scholarly journals Accelerate disaster risk mapping using an automated zonal change detection model for loss estimation and recovery: a flood case study

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Ali Amasha
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Flash Flood ◽  
Disaster Risk ◽  
Loss Estimation ◽  
Risk Mapping ◽  
Detection Model ◽  
Applied Model ◽  
High Resolution Satellite Images ◽  
Before And After

Abstract Background The flash flood still constitutes one of the major natural meteorological disasters harmfully threatening local communities, that creates life losses and destroying infrastructures. The severity and magnitude of disasters always reflected from the size of impacts. Most of the conventional research models related to flooding vulnerability are focusing on hydro-meteorological and morphometric measurements. It, however, requires quick estimate of the flood losses and assess the severity using reliable information. An automated zonal change detection model applied, using two high-resolution satellite images dated 2009 and 2011 coupled with LU/LC GIS layer, on western El-Arish City, downstream of Wadi El-Arish basin. The model enabled to estimate the severity of a past flood incident in 2010. Results The model calculated the total changes based on the before and after satellite images based on pixel-by-pixel comparison. The estimated direct-damages nearly 32,951 m2 of the total mapped LU/LC classes; (e.g., 11,407 m2 as 3.17% of the cultivated lands; 6031 m2 as 7.22% of the built-up areas and 4040 m2 as 3.62% of the paved roads network). The estimated cost of losses, in 2010 economic prices for the selected three LU/LC classes, is nearly 25 million USD, for the cultivation fruits and olives trees, ~ 4 million USD for built-up areas and ~ 1 million USD for paved roads network. Conclusion The disasters’ damage and loss estimation process takes many detailed data, longtime, and costed as well. The applied model accelerates the disaster risk mapping that provides an informative support for loss estimation. Therefore, decision-makers and professionals need to apply this model for quick the disaster risks management and recovery.

scholarly journals OBJECT-ORIENTED ANALYSIS OF SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS FOR POST-EARTHQUAKE BUILDINGS CHANGE DETECTION

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 139-144
Author(s):  
N. Khodaverdi zahraee ◽  
H. Rastiveis
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Feature Vector ◽  
Human Life ◽  
Textural Features ◽  
Image Objects ◽  
Input Feature ◽  
High Resolution Satellite Images ◽  
Before And After ◽  
Artificial Neural

Earthquake is one of the most divesting natural events that threaten human life during history. After the earthquake, having information about the damaged area, the amount and type of damage can be a great help in the relief and reconstruction for disaster managers. It is very important that these measures should be taken immediately after the earthquake because any negligence could be more criminal losses. The purpose of this paper is to propose and implement an automatic approach for mapping destructed buildings after an earthquake using pre- and post-event high resolution satellite images. In the proposed method after preprocessing, segmentation of both images is performed using multi-resolution segmentation technique. Then, the segmentation results are intersected with ArcGIS to obtain equal image objects on both images. After that, appropriate textural features, which make a better difference between changed or unchanged areas, are calculated for all the image objects. Finally, subtracting the extracted textural features from pre- and post-event images, obtained values are applied as an input feature vector in an artificial neural network for classifying the area into two classes of changed and unchanged areas. The proposed method was evaluated using WorldView2 satellite images, acquired before and after the 2010 Haiti earthquake. The reported overall accuracy of 93% proved the ability of the proposed method for post-earthquake buildings change detection.

scholarly journals APPLICATION OF OFF-NADIR SATELLITE IMAGERY IN EARTHQUAKE DAMAGE ASSESSMENT USING OBJECT-BASED HOG FEATURE DESCRIPTOR

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 167-171
Author(s):  
S. Jabari ◽  
M. Krafczek
Keyword(s):  
Change Detection ◽  
Damage Detection ◽  
Disaster Management ◽  
Satellite Images ◽  
Feature Descriptor ◽  
Object Based ◽  
Before And After ◽  
Map Generation ◽  
Spectral Comparison ◽  
Damage Map

