Construction Site Material Stacking Identification Image Dataset
#image classification #object detection #scene analysis #construction management #site safety #resource optimization #site planning
- 500 records
- 1.4G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
Construction sites face challenges such as disorganized material stacking, large space occupation, impacting safety, and work efficiency. Existing management methods mainly rely on manual patrols and simple monitoring equipment video playback, which are inefficient and insufficiently accurate. This dataset aims to solve the needs of material stacking identification and management through image recognition technology, improving management efficiency and safety at construction sites. Data is collected using on-site photographs with professional camera equipment, capturing images from multiple angles and time periods under various weather conditions. To ensure data quality, multiple rounds of annotation, consistency checks, and reviews by a team of construction industry experts (approximately 20 people) are conducted. Data preprocessing includes image denoising, normalization, data augmentation, and uses hierarchical storage for quick retrieval and use, ultimately stored in JPG format.
Dataset Insights
Sample Examples
c89b977a**.jpg|1080*1382|414.57 KB
6bf7fb76**.jpg|1080*1400|373.70 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| material_type | string | Identifies the type of materials present in the image, such as bricks, rebar, etc. |
| stacking_method | string | Describes how the materials are stacked, such as stacking, spreading, etc. |
| material_condition | string | Assesses the newness and integrity of the materials. |
| stack_height | float | Measures the height of the stacked materials in meters. |
| safety_hazard | boolean | Identifies any safety hazards present in the image. |
| worker_presence | boolean | Determines whether workers are present in the image. |
| weather_condition | string | Identifies the weather conditions at the time the image was taken, such as sunny, rainy, etc. |
| site_clutter | string | Assesses the tidiness and clutter level of the construction site. |
| material_quantity | int | Estimates the quantity of materials present in the image. |
| equipment_present | boolean | Determines whether construction equipment is present in 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 primary use case for this dataset?
- The Construction Site Material Stacking Recognition Image Dataset is used to identify material stacking conditions at construction sites, helping to improve site management and safety.
- Which industry sector does this dataset belong to?
- This dataset belongs to the construction and real estate industry.
- What type of data modality is included in the dataset?
- The dataset includes image-type data modality.
- What problem can this dataset help solve?
- This dataset can help solve the problem of identifying material stacking at construction sites, thereby improving management efficiency and safety.
- How does identifying material stacking at construction sites affect safety?
- Identifying material stacking at construction sites can help prevent safety accidents caused by disorderly stacking of materials, enhancing overall site safety.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Construction Site Material Stacking Identification Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/2bf0ba71345bc537e41115c36339a728?dataset_scene_cate_type=7},
urldate={2026-02-04},
keywords={construction site identification, material stacking management, building image recognition, site safety monitoring},
version={1.0}
}Using this in research? Please cite us.
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