Intelligent recognition of subsurface utilities and voids: A ground penetrating radar dataset for deep learning applicationsMendeley Data
Ground Penetrating Radar (GPR), has emerged as a powerful non-invasive geophysical technique for detecting subsurface utilities, voids, and other subsurface anomalies. However, despite its widespread use in geophysical investigations, and construction management, there is lack of available datasets...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000708 |
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| author | Abdelaziz Mojahid Driss El Ouai Khalid El Amraoui Khalil El-Hami Hamou Aitbenamer |
| author_facet | Abdelaziz Mojahid Driss El Ouai Khalid El Amraoui Khalil El-Hami Hamou Aitbenamer |
| author_sort | Abdelaziz Mojahid |
| collection | DOAJ |
| description | Ground Penetrating Radar (GPR), has emerged as a powerful non-invasive geophysical technique for detecting subsurface utilities, voids, and other subsurface anomalies. However, despite its widespread use in geophysical investigations, and construction management, there is lack of available datasets containing B-scan images of the subsurface features publicly that could be used to train deep learning models for automated anomaly detection. This data article aims at contributing to fill up this gap by creating a dataset specifically designed for automatic detection of subsurface utilities, and voids using deep learning. The dataset consists of 2,239 Radargram images in JPEG format obtained from GPR surveys conducted in urban environments to identify utilities such as pipes, cables, and underground voids. The importance of this dataset lies in: (1) contribute to fill the gap of lack of GPR data, (2) the universality of the data, (3) its potential to enhance the accuracy and efficiency to detect subsurface anomaly through the application of deep learning models, (4) GPR surveys are highly effective but still expensive, and its processing is time-consuming. By providing this labelled dataset for deep learning model training, this can facilitate the development of automated systems, capable of detecting subsurface anomalies effectively, which could reduce manual errors. |
| format | Article |
| id | doaj-art-7c4c1b5e386a490689810e36f7dcc759 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-7c4c1b5e386a490689810e36f7dcc7592025-02-06T05:11:58ZengElsevierData in Brief2352-34092025-04-0159111338Intelligent recognition of subsurface utilities and voids: A ground penetrating radar dataset for deep learning applicationsMendeley DataAbdelaziz Mojahid0Driss El Ouai1Khalid El Amraoui2Khalil El-Hami3Hamou Aitbenamer4Corresponding author.; Institut Scientifique, Université Mohammed V de Rabat, MoroccoInstitut Scientifique, Université Mohammed V de Rabat, MoroccoLCS laboratory, Physics Dept. Faculty of Science, Mohammed V University in Rabat, 10000, MoroccoInstitut Scientifique, Université Mohammed V de Rabat, MoroccoMesure Expert, 10 rue, dayet Aoua, 10090, Agdal, Rabat, MoroccoGround Penetrating Radar (GPR), has emerged as a powerful non-invasive geophysical technique for detecting subsurface utilities, voids, and other subsurface anomalies. However, despite its widespread use in geophysical investigations, and construction management, there is lack of available datasets containing B-scan images of the subsurface features publicly that could be used to train deep learning models for automated anomaly detection. This data article aims at contributing to fill up this gap by creating a dataset specifically designed for automatic detection of subsurface utilities, and voids using deep learning. The dataset consists of 2,239 Radargram images in JPEG format obtained from GPR surveys conducted in urban environments to identify utilities such as pipes, cables, and underground voids. The importance of this dataset lies in: (1) contribute to fill the gap of lack of GPR data, (2) the universality of the data, (3) its potential to enhance the accuracy and efficiency to detect subsurface anomaly through the application of deep learning models, (4) GPR surveys are highly effective but still expensive, and its processing is time-consuming. By providing this labelled dataset for deep learning model training, this can facilitate the development of automated systems, capable of detecting subsurface anomalies effectively, which could reduce manual errors.http://www.sciencedirect.com/science/article/pii/S2352340925000708Ground penetrating radarVoidsSubsurface utilitiesDeep LearningDatasetAutomation |
| spellingShingle | Abdelaziz Mojahid Driss El Ouai Khalid El Amraoui Khalil El-Hami Hamou Aitbenamer Intelligent recognition of subsurface utilities and voids: A ground penetrating radar dataset for deep learning applicationsMendeley Data Data in Brief Ground penetrating radar Voids Subsurface utilities Deep Learning Dataset Automation |
| title | Intelligent recognition of subsurface utilities and voids: A ground penetrating radar dataset for deep learning applicationsMendeley Data |
| title_full | Intelligent recognition of subsurface utilities and voids: A ground penetrating radar dataset for deep learning applicationsMendeley Data |
| title_fullStr | Intelligent recognition of subsurface utilities and voids: A ground penetrating radar dataset for deep learning applicationsMendeley Data |
| title_full_unstemmed | Intelligent recognition of subsurface utilities and voids: A ground penetrating radar dataset for deep learning applicationsMendeley Data |
| title_short | Intelligent recognition of subsurface utilities and voids: A ground penetrating radar dataset for deep learning applicationsMendeley Data |
| title_sort | intelligent recognition of subsurface utilities and voids a ground penetrating radar dataset for deep learning applicationsmendeley data |
| topic | Ground penetrating radar Voids Subsurface utilities Deep Learning Dataset Automation |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000708 |
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