Semi-supervised learning for improved post-disaster damage assessment from satellite imagery

2021 ◽  
Author(s):  
Victor Oludare ◽  
Landry Kezebou ◽  
Karen Panetta ◽  
Sos S. Agaian
Keyword(s):  
Supervised Learning ◽  
Satellite Imagery ◽  
Damage Assessment ◽  
Post Disaster

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

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.

scholarly journals Comparison of Pre-Event VHR Optical Data and Post-Event PolSAR Data to Investigate Damage Caused by the 2011 Japan Tsunami in Built-Up Areas

2018 ◽  
Vol 10 (11) ◽  
pp. 1804 ◽  
Author(s):  
Minyoung Jung ◽  
Junho Yeom ◽  
Yongil Kim
Keyword(s):  
Urban Areas ◽  
Damage Assessment ◽  
Optical Data ◽  
Alos Palsar ◽  
Physical Relationship ◽  
Level Indicator ◽  
Damage Level ◽  
2011 Japan Tsunami ◽  
The Difference ◽  
Post Disaster

Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite data is essential for the timely damage investigation because the availability of data in a disaster area is usually limited. This article proposes a novel method to assess damage in urban areas by analyzing combined pre-disaster very high resolution (VHR) optical data and post-disaster polarimetric SAR (PolSAR) data, which has rarely been used in previous research because the two data have extremely different characteristics. To overcome these differences and effectively compare VHR optical data and PolSAR data, a technique to simulate polarization orientation angles (POAs) in built-up areas was developed using building orientations extracted from VHR optical data. The POA is an intrinsic parameter of PolSAR data and has a physical relationship with building orientation. A damage level indicator was also proposed, based on the consideration of diminished homogeneity of POA values by damaged buildings. The indicator is the difference between directional dispersions of the pre and post-disaster POA values. Damage assessment in urban areas was conducted by using the indicator calculated with the simulated pre-disaster POAs from VHR optical data and the derived post-disaster PolSAR POAs. The proposed method was validated on the case study of the 2011 tsunami in Japan using pre-disaster KOMPSAT-2 data and post-disaster ALOS/PALSAR-1 data. The experimental results demonstrated that the proposed method accurately simulated the POAs with a root mean square error (RMSE) value of 2.761° and successfully measured the level of damage in built-up areas. The proposed method can facilitate efficient and fast damage assessment in built-up areas by comparing pre-disaster VHR optical data and post-disaster PolSAR data.

Post-Disaster Urban Recovery Monitoring in Pisco After the 2007 Peru Earthquake Using Satellite Image

2014 ◽  
Vol 9 (6) ◽  
pp. 1059-1068 ◽  
Author(s):  
Tomoyo Hoshi ◽  
◽  
Osamu Murao ◽  
Kunihiko Yoshino ◽  
Fumio Yamazaki ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Urban Areas ◽  
Satellite Image ◽  
Recovery Process ◽  
Visual Interpretation ◽  
Field Surveys ◽  
Recovery Processes ◽  
The City ◽  
Post Disaster

Pisco was the area most damaged by the 2007 Peru earthquake. The purpose of this research is to develop possibilities of using satellite imagery to monitor postdisaster urban recovery processes, focusing on the urban change in Pisco between 2007 and 2011. To this end, the authors carried out field surveys in the city in 2012 and 2013 and also examined previous surveys to determine that building reconstruction peaked between 2008 and 2009. After analyzing the five-year recovery process, the authors compared its reconstruction conditions by visual interpretation with those by image analysis using satellite image. An accuracy of 71.2% was achieved for the visual interpretation results in congested urban areas, and that for developed districts was about 60%. The result shows that satellite imagery can be a useful tool for monitoring and understanding post-disaster urban recovery processes in the areas in which conducting long-term field survey is difficult.

Comparison of Damage Assessment Maps Derived from Very High Spatial Resolution Satellite and Aerial Imagery Produced for the Haiti 2010 Earthquake

2011 ◽  
Vol 27 (1_suppl1) ◽  
pp. 199-218 ◽  
Author(s):  
Christina Corbane ◽  
Daniela Carrion ◽  
Guido Lemoine ◽  
Marco Broglia
Keyword(s):  
Satellite Imagery ◽  
Aerial Photography ◽  
Damage Assessment ◽  
High Spatial Resolution ◽  
Building Damage ◽  
Aerial Imagery ◽  
Rapid Mapping ◽  
Very High Spatial Resolution ◽  
High Level ◽  
Very High

Following the devastating M7.2 earthquake that affected Haiti on 12 January 2010 two types of building damage assessment maps were produced: 1) area-based damage assessments using pre- and post-event satellite imagery and 2) detailed building-by-building damage assessments using post-event aerial photography. In this paper, we compare the reliability and the usability of area-based damage assessment maps from satellite imagery with respect to the detailed damage assessment from aerial data. The main objective is to better understand how cooperative rapid mapping can steer the more detailed assessments that are typical in determining postdisaster recovery and reconstruction efforts. The results of these experiments indicate that damage assessment maps based on satellite data are capable of capturing the damage pattern, mainly in areas with a high level of damaged and many collapsed structures. However, these maps cannot provide the level of information needed for the quantification of damage intensity.

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