scholarly journals Detection of water surface natural objects based on the satellite sensing data

2021 ◽  
Vol 2131 (3) ◽  
pp. 032053
Author(s):  
N D Panasenko ◽  
N S Motuz
Keyword(s):  
Neural Network ◽  
Water Surface ◽  
Satellite Images ◽  
Computer Experiments ◽  
Filtration Method ◽  
Sensing Data ◽  
Azov Sea ◽  
Detection And Identification ◽  
Network Approaches ◽  
Sea Area

Abstract The article shows an application of satellite sensing data method in geoenvironmental monitoring of water surface. It is expected to apply combination of LBP and neural network approaches for detection and identification objects of natural and anthropogenic origin. The applying of satellite images, the implementation and operation of the filtration method and satellite sensing data assimilation in real or near-real time are considered to detect the blooming areas and their coordinates. The research demonstrates the need and possibility to apply neural approach and the theory of deep learning for solving the tasks. The results of computer experiments are presented basing on the images from satellites Resurs-P, WorldView and Landsat over the Azov sea area.

scholarly journals Recognition of “flowering” processes on the base of remote sensing data in shallow water ponds on the example of the Azov Sea

2020 ◽  
Vol 175 ◽  
pp. 12013 ◽  
Author(s):  
Marina Ganzhur ◽  
Nikita Dyachenko ◽  
Olga Smirnova ◽  
Anna Poluyan ◽  
Natalya Panasenko
Keyword(s):  
Remote Sensing ◽  
Shallow Water ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Shallow Waters ◽  
Sea Of Azov ◽  
The Sea Of Azov ◽  
Sensing Data ◽  
Azov Sea

This work considers to the processes of «bloom» phytoplankton processes that cause hypoxic phenomena in shallow waters the example of the Sea of Azov. For the accumulation of information, multichannel satellite images of remote sensing are taken as a basis. In the process, the task of programmatically highlighting the contours of the areas of «bloom» is implemented.

scholarly journals Algorithm for monitoring the plankton population dynamics based on satellite sensing data

2021 ◽  
Vol 2131 (3) ◽  
pp. 032052
Author(s):  
N D Panasenko ◽  
A Yu Poluyan ◽  
N S Motuz
Keyword(s):  
Neural Network ◽  
Computer Experiments ◽  
Scientific Work ◽  
Water Area ◽  
Mass Center ◽  
Phytoplankton Population ◽  
Sensing Data ◽  
Phytoplankton Distribution ◽  
Network Methods ◽  
Plankton Population

Abstract The scientific work describes the algorithms for processing the multispectral water coastal imagery from satellite sensing data with the aim of identifying the phytoplankton population of a spotted structure: determining the contour, distributing color gradation and as a result - determining the concentration of phytoplankton distribution inside the zones and mass centers. Such characteristics let determine the speed of changing contours spots and their concentration, the mass center shift as a consequence of the water masses movement and the processes of phytoplankton growing and dying. All these may be done on the base of the processed image series of the same water area over different time (different dates). The combination of LBP and neural network methods are observed as algorithms for image processing and the results of computer experiments are presented.

scholarly journals Application of Artificial Intelligence Algorithms to Control the Use of Flood-prone Areas

2021 ◽  
pp. 6-24
Author(s):  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Large Spatial Scale ◽  
Sensing Data ◽  
Hazardous Area ◽  
Flood Prone Areas

This article examines the possibility of using artificial intelligence tools to analyze the use of territories prone to flooding during floods. A modern system for monitoring the economic use of flood-prone areas should be based on the use of Earth remote sensing data. The analysis of satellite images, being a laborious task, can be automated through the use of specially trained convolutional neural networks of semantic segmentation based on the algorithm proposed in this article. In this work, on the previously identified flooding zones, using remote sensing data, development objects are automatically determined (segmented) for different times and, by combining information at different times, an assessment of the intensity of this construction in the inter-flood period is made. To form a training sample, a survey of several settlements in the Trans-Baikal Territory was carried out using unmanned aerial vehicles. The neural network was configured using the Python language and the PyTorch library. To select the best convolutional neural network configuration, various combinations of architectures and encoder types were tested for performance and accuracy. The best result in terms of speed and accuracy was shown by the U-Net architecture, built using a convolutional neural network with an SE-ResNeXt50 encoder. According to satellite images of high spatial resolution for the Aginskoye village of Trans-Baikal Kray, a development map was drawn in the flood hazardous area in 2013 and 2019. The objects of development in the period between floods were identified. The results of the study can make it possible to consider a number of important factors when planning the rational use of flood-prone areas in order to improve the quality of life in the region. The obtained maps of the development of flood-prone zones of a large spatial scale are planned to be recommended in the work of state authorities in the field of water resources protection and elimination of natural disasters.

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

A new dyke rock from Pokrovo-Kyriyivo massif (Azov Sea AREA, Ukraine)

2017 ◽  
Vol 39 (3) ◽  
pp. 53-66 ◽  
Author(s):  
S. Kryvdik ◽  
◽  
V. Sharygin ◽  
V. Gatsenko ◽  
E. Lunev ◽  
...  
Keyword(s):  
Dyke Rock ◽  
Azov Sea ◽  
Sea Area

Detection and Identification of Voltage Variation Events Based on Artificial Neural Network

2020 ◽  
Vol 13 (5) ◽  
pp. 224
Author(s):  
Dimas Okky Anggriawan ◽  
Rauf Hanrif Mubarok ◽  
Eka Prasetyono ◽  
Endro Wahjono ◽  
M. Iqbal Fitrianto ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Detection And Identification ◽  
Voltage Variation ◽  
Artificial Neural
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