<p><strong>Abstract.</strong> One of the most crutial applications of very-high-resolution (VHR) satellite images is disaster management. In disaster management, time is of great importance. Therefore, it is vital to acquire satellite images as quickly as possible and benefit from automatic change detection to speed up the process. Automatic damage map generation is performed by overlaying the co-registered before and after images of the area of interest and, compring them to highlight the affected infrastructures. For speeding up image capture, satellites tilt their imaging sensor and take images from oblique angles. However, this kind of image acquisition causes severe geometric distortion in the images, which hinders image co-registration in automatic change detection. In this study, a Patch-Wise Co-Registration (PWCR) solution is used. In this algorithm, the before and after images are co-registered in a segment-by-segment manner. From the literature, this algorithm is followed by a spectral comparison to detect changes. However, due to the complicated structure of debris in damage detection applications, spectral comparison methods cannot perform well. In this work, we developed an object-based method using Histogram of Oriented Gradient descriptor to detect damges and compared our results to different existing spectral and textural change detection methods. The algorithm is tested on images from the 2010-Heidi earthquake, captured by DigitalGlobe. The achieved highly accurate results demonstrate the potential of using off-nadir remote sensing images for automatic urban damage detection possibly in early response systems as it speeds up the damage map generation by providing flexibility to utilize images taken from different anlges.</p>

scholarly journals Multitemporal High-Resolution Satellite Images for the Study and Monitoring of an Ancient Mesopotamian City and its Surrounding Landscape: The Case of Ur

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Giacomo Di Giacomo ◽  
Giuseppe Scardozzi
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Gulf War ◽  
Archaeological Remains ◽  
Important Data ◽  
High Resolution Satellite Images ◽  
Ancient City ◽  
Before And After ◽  
Surrounding Landscape ◽  
The City

The paper concerns the use of multitemporal high-resolution satellite images for the study of the ancient city of Ur, in southern Mesopotamia, inaccessible to scholars from 2003. The acquired dataset is composed by two Gambit KH-7 (1966) and one Corona KH-4B (1968) declassified spy space photos and by few images taken by the recent satellites for civilian use QuickBird-2 (2002, 2004, 2007), Ikonos-2 (2008), and WorldView-1 (2008). The processing of all these images and the integration with ASTER and SRTM DEMs allowed the acquisition of new data about the topographical layout of the city and its monuments and ancient roads; the georeferencing of all archaeological remains and traces visible on the images allowed the upgrade of the archaeological map of Ur. The research also provided important data concerning the reconstruction of the surrounding landscape, where a lot of traces of old channels and riverbeds of the Euphrates were identified in areas much modified and altered during the last decades by urbanization and agricultural works. Moreover, the multitemporal images allowed the monitoring of the conservation of the archaeological area, particularly before and after second Gulf War.

scholarly journals Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

2017 ◽  
Vol 9 (8) ◽  
pp. 804 ◽  
Author(s):  
Biao Wang ◽  
Jaewan Choi ◽  
Seokeun Choi ◽  
Soungki Lee ◽  
Penghai Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Image Fusion ◽  
Land Cover Change ◽  
Satellite Images ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Land Cover Change Detection

scholarly journals Multi-Temporal Change Detection Analysis of Vertical Sprawl over Limassol City Centre and Amathus Archaeological Site in Cyprus during 2015–2020 Using the Sentinel-1 Sensor and the Google Earth Engine Platform

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1884
Author(s):  
Athos Agapiou
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Archaeological Site ◽  
Google Earth ◽  
City Centre ◽  
Time Interval ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
The City

Urban sprawl can negatively impact the archaeological record of an area. In order to study the urbanisation process and its patterns, satellite images were used in the past to identify land-use changes and detect individual buildings and constructions. However, this approach involves the acquisition of high-resolution satellite images, the cost of which is increases according to the size of the area under study, as well as the time interval of the analysis. In this paper, we implemented a quick, automatic and low-cost exploration of large areas, for addressing this purpose, aiming to provide at a medium resolution of an overview of the landscape changes. This study focuses on using radar Sentinel-1 images to monitor and detect multi-temporal changes during the period 2015–2020 in Limassol, Cyprus. In addition, the big data cloud platform, Google Earth Engine, was used to process the data. Three different change detection methods were implemented in this platform as follow: (a) vertical transmit, vertical receive (VV) and vertical transmit, horizontal receive (VH) polarisations pseudo-colour composites; (b) the Rapid and Easy Change Detection in Radar Time-Series by Variation Coefficient (REACTIV) Google Earth Engine algorithm; and (c) a multi-temporal Wishart-based change detection algorithm. The overall findings are presented for the wider area of the Limassol city, with special focus on the archaeological site of “Amathus” and the city centre of Limassol. For validation purposes, satellite images from the multi-temporal archive from the Google Earth platform were used. The methods mentioned above were able to capture the urbanization process of the city that has been initiated during this period due to recent large construction projects.

scholarly journals DEEP BAYESIAN ACTIVE LEARNING IN HIGH-RESOLUTION SATELLITE IMAGES FOR CHANGE DETECTION IN URBAN AND SUBURBAN AREAS

