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BDD-Net: A General Protocol for Mapping Buildings Damaged by a Wide Range of Disasters Based on Satellite Imagery
2020
Remote Sensing
Preserved Fulltext
The use of satellite imagery for disaster assessment can overcome this problem. ...
BDD-Net was developed to automatically classify every pixel of a post-disaster image into one of non-damaged building, damaged building, or background classes. ...
Acknowledgments: The authors would like to thank editors and the anonymous reviewers for their insightful suggestions and comments. ...
doi:10.3390/rs12101670
fatcat:ltszpxxs2zbtzbc4xwwynpvrba
Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment
[article]
2017
arXiv
pre-print
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We propose Nazr-CNN1, a deep learning pipeline for object detection and fine-grained classification in images acquired from Unmanned Aerial Vehicles (UAVs) for damage assessment and monitoring. ...
We show one such case of transfer learning to assess the level of damage in aerial images collected after a typhoon in Philippines. ...
DEEP LEARNING FRAMEWORK Since our labeled data set is relatively small (1,085 images), we have built our deep learning pipeline (Nazr-CNN) using existing pre-trained networks as building blocks. ...
arXiv:1611.06474v2
fatcat:jh7lgi62jfgdbkga5mqtvmqrwe
Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery
[article]
2022
arXiv
pre-print
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training data most accurately predicts the level of damage caused. ...
We present a novel methodology of interpretable deep learning that seeks to explicitly investigate the most useful modalities of information in the training data to create an accurate classification model ...
The blurred building of size 64 pixels would not be a useful data point for any deep learning model to learn from and yield accurate results. ...
arXiv:2201.10523v1
fatcat:jxkezulgdrdo3hpdhnf2gwwgee
DEEP LEARNING FOR AUTOMATIC BUILDING DAMAGE ASSESSMENT: APPLICATION IN POST-DISASTER SCENARIOS USING UAV DATA
2021
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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del Tronto village, using DEEP (Digital Engine for Emergency Photo-analysis) a deep learning tool for automatic building footprint segmentation and building damage classification, functional to the rapid ...
Thanks to the use of image-based survey techniques as the main acquisition methods (UAV photogrammetry), damage assessment analysis has been carried out to assess and map the damages that occurred in Pescara ...
Thanks to INGC for allowing the use of the Mozambique image dataset. ...
doi:10.5194/isprs-annals-v-1-2021-113-2021
fatcat:ijxtcyy3evguhchy5i226pbo6a
Deep Learning-Enabled Semantic Inference of Individual Building Damage Magnitude from Satellite Images
2020
Algorithms
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We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. ...
studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. ...
Each building has a ground truth polygon that outlines its location in the image, and a ground truth label that assigns the building a semantic label from the joint damage scale. ...
doi:10.3390/a13080195
fatcat:o3a33wg4jnfadbxap5rbkbabam
RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment
[article]
2022
arXiv
pre-print
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Deep learning based computer vision techniques can help in scene understanding, and help rescue teams with precise damage assessment. ...
Unfortunately, available datasets for natural disaster damage assessment lack detailed annotation of the affected areas, and therefore do not support the deep learning models in total damage assessment ...
His research interest includes Deep Learning, Machine Learning, Semantic Segmentation, Few Shot Learning, Meta Learning, and Bayesian Learning. ...
arXiv:2202.12361v1
fatcat:7hbi7h5xlveqbcos7hoiwnuxki
Earthquake-induced Building Damage Mapping Based on Multi-task Deep Learning Framework
2019
IEEE Access
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In this paper, a multi-task deep learning framework is proposed to map damaged and intact buildings from large-scale very high spatial resolution images. ...
The development in earth observation using high spatial resolution images makes it possible to recognize damaged and intact buildings. ...
The main task is to detect buildings in good shape and damaged and the extra task is to semantically segment images into multiple ground objects. The extra task is used to supplement the main task. ...
doi:10.1109/access.2019.2958983
fatcat:7fihfiatobhrnllese62h2by2m
The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment
2021
Geosciences
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The amount of devastation was so great that it took years to achieve a proper assessment of the economical and structural damage, with the consequences still being felt today. ...
We focus on more than 15 studies that are compared and evaluated in terms of the data they require, the methods used, their degree of automation, their metric performances, and their strengths and weaknesses ...
