The Attain Dataset is available exclusively for academic purposes. While every effort has been made to ensure the accuracy of the dataset, no responsibility is assumed for any errors or omissions. Academic usage of this dataset is free of charge, but any form of commercial use or distribution for commercial purposes is strictly forbidden. Sharing this dataset with any individual or entity that has not reviewed and agreed to these terms is also prohibited.
Neither Attain nor any third-party contributors involved in the dissemination of this dataset are liable for the content, accuracy, or any errors or omissions in the provided information. Attain reserves the right to modify, update, or remove any part of this information at any time without notice, and accepts no liability for any such changes.
Users who publish work utilizing the datasets below are required to cite the following paper:
Sholevar, N., Golroo, A., & Esfahani, S. R. (2022). Machine learning techniques for pavement condition evaluation. Automation in Construction, 136, 104190.
Attain_RSP_WS_v2.0Sholevar, N., Golroo, A., & Esfahani, S. R. (2022). Machine learning techniques for pavement condition evaluation. Automation in Construction, 136, 104190.
Attain_RSP_OS_v1.0Valipour, P. S., Golroo, A., Kheirati, A., Fahmani, M., & Amani, M. J. (2024). Automatic pavement distress severity detection using deep learning. Road Materials and Pavement Design, 25(8), 1830-1846.
Attain_RSP_WS_v1.0Fahmani, M., Golroo, A., & Sedighian-Fard, M. (2024). Deep learning-based predictive models for pavement patching and manholes evaluation. International Journal of Pavement Engineering, 25(1), 2349901.
Attain_SMP_WS_v2.0-
Attain_SMP_OS_v1.0-
Attain_SMP_WS_v1.0-
