1 |
Kim, H.M., D.G. Lee, and S. Sung, 2016. Effect of urban green spaces and flooded area type on flooding probability, Sustainability, 8(2): 134. https://doi.org/10.3390/su8020134
DOI
|
2 |
Choi, J.W., 2018. A Study on Model Development for the Density Management of Overcrowded Planting Sites and the Planting Design of New Planting Sites-A Case Study of Buffer Green Spaces in the Dongtan New Town, Hwaseong, Journal of the Korean Institute of Landscape Architecture, 46(5): 82-92 (in Korean with English abstract). https://doi.org/10.9715/KILA.2018.46.5.082
DOI
|
3 |
Kang, J.E. and M.J. Lee, 2012. Assessment of flood vulnerability to climate change using fuzzy model and GIS in Seoul, Journal of the Korean Association of Geographic Information Studies, 15(3): 119-136 (in Korean with English abstract). https://doi.org/10.11108/kagis.2012.15.3.119
DOI
|
4 |
Kaur, P., B.S. Khehra, and E.B.S. Mavi, 2021. Data augmentation for object detection: A review, Proc. of 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), East Lansing, MI, USA, Aug. 9-11, pp. 537-543. https://doi.org/10.1109/MWSCAS47672.2021.9531849
DOI
|
5 |
Hwang, S.R., M.J. Lee, and I.P. Lee, 2012. Detection of Individual Trees and Estimation of Mean Tree Height using Airborne LIDAR Data, Spatial Information Research, 20(3): 27-38 (in Korean with English abstract). https://doi.org/10.12672/ksis.2012.20.3.027
DOI
|
6 |
Park, M., 2021. Comparison of Accuracy between Analysis Tree Detection in UAV Aerial Image Analysis and Quadrat Method for Estimating the Number of Treesto be Removed in the Environmental Impact Assessment, Journal of Environmental Impact Assessment, 30(3): 155-163 (in Korean with English abstract). https://doi.org/10.14249/eia.2021.30.3.155
DOI
|
7 |
Puliti, S. and R. Astrup, 2022. Automatic detection of snow breakage at single tree level using YOLOv5 applied to UAV imagery, International Journal of Applied Earth Observation and Geoinformation, 112: 102946. https://doi.org/10.1016/j.jag.2022.102946
DOI
|
8 |
Wang, J. and L. Perez, 2017. The effectiveness of data augmentation in image classification using deep learning, arXiv preprint arXiv:1712.04621. https://doi.org/10.48550/arXiv.1712.04621
DOI
|
9 |
Wang, T.S., S. Oh, H.S. Lee, J. Jang, and M. Kim, 2021. A Study on the AI Detection Model of Marine Deposition Waste Using YOLOv5, Proc. of Korean Institute of Information and Communication Sciences Conference, Gunsan, Korea, Oct. 28-30, vol. 25, pp. 385-387.
|
10 |
Weinstein, B.G., S. Marconi, S. Bohlman, A. Zare, and E. White, 2019. Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks, Remote Sensing, 11(11): 1309. https://doi.org/10.3390/rs11111309
DOI
|
11 |
Xu, R., H. Lin, K. Lu, L. Cao, and Y. Liu, 2021. A forest fire detection system based on ensemble learning, Forests, 12(2): 217. https://doi.org/10.3390/f12020217
DOI
|
12 |
Zamboni, P., J.M. Junior, J.D.A. Silva, G.T. Miyoshi, E.T. Matsubara, K. Nogueira, and W.N. Goncalves, 2021. Benchmarking anchor-based and anchor-free state-of-the-art deep learning methods for individual tree detection in RGB high-resolution images, Remote Sensing, 13(13): 2482. https://doi.org/10.3390/rs13132482
DOI
|
13 |
Sim, W. K. and D. I. Lee, 2001. An analysis of Status quo on the multi-layer planting at the landscape planting area in apartments and neighborhood parks in Seoul metropolitan area, Journal of the Korean Institute of Landscape Architecture, 29(1): 140-151.
|