The authors use both a map of the building footprints to detect lateral shift, and a ground-truth for the damage assessment training. ...
doi:10.3390/geosciences11030133
fatcat:q4e4d73xxff3bcxeiik5byqr7e
BDD-Net: An End-to-End Multiscale Residual CNN for Earthquake-Induced Building Damage Detection
2022
Remote Sensing
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In this study, we explore this fusion potential, which incorporates deep learning. ...
The three main phases are: (1) data preprocessing and building footprint extraction based on building vector maps, (2) sample data preparation and data augmentation, and (3) model optimization and building ...
Ground Truth Data In this research, the sample data was manually collected and divided into two classes: (1) Damage and (2) Non-damage. This sample was used to train BDD-Net. ...
doi:10.3390/rs14092214
fatcat:gmhwax4ohrcqhdj664tfuuxwem
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
[article]
2021
arXiv
pre-print
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With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings. ...
With the powerful ability of feature representation, deep neural networks have been successfully applied to building damage assessment. ...
With a set of labeled data, deep learning-based methods can automatically learn image feature representations from low level to high level, without selecting hand-crafted image features. ...
arXiv:2105.07364v1
fatcat:v52hpkmi35dlzbsrrvwe7vpj6m
Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami
2018
Remote Sensing
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In this article, a deep learning algorithm for the semantic segmentation of high-resolution remote-sensing images using the U-net convolutional network was proposed to map the damage rapidly. ...
damage assessment accuracy and lag in timeliness, which dramatically reduces the significance and feasibility of extending the present method to practical operational applications. ...
The HR images and ground-truth data of building damage were projected into the UTM/WGS84 geo-referenced coordinate system. ...
doi:10.3390/rs10101626
fatcat:t5h73sysfvctfmdxmtxt62xcva
Pyramid Pooling Module-Based Semi-Siamese Network: A Benchmark Model for Assessing Building Damage from xBD Satellite Imagery Datasets
2020
Remote Sensing
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Most mainstream research on assessing building damage using satellite imagery is based on scattered datasets and lacks unified standards and methods to quantify and compare the performance of different ...
Our proposed method achieves F1 scores of 0.90, 0.41, 0.65, and 0.70 for the undamaged, minor-damaged, major-damaged, and destroyed building classes, respectively. ...
We thank the two reviewers for their helpful and constructive comments on our work. The author gratefully acknowledges the support of K.C. Wong Education Foundation, Hong Kong. ...
doi:10.3390/rs12244055
fatcat:undwujdacvd7zb7m4rdj6d6anu
Tsunami Damage Detection with Remote Sensing: A Review
2020
Geosciences
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Recent advances of remote sensing and technologies of image analysis meet the above needs and lead to more rapid and efficient understanding of tsunami affected areas. ...
E.M. is for tsunami damage interpretation, L.M. is for machine learning method, and Y.B. is for deep learning method. All authors have read and agreed to the published version of the manuscript. ...
Funding: This study was partly funded by the JSPS Kakenhi Program (17H06108 and 17H02050) and the Core Research Cluster of Disaster Science at Tohoku University. ...
doi:10.3390/geosciences10050177
fatcat:o6trnuw7efgvjp7zbnh3ev2nqm
AUTOMATED BUILDING SEGMENTATION AND DAMAGE ASSESSMENT FROM SATELLITE IMAGES FOR DISASTER RELIEF
2021
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Critical challenges are addressed, including the detection of clustered buildings with uncommon visual appearances, the classification of damage levels by both humans and deep learning models, and the ...
We show promising building damage assessment results and highlight the strong performance impact of data pre-processing on the generalization capability of deep convolutional models. ...
These data form the basis for our ground truth and allowed a quantitative evaluation of our models on the city of Beria, Mozambique. ...
doi:10.5194/isprs-archives-xliii-b3-2021-741-2021
fatcat:yw7aihv3onetbb5i4mkipsespi
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
2021
IEEE Access
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On the other hand the primary source of the ground-level imageries is social media [30] , these imageries lack geo-location tags [27] and suffers from data scarcity for deep learning training [11] ...
[21] used crowd sourced images from social media which captured disaster sites from the ground level. ...
doi:10.1109/access.2021.3090981
fatcat:wacd4jsapzfuhhu5kjvkl3ab2m
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