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FORECASTING and ASSESSMENT of LAND CONDITIONS USING NEURAL NETWORKS. / Khokhriakova, Anastasiia; Grishkin, Valery.

In: CEUR Workshop Proceedings, Vol. 3041, 2021, p. 291-295.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Khokhriakova, A & Grishkin, V 2021, 'FORECASTING and ASSESSMENT of LAND CONDITIONS USING NEURAL NETWORKS', CEUR Workshop Proceedings, vol. 3041, pp. 291-295.

APA

Khokhriakova, A., & Grishkin, V. (2021). FORECASTING and ASSESSMENT of LAND CONDITIONS USING NEURAL NETWORKS. CEUR Workshop Proceedings, 3041, 291-295.

Vancouver

Khokhriakova A, Grishkin V. FORECASTING and ASSESSMENT of LAND CONDITIONS USING NEURAL NETWORKS. CEUR Workshop Proceedings. 2021;3041:291-295.

Author

Khokhriakova, Anastasiia ; Grishkin, Valery. / FORECASTING and ASSESSMENT of LAND CONDITIONS USING NEURAL NETWORKS. In: CEUR Workshop Proceedings. 2021 ; Vol. 3041. pp. 291-295.

BibTeX

@article{4aefa48803c5443fb233e2fa9bb527a1,
title = "FORECASTING and ASSESSMENT of LAND CONDITIONS USING NEURAL NETWORKS",
abstract = "In some regions, mainly occupied by agriculture and cattle breeding, irreversible soil changes, e.g. desertification, have appeared, which can lead to serious environmental and economic problems. This paper considers the application of neural networks for prediction and assessment of desertification-prone lands using satellite images. An autoencoder type of the neural network is applied for these purposes. Datasets were generated for training from the Sentinel-2 satellite open database. The first network was used for prediction. The second network is responsible for segmentation of the image into classes using NDVI index. In this paper we explain the method, the architecture of the network and present some experimental results. The presented method allows making a qualitative and quantitative assessment of possible changes, which can be useful for planning preventive works.",
keywords = "Autoencoder, Desertification, Forecasting, Multispectral images, Neural network, Satellite images",
author = "Anastasiia Khokhriakova and Valery Grishkin",
note = "Publisher Copyright: {\textcopyright} 2021 CEUR-WS. All rights reserved.; 9th International Conference {"}Distributed Computing and Grid Technologies in Science and Education{"}, GRID 2021 ; Conference date: 05-07-2021 Through 09-07-2021",
year = "2021",
language = "English",
volume = "3041",
pages = "291--295",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "RWTH Aahen University",
url = "https://indico.jinr.ru/event/1086/overview",

}

RIS

TY - JOUR

T1 - FORECASTING and ASSESSMENT of LAND CONDITIONS USING NEURAL NETWORKS

AU - Khokhriakova, Anastasiia

AU - Grishkin, Valery

N1 - Conference code: 9

PY - 2021

Y1 - 2021

N2 - In some regions, mainly occupied by agriculture and cattle breeding, irreversible soil changes, e.g. desertification, have appeared, which can lead to serious environmental and economic problems. This paper considers the application of neural networks for prediction and assessment of desertification-prone lands using satellite images. An autoencoder type of the neural network is applied for these purposes. Datasets were generated for training from the Sentinel-2 satellite open database. The first network was used for prediction. The second network is responsible for segmentation of the image into classes using NDVI index. In this paper we explain the method, the architecture of the network and present some experimental results. The presented method allows making a qualitative and quantitative assessment of possible changes, which can be useful for planning preventive works.

AB - In some regions, mainly occupied by agriculture and cattle breeding, irreversible soil changes, e.g. desertification, have appeared, which can lead to serious environmental and economic problems. This paper considers the application of neural networks for prediction and assessment of desertification-prone lands using satellite images. An autoencoder type of the neural network is applied for these purposes. Datasets were generated for training from the Sentinel-2 satellite open database. The first network was used for prediction. The second network is responsible for segmentation of the image into classes using NDVI index. In this paper we explain the method, the architecture of the network and present some experimental results. The presented method allows making a qualitative and quantitative assessment of possible changes, which can be useful for planning preventive works.

KW - Autoencoder

KW - Desertification

KW - Forecasting

KW - Multispectral images

KW - Neural network

KW - Satellite images

UR - http://www.scopus.com/inward/record.url?scp=85121642225&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85121642225

VL - 3041

SP - 291

EP - 295

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - 9th International Conference "Distributed Computing and Grid Technologies in Science and Education", GRID 2021

Y2 - 5 July 2021 through 9 July 2021

ER -

ID: 91657216