Automatic Extraction of Road Network from Satellite Images of Urban Areas Using Convolution Neural Network

2021 ◽  
pp. 181-192
Author(s):  
Aman Nohwal ◽  
Tushar Jangid ◽  
Narayan Panigrahi
Keyword(s):  
Neural Network ◽  
Urban Areas ◽  
Road Network ◽  
Satellite Images ◽  
Convolution Neural Network ◽  
Automatic Extraction

Convolution Neural Network-based Agriculture Drought Prediction using Satellite Images

2021 ◽  
Author(s):  
Vandana Sudhakar Sardar ◽  
Yindumathi K M ◽  
Shilpa Shashikant Chaudhari ◽  
Prosenjith Ghosh
Keyword(s):  
Neural Network ◽  
Satellite Images ◽  
Convolution Neural Network ◽  
Drought Prediction

Identification of Violence Images Using Convolution Neural Network in Social Network

2020 ◽  
Vol 5 (3) ◽  
pp. 34-38
Author(s):  
Santhi Baskaran ◽  
Jahnavi Korrapati ◽  
Sooriya K. ◽  
Pavithra R.
Keyword(s):  
Neural Network ◽  
Social Network ◽  
Convolution Neural Network

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

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