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Forest Change Detection Service Based on Artificial Intelligence Learning Data

인공지능 학습용 데이터 기반의 산림변화탐지 서비스

  • Received : 2021.12.31
  • Accepted : 2022.02.11
  • Published : 2022.08.31

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.

4차 산업혁명 시대가 무르익으면서 방대한 데이터를 기반으로 한 인공지능(AI, Artificial Intelligence)의 활용이 전 산업 분야로 확대 중이다. 그러나 산림 수종을 분석하는 분야는 지금까지 인공지능의 활용이 미진하여 여전히 수작업으로 분석하고 있고 다수의 오류가 발생하고 있다. 본 연구에서는 수도권의 항공사진과 모사 이미지 등을 이용하여 소나무, 낙엽송, 침엽수, 활엽수 등 산림 수종을 분석하기 위한 인공지능 학습용 데이터 약 60,000장을 구축하였고 수종 구분 AI 모델도 함께 개발하였다. 이러한 연구는 우리나라의 산림 변화를 사전에 예측하여 변화에 신속한 대응이 가능하고 산림 주제도 제작 시 필요한 수종 분할 이미지를 기초자료로 활용함으로써 업무 생산성을 높일 것으로 기대한다.

Keywords

References

  1. AI Hub [Internet], http://www.aihub.or.kr
  2. Creation Guideline for Artificiall Intelligence Learning Data Set, NIA, 2021.
  3. Quality Management Guildeline for Artificial Intelligence Learning Data v1.0, NIA, 2021.
  4. 국립산림과학원, "디지털 항공영상을 이용한 대축척 임상도 제작 및 갱신방법," 2012.
  5. 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.
  6. V. Andersson, Semantic Segmentation: Using Convolutional Neural Networks and Sparse Dictionaries. 2017.
  7. 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.
  8. 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.
  9. L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, "Encoder-decoder with atrous separable convolution for semantic image segmentation," ECCV, 2018.
  10. 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. https://doi.org/10.1109/TPAMI.2017.2699184
  11. 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. https://doi.org/10.1016/j.patcog.2011.07.013
  12. 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.
  13. 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.