Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests

Towards better exploiting convolutional neural networks for remote sensing scene classification

(2017) Keiller Nogueira et al.   PATTERN RECOGNITION

Detecting latitudinal and altitudinal expansion of invasive bamboo Phyllostachys edulis and Phyllostachys bambusoides (Poaceae) in Japan to project potential habitats under 1.5°C-4.0°C global warming

(2017) Kohei Takenaka Takano et al.   Ecology and Evolution

Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

(2017) Edwin Raczko et al.   European Journal of Remote Sensing

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

(2017) Emilio Guirado et al.   Remote Sensing

Deep Learning for Image-Based Cassava Disease Detection

(2017) Amanda Ramcharan et al.   Frontiers in Plant Science

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

(2017) Xiao Xiang Zhu et al.   IEEE Geoscience and Remote Sensing Magazine

Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images

(2016) Weijia Li et al.   Remote Sensing

Using Deep Learning for Image-Based Plant Disease Detection

(2016) Sharada P. Mohanty et al.   Frontiers in Plant Science

Chronological location analyses of giant bamboo (Phyllostachys pubescens) groves and their invasive expansion in a satoyama landscape area, western Japan

(2014) Shigeo Suzuki   PLANT SPECIES BIOLOGY

Remote sensing imagery in vegetation mapping: a review

(2008) Y. Xie et al.   Journal of Plant Ecology