Building Damage Assessment Using High-Resolution Satellite SAR Images of the 2010 Haiti Earthquake

2016 ◽  
Vol 32 (1) ◽  
pp. 591-610 ◽  
Author(s):  
Hiroyuki Miura ◽  
Saburoh Midorikawa ◽  
Masashi Matsuoka
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Building Damage ◽  
Haiti Earthquake ◽  
Sar Images ◽  
Linear Discriminant ◽  
Intensity Images ◽  
Image Pixels ◽  
The Difference ◽  
Collapsed Buildings

Damage to individual buildings in an urban area of Port-au-Prince, Haiti, from the 2010 Haiti earthquake was assessed by means of high-resolution synthetic aperture radar (SAR) intensity images and ancillary building footprints. A comparison of pre- and post-event images and a building damage inventory showed that backscattering intensity between images was more significantly changed in collapsed buildings than in less damaged buildings. The linear discriminant function, based on the difference and correlation coefficient of the images was developed to detect collapsed buildings. The result showed that almost 75% of the buildings were correctly detected by discriminant analysis. An accuracy assessment revealed the difficulty of detecting small and congested buildings because the number of image pixels was too small and the buildings were obscured by neighboring buildings and other features in the images.

Extraction of Tsunami-Flooded Areas and Damaged Buildings in the 2011 Tohoku-Oki Earthquake from TerraSAR-X Intensity Images

2013 ◽  
Vol 29 (1_suppl) ◽  
pp. 183-200 ◽  
Author(s):  
Wen Liu ◽  
Fumio Yamazaki ◽  
Hideomi Gokon ◽  
Shun-ichi Koshimura
Keyword(s):  
High Resolution ◽  
Emergency Management ◽  
Emergency Response ◽  
Water Surface ◽  
Tohoku Earthquake ◽  
Visual Interpretation ◽  
Automated Method ◽  
Intensity Images ◽  
The Difference ◽  
Flooded Areas

The Tohoku earthquake of 11 March 2011 caused very large tsunamis and widespread devastation. Various high-resolution satellites captured details of affected areas and were utilized in emergency response. In this study, high-resolution pre- and post-event TerraSAR-X intensity images were used to identify tsunami-flooded areas and damaged buildings. Since water surface generally shows very little backscatter, flooded areas could be extracted by the difference of backscattering coefficients between the pre- and post-event images. Impacted buildings were detected by calculating the difference and correlation coefficient within the outline of each building. The damage estimates were compared with visual interpretation results, which suggest that the overall accuracy of the proposed method for flooded areas was 80%, and for damaged buildings was 94%. Since the proposed half-automated method takes less processing time and is applicable to various cases, it is expected to provide quick and useful information in emergency management.

scholarly journals Geomorpho90m - Global high-resolution geomorphometry layers: empirical evaluation and accuracy assessment

2019 ◽  
Author(s):  
Giuseppe Amatulli ◽  
Daniel McInerney ◽  
Tushar Sethi ◽  
Peter Strobl ◽  
Sami Domisch
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Empirical Evaluation ◽  
Shuttle Radar Topography Mission ◽  
The United States ◽  
Rate Of Change ◽  
Environmental Models ◽  
Digital Elevation ◽  
The Difference ◽  
Elevation Model

Topographical relief is composed of the vertical and horizontal variations of the Earth's terrain and drives processes in geography, climatology, hydrology, and ecology. Its assessment and characterisation is fundamental for various types of modelling and simulation analyses. In this regard, the Multi-Error-Removed Improved Terrain (MERIT) Digital Elevation Model (DEM) is the best global, high-resolution DEM currently available at a 3 arc-seconds (90 m) resolution. This is an improved product as multiple error components have been corrected from the underlying Shuttle Radar Topography Mission (SRTM3) and ALOS World 3D - 30 m (AW3D30) DEMs. To depict topographical variations worldwide, we developed the Geomorpho90m dataset comprising of different geomorphometry features derived from the MERIT-DEM. The fully standardised geomorphometry variables consist of layers that describe (i) the rate of change using the first and second order derivatives, (ii) the ruggedness, and (iii) the geomorphology landform. To assess how remaining artefacts in the MERIT-DEM could affect the derived topographic variables, we compared our results with the same variables generated using the 3D Elevation Program (3DEP) DEM, which is the highest quality DEM for the United States of America. We compared the two data sources by calculating the first order derivative (i.e., the rate of change through space measured in degrees) of the difference between a MERIT-derived vs. a 3DEP-derived topographic variable. All newly-created topographic variables are readily available at resolutions of 3 and 7.5 arc-seconds under the WGS84 geographic system, and at a spatial resolution of 100 m under the Equi7 projection. The newly-developed Geomorpho90m dataset provides a globally standardised dataset for environmental models and analyses in the field of geography, geology, hydrology, ecology and biogeography.

