Open AccessFORECASTING AND ASSESSMENT OF LAND CONDITIONS USING NEURAL NETWORKS
Author(s) -
A. Khokhriakova,
Valery Grishkin
Publication year - 2021
Publication title -
9th international conference "distributed computing and grid technologies in science and education"
Language(s) - English
Resource type - Conference proceedings
DOI - 10.54546/mlit.2021.59.28.002
Subject(s) - artificial neural network , computer science , autoencoder , artificial intelligence , satellite , desertification , segmentation , machine learning , data mining , engineering , ecology , biology , aerospace engineering
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. Thispaper considers the application of neural networks for prediction and assessment of desertificationprone lands using satellite images. An autoencoder type of the neural network is applied for thesepurposes. Datasets were generated for training from the Sentinel-2 satellite open database. The firstnetwork was used for prediction. The second network is responsible for segmentation of the imageinto classes using NDVI index. In this paper we explain the method, the architecture of the networkand present some experimental results. The presented method allows making a qualitative andquantitative assessment of possible changes, which can be useful for planning preventive works.