Browse > Article
http://dx.doi.org/10.3745/KTSDE.2022.11.8.347

Forest Change Detection Service Based on Artificial Intelligence Learning Data  

Chung, Hankun (숭실대학교 IT정책경영학과)
Kim, Jong-in (숭실대학교 IT정책경영학과)
Ko, Sun Young (숭실대학교 IT정책경영학과)
Chai, Seunggi ((주)올포랜드 전략사업)
Shin, Youngtae (숭실대학교 컴퓨터학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.8, 2022 , pp. 347-354 More about this Journal
Abstract
Since the era of the 4th industrial revolution has been ripe, the use of artificial intelligence(AI) based on massive data is beginning to be actively applied in various fields. However, as the process of analyzing forest species is carried out manually, many errors are occurring. Therefore, in this paper, about 60,000 pieces of AI learning data were automatically analyzed for pine, larch, conifer, and broadleaf trees of aerial photographs and pseudo images in the metropolitan area, and an AI model was developed to distinguish tree species. Through this, it is expected to increase in work efficiency by using the tree species division image as basic data when producing forest change detection and forest field topics.
Keywords
Artificial Intelligence; Learning Data; Forest Tree Species; Forest Change Detection; Aerial Photographs;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 AI Hub [Internet], http://www.aihub.or.kr
2 Quality Management Guildeline for Artificial Intelligence Learning Data v1.0, NIA, 2021.
3 국립산림과학원, "디지털 항공영상을 이용한 대축척 임상도 제작 및 갱신방법," 2012.
4 V. Andersson, Semantic Segmentation: Using Convolutional Neural Networks and Sparse Dictionaries. 2017.
5 L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, "Encoder-decoder with atrous separable convolution for semantic image segmentation," ECCV, 2018.
6 L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.40, No.4, pp.834-848, 2018.   DOI
7 L. G. Hafemann, L. S. Oliveira, and P. Cavalin, "Forest Species Recognition using Deep Convolutional Neural Networks," In 2014 22nd International Conference on Pattern Recognition, pp.1103-1107, 2014.
8 P. P. de Bem, O. A. de Carvalho Junior, R. Fontes Guimaraes, and R. A. Trancoso Gomes, "Change detection of deforestation in the Brazilian Amazon using landsat data and convolutional neural networks," Remote Sensing, Vol.12, No.6, pp.901, 2020.
9 J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3431-3440, 2015.
10 D. Lobo Torres, R. Queiroz Feitosa, P. Nigri Happ, L. Elena Cue La Rosa, J. Marcato Junior, J. Martins, P. Ola Bressan, W. N. Goncalves, and V. Liesenberg, "Applying fully convolutional architectures for semantic segmentation of a single tree species in urban environment on high resolution UAV optical imagery," Sensors, Vol.20, No.2, pp.563, 2020.
11 A. Zlateski, R. Jaroensri, P. Sharma, and F. Durand, "On the importance of label quality for semantic segmentation," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1479-1487, 2018.
12 J. P. Papa, A.X. FalcaO, V. H. C. De Albuquerque, and J. M. R. Tavares, "Efficient supervised optimum-path forest classification for large datasets," Pattern Recognit, Vol.45, No.1, pp.512-520, 2012.   DOI
13 Creation Guideline for Artificiall Intelligence Learning Data Set, NIA, 2021.