Tunnel Construction Face Geological Recognition Dataset
#image classification #geological image recognition #computer vision #tunnel construction #geological recognition #underground engineering
- 500 records
- 1.3G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
In tunnel construction, accurately recognizing the geological characteristics of the tunnel face is an important challenge to ensure construction safety and efficiency. Currently, traditional geological exploration techniques are costly and time-consuming, and they fall short in terms of recognition accuracy and operational continuity. This dataset aims to address the need for automated, real-time recognition results in existing tunnel geological recognition processes. The dataset collection process involves using drones equipped with professional photography equipment to capture at tunnel construction sites, combined with fixed cameras to collect images of geological changes at different times. The data undergoes multiple rounds of annotation and expert consistency checks to ensure annotation accuracy, managed by a team with a background in geology and engineering. Preprocessing includes denoising, enhancement, and image normalization. Data is stored in high-resolution JPG format and organized according to geological characteristic labels.
Dataset Insights
Sample Examples
97c44026**.jpg|640*426|43.17 KB
d943f2a5**.jpg|400*299|17.59 KB
5b74c3b9**.jpg|474*355|55.84 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| rock_type | string | The type of rock shown in the tunnel face area depicted within the image, such as granite, sandstone, etc. |
| weathering_degree | string | The degree of weathering of the rock in the image, categorized into levels such as fresh, slightly weathered, moderately weathered, etc. |
| fracture_density | string | The density of fractures within the rock at the tunnel face area shown in the image. |
| color_variation | string | The color variation of rocks at the tunnel face shown in the image. |
| mineral_visibility | string | The visibility of minerals on the rock surface in the image, such as visible grains, spots, etc. |
| moisture_level | string | The moisture level of the rock at the tunnel face shown in the image, such as dry, damp, etc. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What is the Tunnel Construction Face Geology Identification Dataset?
- The Tunnel Construction Face Geology Identification Dataset is a high-quality image dataset specifically used for face geology identification in tunnel construction environments.
- What are the application scenarios of the Tunnel Construction Face Geology Identification Dataset?
- This dataset can be applied in the scientific research field to help engineers and researchers enhance geological identification capabilities in tunnel construction, thereby optimizing construction plans.
- What modality of data does the Tunnel Construction Face Geology Identification Dataset include?
- This dataset includes image modality data, focusing on high-quality images related to geological identification.
- Who would be interested in the Tunnel Construction Face Geology Identification Dataset?
- Scientists, engineers, and researchers engaged in tunnel construction and geological research would be interested in this dataset.
- How can the Tunnel Construction Face Geology Identification Dataset be utilized in research?
- In research, this dataset can be used to train and validate geology identification models, enhancing support for tunnel construction under different geological conditions.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Tunnel Construction Face Geological Recognition Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/6d541b9e2fc80583243de2b3830d0c51},
urldate={2026-02-04},
keywords={tunnel construction dataset, geological recognition images, face geological analysis},
version={1.0}
}Using this in research? Please cite us.
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