scholarly journals MLC based Classification of Satellite Images for Damage Assessment Index in Disaster Management

2019 ◽  
Vol 8 (6) ◽  
pp. 2825-2830
Author(s):  
Darala Siva ◽  
Keyword(s):  
Disaster Management ◽  
Damage Assessment ◽  
Satellite Images ◽  
Assessment Index

scholarly journals Satellite Imagery-Based Damage Assessment on Nineveh and Nebi Yunus Archaeological Site in Iraq

2020 ◽  
Vol 12 (10) ◽  
pp. 1672
Author(s):  
Emanuele Angiuli ◽  
Epifanio Pecharromán ◽  
Pablo Vega Ezquieta ◽  
Maria Gorzynska ◽  
Ionut Ovejanu
Keyword(s):  
European Union ◽  
Damage Assessment ◽  
Visual Inspection ◽  
Satellite Images ◽  
Archaeological Site ◽  
The European Union ◽  
Satellite Centre ◽  
The City ◽  
The Eu

During the last decades, archaeological site looting throughout Iraq has increased significantly up to a point where some of the most famous and relevant ancient Mesopotamian cities are currently threatened in their integrity. Several important archaeological monuments and artifacts have been destroyed, due to ISIL attacks and associated looting. Since 2016, the policies of the European Union have been increasingly harsh to condemn these atrocious acts of destruction. In such a scenario, the European Union Satellite Centre can be an invaluable instrument for the identification and assessment of the damage in areas occupied by ISIL. A detailed view of the damage suffered by the Nineveh and Nebi Yunus ancient sites, in Iraq, was assessed via visual inspection. The analysis was conducted considering the main events that occurred in the city of Mosul, between November 2013 and March 2018. More than 25 satellite images, new acquisitions and archived, supported by collateral data, allowed the detection and classification of the damage occurred over time. A description of the methodology and the classification of category and type of damage is presented. The results of the analysis confirm the dramatic levels of destruction that these two ancient sites have been suffering since 2013. The analysis reported in this paper is part of a wider study that the SatCen conducted in cooperation with the EU Counter-Terrorism Office and PRISM Office. The whole activity aimed at confirming to EU institutions the massive looting and trafficking operated in the area. The results have been provided to archaeologists in the field as well in support of local authorities who are trying to evaluate the current situation in the area.

Classification of vegetation in Cucphuong National Park with the aim of large-scale mapping, Vietnam

1996 ◽  
pp. 64-67 ◽  
Author(s):  
Nguen Nghia Thin ◽  
Nguen Ba Thu ◽  
Tran Van Thuy
Keyword(s):  
National Park ◽  
Large Scale ◽  
Satellite Images ◽  
Field Work ◽  
Aerial Photographs ◽  
Vegetation Map ◽  
Dense Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest

The tropical seasonal rainy evergreen broad-leaved forest vegetation of the Cucphoung National Park has been classified and the distribution of plant communities has been shown on the map using the relations of vegetation to geology, geomorphology and pedology. The method of vegetation mapping includes: 1) the identifying of vegetation types in the remote-sensed materials (aerial photographs and satellite images); 2) field work to compile the interpretation keys and to characterize all the communities of a study area; 3) compilation of the final vegetation map using the combined information. In the classification presented a number of different level vegetation units have been identified: formation classes (3), formation sub-classes (3), formation groups (3), formations (4), subformations (10) and communities (19). Communities have been taken as mapping units. So in the vegetation map of the National Park 19 vegetation categories has been shown altogether, among them 13 are natural primary communities, and 6 are the secondary, anthropogenic ones. The secondary succession goes through 3 main stages: grassland herbaceous xerophytic vegetation, xerophytic scrub, dense forest.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

Machine Learning based Classification of Online News Data for Disaster Management

2020 ◽  
Author(s):  
Lakshmi S Gopal ◽  
Rekha Prabha ◽  
Divya Pullarkatt ◽  
Maneesha Vinodini Ramesh
Keyword(s):  
Machine Learning ◽  
Disaster Management ◽  
Online News

Landcover classification of satellite images based on an adaptive interval fuzzy c-means algorithm coupled with spatial information

2019 ◽  
Vol 41 (6) ◽  
pp. 2189-2208
Author(s):  
Jindong Xu ◽  
Guozheng Feng ◽  
Baode Fan ◽  
Weiqing Yan ◽  
Tianyu Zhao ◽  
...  
Keyword(s):  
Satellite Images ◽  
Spatial Information ◽  
Fuzzy C Means ◽  
Landcover Classification ◽  
Fuzzy C Means Algorithm
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