scholarly journals Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model

2020 ◽  
Vol 10 (2) ◽  
pp. 602 ◽  
Author(s):  
Min Ji ◽  
Lanfa Liu ◽  
Rongchun Zhang ◽  
Manfred F. Buchroithner
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Imagery ◽  
Human Life ◽  
Haiti Earthquake ◽  
Intermediate Layers ◽  
High Resolution Satellite Imagery ◽  
Collapsed Buildings ◽  
Very High ◽  
Visual Geometry

The building is an indispensable part of human life which provides a place for people to live, study, work, and engage in various cultural and social activities. People are exposed to earthquakes, and damaged buildings caused by earthquakes are one of the main threats. It is essential to retrieve the detailed information of affected buildings after earthquakes. Very high-resolution satellite imagery plays a key role in retrieving building damage information since it captures imagery quickly and effectively after the disaster. In this paper, the pretrained Visual Geometry Group (VGG)Net model was applied for identifying collapsed buildings induced by the 2010 Haiti earthquake using pre- and post-event remotely sensed space imagery, and the fine-tuned pretrained VGGNet model was compared with the VGGNet model trained from scratch. The effects of dataset augmentation and freezing different intermediate layers were also explored. The experimental results demonstrated that the fine-tuned VGGNet model outperformed the VGGNet model trained from scratch with increasing overall accuracy (OA) from 83.38% to 85.19% and Kappa from 60.69% to 67.14%. By taking advantage of dataset augmentation, OA and Kappa went up to 88.83% and 75.33% respectively, and the collapsed buildings were better recognized with a larger producer accuracy of 86.31%. The present study showed the potential of using the pretrained Convolutional Neural Network (CNN) model to identify collapsed buildings caused by earthquakes using very high-resolution satellite imagery.

scholarly journals Geomorpho90m - Global high-resolution geomorphometry layers: empirical evaluation and accuracy assessment

2019 ◽  
Author(s):  
Giuseppe Amatulli ◽  
Daniel McInerney ◽  
Tushar Sethi ◽  
Peter Strobl ◽  
Sami Domisch
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Empirical Evaluation ◽  
Shuttle Radar Topography Mission ◽  
The United States ◽  
Rate Of Change ◽  
Environmental Models ◽  
Digital Elevation ◽  
The Difference ◽  
Elevation Model

Topographical relief is composed of the vertical and horizontal variations of the Earth's terrain and drives processes in geography, climatology, hydrology, and ecology. Its assessment and characterisation is fundamental for various types of modelling and simulation analyses. In this regard, the Multi-Error-Removed Improved Terrain (MERIT) Digital Elevation Model (DEM) is the best global, high-resolution DEM currently available at a 3 arc-seconds (90 m) resolution. This is an improved product as multiple error components have been corrected from the underlying Shuttle Radar Topography Mission (SRTM3) and ALOS World 3D - 30 m (AW3D30) DEMs. To depict topographical variations worldwide, we developed the Geomorpho90m dataset comprising of different geomorphometry features derived from the MERIT-DEM. The fully standardised geomorphometry variables consist of layers that describe (i) the rate of change using the first and second order derivatives, (ii) the ruggedness, and (iii) the geomorphology landform. To assess how remaining artefacts in the MERIT-DEM could affect the derived topographic variables, we compared our results with the same variables generated using the 3D Elevation Program (3DEP) DEM, which is the highest quality DEM for the United States of America. We compared the two data sources by calculating the first order derivative (i.e., the rate of change through space measured in degrees) of the difference between a MERIT-derived vs. a 3DEP-derived topographic variable. All newly-created topographic variables are readily available at resolutions of 3 and 7.5 arc-seconds under the WGS84 geographic system, and at a spatial resolution of 100 m under the Equi7 projection. The newly-developed Geomorpho90m dataset provides a globally standardised dataset for environmental models and analyses in the field of geography, geology, hydrology, ecology and biogeography.

A Comprehensive Analysis of Building Damage in the 12 January 2010 Mw7 Haiti Earthquake Using High-Resolution Satelliteand Aerial Imagery

2011 ◽  
Vol 77 (10) ◽  
pp. 997-1009 ◽  
Author(s):  
Christina Corbane ◽  
Keiko Saito ◽  
Luca Dell’Oro ◽  
Einar Bjorgo ◽  
Stuart P.D. Gill ◽  
...  
Keyword(s):  
High Resolution ◽  
Building Damage ◽  
Comprehensive Analysis ◽  
Aerial Imagery ◽  
Haiti Earthquake
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