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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.25211 |
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| _version_ | 1866912676007903232 |
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| author | Khandakar, Amith Michelson, David Rabbani, Shaikh Golam Shafi, Fariya Bintay Ahamed, Md. Faysal Rahman, Khondokar Radwanur Rahman, Md Abidur Nabi, Md. Fahmidun Ayari, Mohamed Arselene Khan, Khaled Suganthan, Ponnuthurai Nagaratnam |
| author_facet | Khandakar, Amith Michelson, David Rabbani, Shaikh Golam Shafi, Fariya Bintay Ahamed, Md. Faysal Rahman, Khondokar Radwanur Rahman, Md Abidur Nabi, Md. Fahmidun Ayari, Mohamed Arselene Khan, Khaled Suganthan, Ponnuthurai Nagaratnam |
| contents | It's important to monitor road issues such as bumps and potholes to enhance safety and improve road conditions. Smartphones are equipped with various built-in sensors that offer a cost-effective and straightforward way to assess road quality. However, progress in this area has been slow due to the lack of high-quality, standardized datasets. This paper discusses a new dataset created by a mobile app that collects sensor data from devices like GPS, accelerometers, gyroscopes, magnetometers, gravity sensors, and orientation sensors. This dataset is one of the few that integrates Geographic Information System (GIS) data with weather information and video footage of road conditions, providing a comprehensive understanding of road issues with geographic context. The dataset allows for a clearer analysis of road conditions by compiling essential data, including vehicle speed, acceleration, rotation rates, and magnetic field intensity, along with the visual and spatial context provided by GIS, weather, and video data. Its goal is to provide funding for initiatives that enhance traffic management, infrastructure development, road safety, and urban planning. Additionally, the dataset will be publicly accessible to promote further research and innovation in smart transportation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25211 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RoadSens-4M: A Multimodal Smartphone & Camera Dataset for Holistic Road-way Analysis Khandakar, Amith Michelson, David Rabbani, Shaikh Golam Shafi, Fariya Bintay Ahamed, Md. Faysal Rahman, Khondokar Radwanur Rahman, Md Abidur Nabi, Md. Fahmidun Ayari, Mohamed Arselene Khan, Khaled Suganthan, Ponnuthurai Nagaratnam Robotics It's important to monitor road issues such as bumps and potholes to enhance safety and improve road conditions. Smartphones are equipped with various built-in sensors that offer a cost-effective and straightforward way to assess road quality. However, progress in this area has been slow due to the lack of high-quality, standardized datasets. This paper discusses a new dataset created by a mobile app that collects sensor data from devices like GPS, accelerometers, gyroscopes, magnetometers, gravity sensors, and orientation sensors. This dataset is one of the few that integrates Geographic Information System (GIS) data with weather information and video footage of road conditions, providing a comprehensive understanding of road issues with geographic context. The dataset allows for a clearer analysis of road conditions by compiling essential data, including vehicle speed, acceleration, rotation rates, and magnetic field intensity, along with the visual and spatial context provided by GIS, weather, and video data. Its goal is to provide funding for initiatives that enhance traffic management, infrastructure development, road safety, and urban planning. Additionally, the dataset will be publicly accessible to promote further research and innovation in smart transportation systems. |
| title | RoadSens-4M: A Multimodal Smartphone & Camera Dataset for Holistic Road-way Analysis |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.25211 |