Updating GIS Building Inventory Data Using High-Resolution Satellite Images for Earthquake Damage Assessment: Application to Metro Manila, Philippines

2006 ◽  
Vol 22 (1) ◽  
pp. 151-168 ◽  
Author(s):  
Hiroyuki Miura ◽  
Saburoh Midorikawa
Keyword(s):  
High Resolution ◽  
Damage Assessment ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Earthquake Damage ◽  
Inventory Data ◽  
Building Inventory ◽  
High Resolution Satellite Images ◽  
Metro Manila ◽  
Earthquake Damage Assessment

In order to conduct earthquake damage assessment, a methodology for updating GIS building inventory data in Metro Manila, Philippines, using remote sensing data is proposed. The locations of newly constructed mid- and high-rise buildings are detected from high-resolution satellite images using the image analysis technique, while the number of low-rise buildings is estimated from the built-up areas on a land cover classification map. The building inventory data is updated by incorporating the data on the newly constructed buildings into the existing data. The number of buildings in the updated inventory data shows good agreement with the results of the manual interpretation and a recent survey. A building damage assessment for a scenario earthquake is conducted using the updated inventory data.

scholarly journals A fuzzy decision making system for building damage map creation using high resolution satellite imagery

2013 ◽  
Vol 13 (2) ◽  
pp. 455-472 ◽  
Author(s):  
H. Rastiveis ◽  
F. Samadzadegan ◽  
P. Reinartz
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Damage Assessment ◽  
Fuzzy Inference ◽  
Earthquake Damage ◽  
Fuzzy Decision Making ◽  
Inference System ◽  
High Resolution Satellite Imagery ◽  
Roof Part ◽  
Earthquake Damage Assessment

Abstract. Recent studies have shown high resolution satellite imagery to be a powerful data source for post-earthquake damage assessment of buildings. Manual interpretation of these images, while being a reliable method for finding damaged buildings, is a subjective and time-consuming endeavor, rendering it unviable at times of emergency. The present research, proposes a new state-of-the-art method for automatic damage assessment of buildings using high resolution satellite imagery. In this method, at the first step a set of pre-processing algorithms are performed on the images. Then, extracting a candidate building from both pre- and post-event images, the intact roof part after an earthquake is found. Afterwards, by considering the shape and other structural properties of this roof part with its pre-event condition in a fuzzy inference system, the rate of damage for each candidate building is estimated. The results obtained from evaluation of this algorithm using QuickBird images of the December 2003 Bam, Iran, earthquake prove the ability of this method for post-earthquake damage assessment of buildings.

Visual Damage Assessment using High-Resolution Satellite Images following the 2003 Bam, Iran, Earthquake

2005 ◽  
Vol 21 (1_suppl) ◽  
pp. 309-318 ◽  
Author(s):  
Keiko Saito ◽  
Robin Spence ◽  
Terence A. de C Foley
Keyword(s):  
High Resolution ◽  
Damage Assessment ◽  
Satellite Images ◽  
Building Damage ◽  
Absolute Difference ◽  
Visual Interpretation ◽  
Damage Distribution ◽  
Visual Damage ◽  
High Resolution Satellite Images ◽  
Damage Map

Visual interpretation of the building damage distribution in Bam, Iran, caused by the earthquake on 26 December 2003 has been carried out using pre- and post-earthquake QuickBird panchromatic high-resolution satellite images to produce a damage map. Two experienced interpreters carried out the assessments, and their results were compared to analyze the reasons for discrepancies likely to occur from interpretations by different interpreters. The first damage interpretation was carried out on the post-earthquake image, whereas the second interpretation compared the pre- and post-earthquake images. The analysis revealed that when using only the post-earthquake image, interpreters tend to underestimate the levels of damage, since both interpreters assigned higher damage levels when the pre- and post-earthquake image were compared than when only using the post-earthquake image. The absolute difference in the damage levels the two interpreters assigned in the post-only assessment and pre-and post-event comparison assessment remained the same.

scholarly journals High-Resolution Early Earthquake Damage Assessment System for Mexico City Based on a Single-Station

