Mobiusi

Construction Site Material Stacking Encroachment Detection Image Dataset

#object detection #image classification #obstacle detection #construction site management #road safety monitoring #smart city planning
  • 500 records
  • 1.2G
  • JPG
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-02-04

AI Analysis & Value Prop

In construction site safety management, road occupation caused by improper material stacking is a widespread issue affecting traffic flow and safety management. Current manual inspections and management are inefficient, unable to monitor in real-time and address issues promptly. Therefore, there is an urgent need for an efficient automated monitoring solution. This dataset aims to identify and detect the material stacking conditions at construction sites, aiding in intelligent site management. The data is collected through drones and fixed cameras, covering different weather conditions and time periods to ensure diversity and comprehensiveness. Quality control includes multiple rounds of annotation and expert review to ensure accuracy. The annotation team consists of 10 professionals with experience in image processing. Data preprocessing involves techniques such as image denoising, contrast enhancement, and semantic segmentation. Data is stored in JPEG format, accompanied by annotation information in JSON format.

Dataset Insights

Sample Examples

78ffa009**.png|720*1280|1.94 MB

Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
material_typestringThe type of material shown in the image, such as gravel, wood, etc.
obstruction_levelstringThe degree to which the material is obstructing the road, such as partial obstruction or complete blockage.
equipment_presencebooleanIndicates whether any equipment is present in the image, such as an excavator.
safety_signs_visibilitybooleanIndicates whether safety signs, such as warning boards, are visible in the image.
pedestrian_visibilitybooleanIndicates whether pedestrians are visible walking through the image.
weather_conditionstringThe weather condition at the time the image was taken, such as sunny, cloudy, or rainy.
time_of_daystringThe time of day when the image was taken, such as morning, afternoon, or evening.
vehicle_presencebooleanIndicates whether vehicles are present in the image.

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What is the construction site material stack encroachment detection image dataset?
The construction site material stack encroachment detection image dataset is specifically designed to identify and detect instances where material stacks on construction sites encroach on roadways.
What are the application scenarios suitable for the construction site material stack encroachment detection image dataset?
This dataset can be used for smart construction management, urban roadway planning, and construction site safety supervision.
How can this dataset be used to detect material stack encroachments on roadways?
Machine learning and computer vision techniques can be applied to analyze images provided in the dataset, thus identifying encroachment instances.
What information is included in the construction site material stack encroachment detection image dataset?
The dataset primarily includes images of material stacks on construction sites, along with annotations describing whether the stacks encroach on roadways.
Why is detecting material stack encroachment on roadways important?
Detecting such encroachments is important to enhance road safety, reduce traffic accidents, and ensure compliance on construction sites.

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Cite this Work

@dataset{Mobiusi2026,
  title={Construction Site Material Stacking Encroachment Detection Image Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/dae930780a34ec729695b67c6062ded4},
  urldate={2026-02-04},
  keywords={construction site encroachment detection, construction material management, road safety AI},
  version={1.0}
}

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