2021 ◽  
Vol V-3-2021 ◽  
pp. 175-180
Author(s):  
L. Ragia ◽  
A. Panagiotopoulou
Keyword(s):  
High Resolution ◽  
Active Learning ◽  
Change Detection ◽  
Satellite Images ◽  
Robust Statistics ◽  
Training Set ◽  
Suburban Areas ◽  
Training Images ◽  
High Resolution Satellite Images ◽  
Zero Test

Abstract. In this work the problem of change detection in high-resolution (HR) satellite images is addressed. The active learning (AL) algorithm Bayesian active learning disagreement (BALD) is applied on WorldView images of urban and suburban areas in the island of Crete, Greece. Comparisons with results from random sampling (RS) on AL are carried out. Several cases of selecting different amounts of images in the training set of a convolutional neural network (CNN) are experimented. The results show that the validation accuracy of classification as changed or unchanged of the BALD algorithm is superior to that of the RS algorithm. Indeed, the BALD algorithm achieves zero test error against the test errors 34.6% and 38.5% of the RS algorithm. Actually, as the amount of training images increases, the accuracy also increases. Interesting experiments could be executed in the future utilizing estimators from robust statistics inside the AL acquisition function framework. Up to now in the literature no other work has appeared to present deep AL on WorldView images for change detection.

scholarly journals PROPOSAL OF CHANGE DETECTION IN LAND COVER FOR PIPELINE MONITORING FROM RAPIDEYE AND IKONOS IMAGES

2014 ◽  
Vol 32 (4) ◽  
pp. 655 ◽  
Author(s):  
Paulina Setti Riedel ◽  
Mara Lúcia Marques ◽  
Mateus Vidotti Ferreira ◽  
Marcelo Elias Delaneze
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Satellite Images ◽  
Bare Soil ◽  
Reference Limit ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
Pipeline Monitoring ◽  
Structural Preservation

ABSTRACT. The goal of this study was to improve and evaluate the applicability of a methodological procedure of pipeline monitoring to reveal indicators of thirdparty activities that may interfere with the structural preservation of pipes and environmental damages. The procedure was developed from the technique of changedetection through object-based classification of land cover, using high resolution satellite images applied to a section of the Guararema-Mauá – São Paulo pipeline, Brazil. In the seven-month monitoring period performed with RapidEye imaging, an area of 2.024 km2 was identified as area of change, corresponding to 3.30% of thetotal area analyzed. For the monitoring performed with Ikonos imaging during a four-month period, changes were detected in an area of 0.187 km2, which correspondedto 1.92% of the total area analyzed. The main changes in land cover were from Bare Soil to Grassland, due to changes related to the different stages of agriculturalactivity and reforestation areas, as well as the natural regeneration of vegetation over the pipeline and solid waste landfill. The results of the change detection of landcover from object-based classification were close to the technique reference limit for areas with great complexity and diversity of space occupation.Keywords: structural preservation of pipes, object-based classification, high resolution satellite images. RESUMO. Este estudo teve por objetivo avaliar a aplicabilidade de um procedimento metodológico de monitoramento de faixas de dutos que revelem indicativos deatividades de terceiros que podem interferir na integridade estrutural dos dutos e provocar danos ambientais. O procedimento foi desenvolvido a partir da técnica dedetecção de mudanças na cobertura da terra pela classificação baseada no objeto, com utilização de imagens orbitais de alta resolução. Este procedimento foi empregadoem um trecho da faixa de dutos Guararema-Mauá – SP, no monitoramento realizado por meio de imagens RapidEye. Em um período de sete meses, foram identificados 2,024 km2 como área de mudança, que corresponde a 3,30% do total da área analisada. Para o monitoramento realizado a partir da imagem Ikonos, com período de quatro meses, foi identificada como mudança uma área de 0,187 km2, correspondendo a 1,92% do total da área analisada. As principais mudanças ocorridas foramentre Solo Exposto e Vegetaçao Rasteira, devido às alterações ocorridas nos estágios de cultivo agrícola e áreas de reflorestamento, como também, estão associadas às áreas de regeneração da vegetação da faixa de dutos e aterro sanitário. Os resultados da detecção de mudanças da cobertura da terra pela classificação baseada no objeto atingiram acertos próximos ao limite de para esta técnica, em áreas com grande complexidade e diversidade de ocupação do espaço.Palavras-chave: integridade estrutural dos dutos, classificação baseada no objeto, imagens orbitais de alta resolução.

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