Datasets:
file_name stringclasses 3 values | quality stringclasses 1 value | construction_sign_count stringclasses 2 values | worker_count stringclasses 2 values | vehicle_count stringclasses 2 values | pedestrian_count stringclasses 2 values | machinery_count stringclasses 3 values | lighting_condition stringclasses 2 values | weather_condition stringclasses 3 values | road_type stringclasses 3 values | construction_type stringclasses 2 values | safety_equipment_present stringclasses 3 values |
|---|---|---|---|---|---|---|---|---|---|---|---|
5b2b559cde957e0c90317ac724604602.jpg | 1920*2560 | 5 | 1 | 3 | 1 | 0 | Daytime | Clear | Secondary road | Pavement repair | Yes |
efcdcf0af88917c425862a7b2881ef7f.jpg | 1920*2560 | 5 | 3 | 1 | 1 | 1 | Daytime | Sunny | Main road | Pavement repair | Present |
f229bcbf51f61a4bcd463f827a1dcb2d.jpg | 1920*2560 | 3 | 3 | 1 | 0 | 2 | daytime | clear | secondary road | pipeline installation | yes |
Urban Street Construction Scene Image Dataset
The current transportation industry faces challenges such as difficult management of urban construction sites and worsening traffic congestion. Some existing solutions often lack object detection capabilities for specific scenarios, leading to frequent accidents and resource wastage. This dataset aims to provide high-quality construction scene images to help researchers and engineers develop more precise object detection models. Images of urban streets under construction are collected through cameras to ensure diversity under different weather and lighting conditions. For quality control, a multi-round annotation and expert review mechanism is applied to ensure consistency and accuracy of annotations. Data is stored in JPG format, classified in folders for quick access and analysis.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| construction_sign_count | int | The number of construction signs in the image. |
| worker_count | int | The number of construction workers in the image. |
| vehicle_count | int | The number of vehicles in the image. |
| pedestrian_count | int | The number of pedestrians in the image. |
| machinery_count | int | The number of construction machines in the image. |
| lighting_condition | string | The lighting condition when the image was taken, such as daytime, night, or cloudy. |
| weather_condition | string | The weather condition when the image was taken, such as sunny, rainy, or snowy. |
| road_type | string | The type of road shown in the image, such as a main road or a secondary road. |
| construction_type | string | The type of construction being depicted in the image, such as road repair or pipeline installation. |
| safety_equipment_present | bool | Whether safety equipment such as cones or barriers are present in the image or not. |
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 |
Source & Contact
If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com
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