2017 ◽  
Vol 56 (1) ◽  
Author(s):  
Mario Ordaz ◽  
Eduardo Reinoso ◽  
Miguel A. Jaimes ◽  
Leonardo Alcántara ◽  
Citlali Pérez
Keyword(s):  
High Resolution ◽  
Mexico City ◽  
Damage Assessment ◽  
Earthquake Damage ◽  
Assessment System ◽  
Peak Ground Velocity ◽  
Population Database ◽  
Emergency Plans ◽  
Single Station ◽  
Earthquake Damage Assessment

A high-resolution early earthquake damage assessment system is presented for Mexico City based on real-time computations of seismic spectral intensities at a reference site. To obtain intensities for the entire Mexico Valley, pre-calculated response spectral ratios at soft sites are used. The estimates of seismic intensities (peak ground acceleration, peak ground velocity and spectral ordinates for selected structural periods), together with intensity-damage relations for buildings, fatalities and water supply network were used to obtain the spatial distribution of expected damage throughout the city. The process takes approximately 10 minutes with no human intervention. Since the available time to carry out all the computations is short, we have built a representative building and population database that concentrates all the information in a square mesh of 400 • 400 m. Results are sent to an Emergency Center and to decision makers to trigger previously set emergency plans and to provide information before emergency plans are in full operation.

scholarly journals Mapping of Polar Areas Based on High-Resolution Satellite Images: The Example of the Henryk Arctowski Polish Antarctic Station

2017 ◽  
Vol 104 (1) ◽  
pp. 65-78
Author(s):  
Zdzisław Kurczyński ◽  
Sebastian Różycki ◽  
Paweł Bylina
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Mean Squared Error ◽  
Remote Sensing Data ◽  
Aerial Images ◽  
Polar Regions ◽  
Technical Requirements ◽  
Digital Elevation ◽  
High Resolution Satellite Images

Abstract To produce orthophotomaps or digital elevation models, the most commonly used method is photogrammetric measurement. However, the use of aerial images is not easy in polar regions for logistical reasons. In these areas, remote sensing data acquired from satellite systems is much more useful. This paper presents the basic technical requirements of different products which can be obtain (in particular orthoimages and digital elevation model (DEM)) using Very-High-Resolution Satellite (VHRS) images. The study area was situated in the vicinity of the Henryk Arctowski Polish Antarctic Station on the Western Shore of Admiralty Bay, King George Island, Western Antarctic. Image processing was applied on two triplets of images acquired by the Pléiades 1A and 1B in March 2013. The results of the generation of orthoimages from the Pléiades systems without control points showed that the proposed method can achieve Root Mean Squared Error (RMSE) of 3-9 m. The presented Pléiades images are useful for thematic remote sensing analysis and processing of measurements. Using satellite images to produce remote sensing products for polar regions is highly beneficial and reliable and compares well with more expensive airborne photographs or field surveys.

scholarly journals TOWARDS AN AUTOMATED FLOOD AREA EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 97-104
Author(s):  
R. G. C. J. Kapilaratne ◽  
S. Kaneta
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Global Climate ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Flood Events ◽  
Large Area ◽  
High Resolution Satellite Images ◽  
Catastrophic Impact

Abstract. Flooding is considered as one of the most devastated natural disasters due to its adverse effect on human lives as well as economy. Since more population concentrate towards flood prone areas and frequent occurrence of flood events due to global climate change, there is an urgent need in remote sensing community for faster and reliable inundation mapping technologies to increase the preparedness of population and reduce the catastrophic impact. With the recent advancement in remote sensing technologies and integration capability of deep learning algorithms with remote sensing data makes faster mapping of large area is feasible. Therefore, this study attempted to explore a faster and low cost solution for flood area extraction by integrating convolution neural networks (CNNs) with high resolution (1.5 m) SPOT satellite images. By consider the system requirement as a measure of cost, capabilities (speed and accuracy) of a deeper (ResNet101) and a shallower (MobileNetV2) CNNs on flood mapping were examined and compared. The models were trained and tested with satellite images captured during several flood events occurred in Japan. It is observed from the results that ResNet101 obtained better flood area mapping accuracy than MobileNetV2. Whereas, MobileNetV2 is having much higher capabilities in faster mapping in 0.3 s/km2 with a competitive accuracy and minimal system requirements than ResNet101.

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