Dataset for Road Roughness Assessment Using Image Classification Techniques and Deep Learning Models: A Case Study on Bangladeshi National Highways
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8937-9_19
Reference10 articles.
1. 2100km-roads-need-repair. https://www.thedailystar.net/news/bangladesh/transport/news/2100km-roads-need-repair-3048706?amp&fbclid=IwAR0quBhI4W7x_4rcs3uqjixYwktmieZbmJsSNuTl8IbcvmhwWgKa2YwtaBU
2. Kalpoma KA, Shome D, Sikder A, Jahin A (2022) A comprehensive study on road quality measurement from high resolution satellite imagery. In: IGARSS 2022: 2022 IEEE international geoscience and remote sensing symposium, Kuala Lumpur, Malaysia, pp 2594–2597. https://doi.org/10.1109/IGARSS46834.2022.9884463
3. Xu Y, Xie Z, Feng Y, Chen Z (2018) Road extraction from high-resolution remote sensingimagery using deep learning. Remote Sens 10(9):1461. https://doi.org/10.3390/rs10091461
4. Zao Y, Chen H, Liu L, Shi Z (2022) Enhance essential features for road extraction from remote sensing images. In: IGARSS 2022: 2022 IEEE international geoscience and remote sensing symposium, Kuala Lumpur, Malaysia, pp 3023–3026. https://doi.org/10.1109/IGARSS46834.2022.9883214
5. Lu X, Zhong Y, Zheng Z, Chen D (2022) GRE and beyond: a global road extraction dataset. In: IGARSS 2022: 2022 IEEE international geoscience and remote sensing symposium, Kuala Lumpur, Malaysia, pp 3035–3038. https://doi.org/10.1109/IGARSS46834.2022.9883915
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