Comparison and Analysis of Algorithms Used for Detecting Slums in Satellite Images

2019 ◽  
Author(s):  
Pallavi Saindane ◽  
Gayatri Ganapathy ◽  
Neha Prabhavalkar ◽  
Nilesh Bhatia ◽  
Aishwarya Vaidya
Keyword(s):  
Satellite Images ◽  
Analysis Of Algorithms ◽  
Comparison And Analysis

scholarly journals Comparison and Analysis of Algorithms used for Detecting Slums in Satellite Images

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Pallavi Saindane ◽  
Gayatri Ganapathy ◽  
Neha Prabhavalkar ◽  
Nilesh Bhatia ◽  
Aishwarya Vaidyaa
Keyword(s):  
Satellite Images ◽  
Analysis Of Algorithms ◽  
Comparison And Analysis

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

The Effects of the sphragis on male and female genitalia in Acraea (Nymphalidae) butterflies

2020 ◽  
Vol 22 ◽  
Author(s):  
Shannon L Summers ◽  
Akito Y Kawahara ◽  
Ana P. S. Carvalho
Keyword(s):  
Interspecific Variation ◽  
Sperm Storage ◽  
Male Mating ◽  
Museum Collections ◽  
Storage Organ ◽  
Mating Plug ◽  
Males And Females ◽  
Comparison And Analysis ◽  
Imagej Software ◽  
Sexually Antagonistic Coevolution

Male mating plugs have been used in many species to prevent female re-mating and sperm competition. One of the most extreme examples of a mating plug is the sphragis, which is a large, complex and externalized plug found only in butterflies. This structure is found in many species in the genus Acraea (Nymphalidae) and provides an opportunity for investigation of the effects of the sphragis on the morphology of the genitalia, which is poorly understood. This study aims to understand morphological interspecific variation in the genitalia of Acraea butterflies. Using specimens from museum collections, abdomen dissections were conducted on 19 species of Acraea: 9 sphragis bearing and 10 non-sphragis bearing species. Genitalia imaging was performed for easier comparison and analysis and measurements of genitalia structures was done using ImageJ software. Some distinguishing morphological features in the females were found. The most obvious difference is the larger and more externalized copulatory opening in sphragis bearing species, with varying degrees of external projections. Females of the sphragis bearing species also tend to have a shorter ductus (the structure that connects the copulatory opening with the sperm storage organ) than those without the sphragis. These differences may be due to a sexually antagonistic coevolution between the males and females, where the females evolve larger and more difficult to plug copulatory openings and the males attempt to prevent re-mating with the sphragis.

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

Designing a coherent land registration system for rural Portugal

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Tiago NUNES ◽  
Miguel COUTINHO
Keyword(s):  
Satellite Images ◽  
Field Research ◽  
Digital Native ◽  
Web Based ◽  
Registration System ◽  
Land Registration ◽  
Daily Rate ◽  
Property Owners ◽  
Property Identification

After almost a century of several attempts to establish a coherent land registration system across the whole country, in 2017 the Portuguese government decided to try a new, digital native approach to the problem. Thus, a web-based platform was created, where property owners from 10 pilot municipalities could manually identify their lands’ properties using a map based on satellite images. After the first month of submissions, it became clear that at the current daily rate, it would take years to achieve the goal of 100% rural property identification across just the 10 municipalities. Field research during the first month after launch enabled us to understand landowners’ relationships with their land, map their struggles with the platform, and prototype ways to improve the whole service. Understanding that these improvements would still not be enough to get to the necessary daily rate, we designed, tested and validated an algorithm that allows us to identify a rural property shape and location without coordinates. Today, we are able to help both Government and landowners identify a rural property location with the click of a button.

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