PT  - JOURNAL ARTICLE
AU  - Shuntaro Watanabe
AU  - Kazuaki Sumi
AU  - Takeshi Ise
TI  - Using deep learning for bamboo forest detection from Google Earth images
AID  - 10.1101/351643
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 351643
4099  - http://biorxiv.org/content/early/2018/06/20/351643.short
4100  - http://biorxiv.org/content/early/2018/06/20/351643.full
AB  - Classifying and mapping vegetation are very important in environmental science or natural resource management. However, these tasks are not easy because conventional methods such as field survey are highly labor intensive. Automatic identification of the objects from visual data is one of the most promising way to reduce the cost for vegetation mapping. Although deep learning has become the new solution for image recognition and classification recently, in general, detection of ambiguous objects such as vegetation still has been considered difficult. In this paper, we investigated the potential for adapting the chopped picture method, a recently described protocol of deep learning, to detect plant community in Google Earth images. We selected bamboo forests as the target. We obtained Google Earth images from 3 regions in Japan using Google Earth. Applying deep convolutional neural network, the model successfully learned the features of bamboo forests in Google Earth images and the best trained model successfully detected 97 % targets. Our results also show that identification accuracy is strongly depends on the image resolution and the quality of training data. Our results highlight that deep learning and chopped picture method potentially become a powerful tool for high accuracy automated detection and mapping of vegetation.