Automatic vegetation identification in Google Earth images using a convolutional neural network: A case study for Japanese bamboo forests

Watanabe, Satoru; Kim, Su-Mi; T, Ise

doi:10.1101/351643

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…The current revolution in deep learning algorithms for computer vision also provides opportunities to improve analysis of remote sensing data. Numerous studies have been published on the classification of medium (e.g., Watanabe et al, 2018;Gallwey et al, 2020;Rai et al, 2020;Virnodkar et al 2020) and high resolution (e.g. Flood et al, 2019;Schiefer et al, 2020;Zhang et al, 2020) satellite images using deep learning methods (Kattenborn et al, 2021).…”

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confidence: 99%

Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network

Jakab

Leeuwen

Tobak

2021

Journal of Environmental Geography

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Agricultural production in greenhouses shows a rapid growth in many parts of the world. This form of intensive farming requires a large amount of water and fertilizers, and can have a severe impact on the environment. The number of greenhouses and their location is important for applications like spatial planning, environmental protection, agricultural statistics and taxation. Therefore, with this study we aim to develop a methodology to detect plastic greenhouses in remote sensing data using machine learning algorithms. This research presents the results of the use of a convolutional neural network for automatic object detection of plastic greenhouses in high resolution remotely sensed data within a GIS environment with a graphical interface to advanced algorithms. The convolutional neural network is trained with manually digitized greenhouses and RGB images downloaded from Google Earth. The ArcGIS Pro geographic information system provides access to many of the most advanced python-based machine learning environments like Keras – TensorFlow, PyTorch, fastai and Scikit-learn. These libraries can be accessed via a graphical interface within the GIS environment. Our research evaluated the results of training and inference of three different convolutional neural networks. Experiments were executed with many settings for the backbone models and hyperparameters. The performance of the three models in terms of detection accuracy and time required for training was compared. The model based on the VGG_11 backbone model (with dropout) resulted in an average accuracy of 79.2% with a relatively short training time of 90 minutes, the much more complex DenseNet121 model was trained in 16.5 hours and showed a result of 79.1%, while the ResNet18 based model showed an average accuracy of 83.1% with a training time of 3.5 hours.

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Section: Introductionmentioning

confidence: 99%

Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network

Jakab

Leeuwen

Tobak

2021

Journal of Environmental Geography

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Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

et al. 2022

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The recent advancement in data science coupled with the revolution in digital and satellite technology has improved the potential for artificial intelligence (AI) applications in the forestry and wildlife sectors. India shares 7% of global forest cover and is the 8th most biodiverse region in the world. However, rapid expansion of developmental projects, agriculture, and urban areas threaten the country’s rich biodiversity. Therefore, the adoption of new technologies like AI in Indian forests and biodiversity sectors can help in effective monitoring, management, and conservation of biodiversity and forest resources. We conducted a systematic search of literature related to the application of artificial intelligence (AI) and machine learning algorithms (ML) in the forestry sector and biodiversity conservation across globe and in India (using ISI Web of Science and Google Scholar). Additionally, we also collected data on AI-based startups and non-profits in forest and wildlife sectors to understand the growth and adoption of AI technology in biodiversity conservation, forest management, and related services. Here, we first provide a global overview of AI research and application in forestry and biodiversity conservation. Next, we discuss adoption challenges of AI technologies in the Indian forestry and biodiversity sectors. Overall, we find that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries. However, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption of AI technology in India. We hope that this synthesis will motivate forest officials, scientists, and conservationists in India to explore AI technology for biodiversity conservation and forest management.

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Automatic vegetation identification in Google Earth images using a convolutional neural network: A case study for Japanese bamboo forests

Cited by 2 publications

References 23 publications

Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network

Detection of Plastic Greenhouses Using High Resolution Rgb Remote Sensing Data and Convolutional Neural Network

Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

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