Application of Fully Convolutional Neural Networks to Mapping Industrial Oil Palm Plantations

Baklanov, Artem; Khachay, Michael; Pasynkov, Maxim

doi:10.1007/978-3-030-11027-7_16

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…Convolutional Neural Networks (CNN) is a state-of-the-art class of deep learning algorithms providing the multilevel representation of input data, which is usually applied to learn in classification or regression estimation. As is known (see, for example, [27]), Convolutional Neural Networks give extremely high-performance results in image analysis, including such hard-to-solve tasks as object detection [54] and semantic segmentation [55,56]. Another prominent advantage of CNNs is the reduced need for a preliminary construction of the feature space.…”

Section: Architectures Of the Examined Networkmentioning

confidence: 99%

Convolutional Neural Network Based Approach to In Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus

Forghani

Khachay

2020

Viruses

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Evaluation of the antigenic similarity degree between the strains of the influenza virus is highly important for vaccine production. The conventional method used to measure such a degree is related to performing the immunological assays of hemagglutinin inhibition. Namely, the antigenic distance between two strains is calculated on the basis of HI assays. Usually, such distances are visualized by using some kind of antigenic cartography method. The known drawback of the HI assay is that it is rather time-consuming and expensive. In this paper, we propose a novel approach for antigenic distance approximation based on deep learning in the feature spaces induced by hemagglutinin protein sequences and Convolutional Neural Networks (CNNs). To apply a CNN to compare the protein sequences, we utilize the encoding based on the physical and chemical characteristics of amino acids. By varying (hyper)parameters of the CNN architecture design, we find the most robust network. Further, we provide insight into the relationship between approximated antigenic distance and antigenicity by evaluating the network on the HI assay database for the H1N1 subtype. The results indicate that the best-trained network gives a high-precision approximation for the ground-truth antigenic distances, and can be used as a good exploratory tool in practical tasks.

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Section: Architectures Of the Examined Networkmentioning

confidence: 99%

Convolutional Neural Network Based Approach to In Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus

Forghani

Khachay

2020

Viruses

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Need for Geographical Information System Enabled Conversational Assistant Driven by Artificial Intelligence for Sandalwood Cultivation Support to Indian Farmer

Pujar¹,

Reddy

Ravisankar³

2022

Materials Horizons: From Nature to Nanomaterials

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Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

et al. 2019

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The land area covered by freely available very high-resolution (VHR) imagery has grown dramatically over recent years, which has considerable relevance for forest observation and monitoring. For example, it is possible to recognize and extract a number of features related to forest type, forest management, degradation and disturbance using VHR imagery. Moreover, time series of medium-to-high-resolution imagery such as MODIS, Landsat or Sentinel has allowed for monitoring of parameters related to forest cover change. Although automatic classification is used regularly to monitor forests using medium-resolution imagery, VHR imagery and changes in web-based technology have opened up new possibilities for the role of visual interpretation in forest observation. Visual interpretation of VHR is typically employed to provide training and/or validation data for other remote sensing-based techniques or to derive statistics directly on forest cover/forest cover change over large regions. Hence, this paper reviews the state of the art in tools designed for visual interpretation of VHR, including Geo-Wiki, LACO-Wiki and Collect Earth as well as issues related to interpretation of VHR imagery and approaches to quality assurance. We have also listed a number of success stories where visual interpretation plays a crucial role, including a global forest mask harmonized with FAO FRA country statistics; estimation of dryland forest area; quantification of deforestation; national reporting to the UNFCCC; and drivers of forest change.

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Application of Fully Convolutional Neural Networks to Mapping Industrial Oil Palm Plantations

Cited by 5 publications

References 20 publications

Convolutional Neural Network Based Approach to In Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus

Convolutional Neural Network Based Approach to In Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus

Need for Geographical Information System Enabled Conversational Assistant Driven by Artificial Intelligence for Sandalwood Cultivation Support to Indian Farmer

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

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