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Few-Shot Learning for Post-Earthquake Urban Damage Detection
2021
Remote Sensing
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data insufficiency and imbalance in post-earthquake urban damage classification. ...
Taking into consideration the recent developments in the fields of Machine Learning and Computer Vision, this study investigates and employs several Few-Shot Learning (FSL) strategies in order to address ...
remote sensing
Article
Few-Shot Learning for Post-Earthquake Urban
Damage Detection
Eftychia Koukouraki *,† , Leonardo Vanneschi and Marco Painho ...
doi:10.3390/rs14010040
fatcat:jeombwrohzfjpiserludgp2lti
A Two-Stage Seismic Damage Assessment Method for Small, Dense, and Imbalanced Buildings in Remote Sensing Images
2022
Remote Sensing
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This study developed a machine-learning-derived two-stage method for post-earthquake building location and damage assessment considering the data characteristics of satellite remote sensing (SRS) optical ...
Results show that the average location accuracy of post-earthquake buildings exceeds 95.7% and that the binary classification accuracy for damage assessment reaches 97.1%. ...
Under the actual scenarios of investigated urban areas, most of the buildings in the SAR images are in normal condition, while only a few are damaged (which are actually concerned by post-earthquake disaster ...
doi:10.3390/rs14041012
fatcat:iqzwazohlbeevmvsz4l45sd5iy
Detecting urban changes using phase correlation and ℓ1-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami
2020
Remote Sensing of Environment
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Among these applications, the identification of damaged infrastructures in urban areas due to a large-scale disaster is a task that is crucial for distributing relief, quantifying losses, and rescue purposes ...
An empirical evaluation consisting of identifying the changes between pre-event and post-event images corresponding to the 2018 Sulawesi Indonesia earthquake-tsunami was performed for this purpose. ...
Acknowledgment This study was partly funded by the Japan Science and Technology Agency (JST) J-Rapid project number JPMJJR1803; the JST CREST project number JP-MJCR1411; the Japan Society for the Promotion ...
doi:10.1016/j.rse.2020.111743
fatcat:nkh7pumnlfd27c2nfladn7afde
Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
2021
Applied Sciences
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Specifically, the best model for hazard-type prediction has an overall accuracy (OA) of 90.1%, and the best damage-level classification model has an explainable OA of 62.6%, upon which both models adopt ...
In this paper, the authors investigate the problem of disaster-scene understanding through a deep-learning approach. Two attributes of images are concerned, including hazard types and damage levels. ...
For example, a post-tsunami image often contains inundation marks or waterrelated textures, whereas the post-earthquake images usually show conspicuous cracking in buildings or cluttered debris. ...
doi:10.3390/app11093952
fatcat:dxzn3d37xrcuhiwdmfigm4fk6y
UAV-Based Structural Damage Mapping: A Review
2019
ISPRS International Journal of Geo-Information
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Structural disaster damage detection and characterization is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and ...
approaches, and finally to studies using advanced deep learning approaches, as well as multi-temporal and multi-perspective imagery to provide comprehensive damage descriptions. ...
Acknowledgments: We thank Pictometry, Inc. for providing the Haiti and Italy imagery used in this study, and the DigitalGlobe Foundation (www.digitalglobefoundation.com) for providing satellite images ...
doi:10.3390/ijgi9010014
fatcat:3zf6fl5sfjezhgsflfpmto4suq
Detecting natural disasters, damage, and incidents in the wild
[article]
2020
arXiv
pre-print
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However, no large-scale image datasets for incident detection exists. ...
Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. ...
Some studies have also applied transfer learning [71] and few-shot learning [57] to deal with unseen situations in emergent disasters. Incident detection on social media. ...
arXiv:2008.09188v1
fatcat:v6bzvop4mfb6fpd5dhrt4beyci
Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery
[article]
2018
arXiv
pre-print
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To enable the comparison of methods for automatic detection of damaged buildings from post-hurricane remote sensing imagery taken from both airborne and satellite sensors, this paper presents the development ...
The proposed approach can be used to build other hurricane-damaged building datasets on which researchers can train and test object detection models to automatically identify damaged buildings. ...
