, and storage of geologic records dating back to 1969. Microsoft Azure and Optical Character Recognition (OCR) were utilized to extract and digitize information from delicate and aging documents. The documents contain geologic and groundwater data collected from various wells in the UAE at that time. The data were primarily gathered for the development of lithologic logs and maps, as well as groundwater fluctuation time series information. Here, we present the outcomes of digitizing the lithology of the aquifer and subsequent mapping. By averting the manual conversion of data into digital format, the method provides significant time and accuracy savings.

Original language	English
Pages	42-45
Number of pages	4
DOIs	https://doi.org/10.1190/iceg2023-009.1
Publication status	Published - 2024
Event	7th International Conference on Engineering Geophysics, ICEG 2023 - Al Ain City, United Arab Emirates Duration: Oct 16 2023 → Oct 19 2023

Conference

Conference	7th International Conference on Engineering Geophysics, ICEG 2023
Country/Territory	United Arab Emirates
City	Al Ain City
Period	10/16/23 → 10/19/23

ASJC Scopus subject areas

Geophysics

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10.1190/iceg2023-009.1

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abstract = "This research article describes a project aimed at automating the digitization, manipulation, and storage of geologic records dating back to 1969. Microsoft Azure and Optical Character Recognition (OCR) were utilized to extract and digitize information from delicate and aging documents. The documents contain geologic and groundwater data collected from various wells in the UAE at that time. The data were primarily gathered for the development of lithologic logs and maps, as well as groundwater fluctuation time series information. Here, we present the outcomes of digitizing the lithology of the aquifer and subsequent mapping. By averting the manual conversion of data into digital format, the method provides significant time and accuracy savings.",

author = "Khalid ElHaj and Dalal Alshamsi and Ebraheem, {Abdel Azim} and Ahmed Sefelnasr",

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AU - ElHaj, Khalid

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AU - Ebraheem, Abdel Azim

AU - Sefelnasr, Ahmed

PY - 2024

Y1 - 2024

N2 - This research article describes a project aimed at automating the digitization, manipulation, and storage of geologic records dating back to 1969. Microsoft Azure and Optical Character Recognition (OCR) were utilized to extract and digitize information from delicate and aging documents. The documents contain geologic and groundwater data collected from various wells in the UAE at that time. The data were primarily gathered for the development of lithologic logs and maps, as well as groundwater fluctuation time series information. Here, we present the outcomes of digitizing the lithology of the aquifer and subsequent mapping. By averting the manual conversion of data into digital format, the method provides significant time and accuracy savings.

AB - This research article describes a project aimed at automating the digitization, manipulation, and storage of geologic records dating back to 1969. Microsoft Azure and Optical Character Recognition (OCR) were utilized to extract and digitize information from delicate and aging documents. The documents contain geologic and groundwater data collected from various wells in the UAE at that time. The data were primarily gathered for the development of lithologic logs and maps, as well as groundwater fluctuation time series information. Here, we present the outcomes of digitizing the lithology of the aquifer and subsequent mapping. By averting the manual conversion of data into digital format, the method provides significant time and accuracy savings.

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ER -

EG09: Artificial Intelligence Potential in Digitizing Old and Raw Geodata

Abstract

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ASJC Scopus subject areas

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