Transmission Line Icing Thickness Prediction Dataset
#Image Classification #Object Detection #Thickness Prediction #Power Transmission Monitoring #Energy Resource Management #Power Equipment Maintenance
- 500 records
- 1.6G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
In the energy sector, transmission line icing is a crucial factor affecting power supply. The current challenge is the lack of detailed monitoring data, making it difficult to take timely measures to prevent line damage. Existing solutions mostly rely on weather data for predictions, but their accuracy is insufficient. This dataset aims to more accurately predict icing thickness through image data, meeting the timely decision-making needs of the power sector. Data collection is carried out by high-resolution cameras under extreme weather conditions, ensuring coverage of different icing levels. For quality control, multiple rounds of annotation and consistency checks are conducted and reviewed by experts in the power field. The annotation team includes 20 experienced technicians. Data preprocessing includes image enhancement, denoising, and resolution adjustment, stored in JPG format and organized by time and geographic location.
Dataset Insights
Sample Examples
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b6e08c16**.jpg|1080*1210|221.87 KB
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| ice_thickness_category | string | Identify the ice thickness category of the transmission line based on the image. |
| weather_condition | string | Identify the weather condition on the day (sunny, rainy, snowy, etc.) based on the image. |
| line_visibility | boolean | Determine whether the transmission line is clearly visible in the image. |
| day_or_night | string | Identify whether it is daytime or nighttime in the image. |
| snow_cover | boolean | Determine whether there is snow cover on the transmission line based on the image. |
| vegetation_presence | boolean | Identify whether there is vegetation surrounding the transmission line in the image. |
| maintenance_status | string | Determine the maintenance status of the transmission line (normal, needs maintenance, critical, etc.) based on the image. |
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 power line icing thickness prediction dataset?
- The power line icing thickness prediction dataset is an image dataset used to predict the icing thickness on power lines, aiming to enhance monitoring and maintenance efficiency in the energy sector.
- Which industry sectors is this dataset suitable for?
- This dataset is suitable for the energy resources sector, particularly in the monitoring and maintenance of power lines.
- How can this dataset improve the efficiency of the energy sector?
- By analyzing and predicting the icing thickness on power lines, the energy sector can conduct monitoring and early maintenance more effectively, reducing accident risks and improving operational efficiency.
- What type of modality does this dataset include?
- This dataset includes image modality for analyzing the ice cover situation on power lines.
- How can the accuracy of predictions be ensured?
- Using high-quality data and advanced machine learning algorithms can enhance the accuracy of icing thickness predictions, thereby improving the reliability of power systems.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Transmission Line Icing Thickness Prediction Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/65d3980f3618416d50c65c1a267f288e?dataset_scene_cate_type=7},
urldate={2026-02-04},
keywords={Transmission Line Icing, Thickness Prediction Dataset, Power Monitoring Data},
version={1.0}
}Using this in research? Please cite us.
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