ACKNOWLEDGMENTS The authors would like to thank the eScience Institute for the support of this project through the Data Science for Social Good program at the University of Washington. ...
arXiv:1812.05581v1
fatcat:svz5e2hslbgsxa2qtcwzc6kpqy
UAV STRATEGIES VALIDATION AND REMOTE SENSING DATA FOR DAMAGE ASSESSMENT IN POST-DISASTER SCENARIOS
2018
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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differently accessible and complex environments, as in urban contexts and damaged built heritage. ...
The subjectivity of the operator involvement is still an open issue, both in the first fieldwork assessment, and in the following operational approach of interpretative damage detection and rapid mapping ...
.), for the earthquake task force organization and for kindly providing the technical and financial support for the personnel involved in the reconnaissance. ...
doi:10.5194/isprs-archives-xlii-3-w4-121-2018
fatcat:syamzlq5p5aypnia6sq2chhelu
Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study
2020
Advanced Engineering Informatics
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In that respect, there are different methods proposed for location extraction which cover different fields such as statistics, machine learning, etc. ...
The study aims to provide a quick response to earthquakes by applying the aforementioned techniques. ...
on status of post disaster 1 A drone shot of some more damage from the massive earthquake in Alaska AlaskaEarthquake −1 KTVA newsroom felt the blow of the earthquake this morning anchorage alaska earthquake ...
doi:10.1016/j.aei.2020.101151
fatcat:fosuam25ojezppx5cvxnpztpki
Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment
2020
Drones
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for post-event structural damage assessment. ...
This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. ...
These methods included a Single Shot MultiBox Detector (SSD), post-earthquake multiple scene recognition (PEMSR) based on transfer learning from SSD, and Histogram of Oriented Gradient along with Support ...
doi:10.3390/drones4020024
fatcat:3qfykcqwibella4zaycdn7g5lm
Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
2022
Mathematics
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Then, pre- and post-disaster images are delivered to the network separately for damage assessment. ...
Based on pairs of pre- and post-disaster remote sensing images, effective building damage level assessment can be conducted. ...
In [16] , a two-stage framework is proposed for damage detection from both pre-disaster and post-disaster images. ...
doi:10.3390/math10111898
fatcat:pcfld4khxvaovihhfiofmzeqpm
On humanitarian logistics education and training
2013
Journal of Humanitarian Logistics and Supply Chain Management
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The VO allows for a "snap shot" of the resources being mobilised and the potential gaps in services. ...
+15 minutes The World is alerted Around the globe, sensor systems detected the earthquake as it happened, recording the location of the epicentre and its severity or magnitude. ...
As we experienced at the earthquakes in the decade of the 2000s, building national, local and community capacity is critical for effective response to earthquakes and collapsed structure emergencies in ...
doi:10.1108/jhlscm-05-2013-0018
fatcat:bbhjgxa3u5e3hlvc5zhpbvousm
xView: Objects in Context in Overhead Imagery
[article]
2018
arXiv
pre-print
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We introduce a new large-scale dataset for the advancement of object detection techniques and overhead object detection research. ...
We utilize a novel process for geospatial category detection and bounding box annotation with three stages of quality control. ...
The performance of recent few-shot learning techniques [17, 28] has typically been evaluated on "K-shot, N-way" datasets which assume even distributions of instances over categories [18] . ...
arXiv:1802.07856v1
fatcat:z2bf4uggrfbqfbhwhsoakdei3a
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence
[article]
2020
arXiv
pre-print
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An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research. ...
Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings ...
For instance, Turker and San 2004 analyze post-earthquake aerial images to detect damaged infrastructure caused by the August 1999 Izmit earthquake in Turkey. ...
arXiv:2004.06675v1
fatcat:spf2ommcfzfwpjnhglmwfb3plm
2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, JSTARS 2021 1447-1457 Research Progress on Few-Shot Learning for Remote Sensing Image Inter-pretation. ...
., +, JSTARS 2021 3361-3372 Using 3-D Convolution and Multimodal Architecture for Earthquake Damage Detection Based on Satellite Imagery and Digital Urban Data. ...
doi:10.1109/jstars.2022.3143012
fatcat:dnetkulbyvdyne7zxlblmek2qy
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