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Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for
Shuntaro Watanabe1,2, Kazuaki Sumi3, Takeshi Ise4
1Field Science Education and Research Center (FSERC), Kyoto University, Kitashirakawaoiwake-cho, Sakyo-ku, Kyoto, 606-8502, Japan. watanabe@sci.kagoshima-u.ac.jp.
BMC Ecology
|November 28, 2020
Summary
Convolutional Neural Networks (CNNs) and the chopped picture method show high accuracy in detecting vegetation from Google Earth images. This automated approach significantly reduces the labor and cost associated with traditional vegetation mapping.
Area of Science:
- Environmental Science
- Remote Sensing
- Computer Science
Background:
- Vegetation classification and mapping are essential for environmental science and resource management.
- Traditional field surveys are labor-intensive and costly.
- Automated methods using computer vision show promise for efficient vegetation mapping.
Purpose of the Study:
- To investigate the effectiveness of the chopped picture method with Convolutional Neural Networks (CNNs).
- To evaluate the efficiency of CNNs for plant community detection using Google Earth imagery.
Main Methods:
- Utilized Google Earth images from three regions in Japan, focusing on bamboo forests.
- Applied Convolutional Neural Networks (CNNs) with the chopped picture method for image analysis.
Main Results:
- The best-trained CNN model achieved over 90% accuracy in detecting target bamboo forests.
- CNN identification accuracy surpassed that of conventional machine learning methods.
Conclusions:
- CNNs combined with the chopped picture method offer a powerful tool for automated vegetation detection.
- This approach enables high-accuracy automated mapping of vegetation resources.
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