Utilizing New Technologies in Managing Hazards and Disasters

Eguchi, Ronald T.; Huyck, Charles; Ghosh, Shubharoop; Adams, Beverley J.; McMillan, Anneley

doi:10.1007/978-90-481-2238-7_15

Cited by 18 publications

(14 citation statements)

References 25 publications

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“…The authors reported that it is more meaningful to perform a fusion of the feature maps coming from each of the resolutions than to have all the multi-resolution imagery share features in a single CNN. Nonetheless, the multi-resolution feature fusion approach was (1) not tested for the airborne (manned and unmanned) resolution levels and (2) not tested for the model transferability when a new region was only considered in the validation step.…”

Section: Cnn Feature Fusion Approaches In Remote Sensingmentioning

confidence: 99%

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Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

et al. 2018

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Remote sensing images have long been preferred to perform building damage assessments. The recently proposed methods to extract damaged regions from remote sensing imagery rely on convolutional neural networks (CNN). The common approach is to train a CNN independently considering each of the different resolution levels (satellite, aerial, and terrestrial) in a binary classification approach. In this regard, an ever-growing amount of multi-resolution imagery are being collected, but the current approaches use one single resolution as their input. The use of up/down-sampled images for training has been reported as beneficial for the image classification accuracy both in the computer vision and remote sensing domains. However, it is still unclear if such multi-resolution information can also be captured from images with different spatial resolutions such as imagery of the satellite and airborne (from both manned and unmanned platforms) resolutions. In this paper, three multi-resolution CNN feature fusion approaches are proposed and tested against two baseline (mono-resolution) methods to perform the image classification of building damages. Overall, the results show better accuracy and localization capabilities when fusing multi-resolution feature maps, specifically when these feature maps are merged and consider feature information from the intermediate layers of each of the resolution level networks. Nonetheless, these multi-resolution feature fusion approaches behaved differently considering each level of resolution. In the satellite and aerial (unmanned) cases, the improvements in the accuracy reached 2% while the accuracy improvements for the airborne (manned) case was marginal. The results were further confirmed by testing the approach for geographical transferability, in which the improvements between the baseline and multi-resolution experiments were overall maintained.

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Section: Cnn Feature Fusion Approaches In Remote Sensingmentioning

confidence: 99%

“…The location of damaged buildings after a disastrous event is of utmost importance for several stages of the disaster management cycle [1,2]. Manual inspection is not efficient since it takes a considerable amount of resources and time.…”

Section: Introductionmentioning

confidence: 99%

Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

et al. 2018

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“…Remote sensing techniques have been increasingly adopted to map disaster areas in recent decades [1][2][3] as witnessed by a rapidly growing amount of research works dealing with optical [4][5][6], radar [7,8], and laser [9,10] data with each sensor giving different advantages. Except for subtle damage patterns that deformation-sensitive radar can identify particularly well, optical data are usually preferrable to detect building damage [11] in the early hours after the event as images are more easily interpretable by human operators who still typically carry out the initial damage map production manually [1].…”

Section: Introductionmentioning

confidence: 99%

Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

et al. 2019

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The timely and efficient generation of detailed damage maps is of fundamental importance following disaster events to speed up first responders’ (FR) rescue activities and help trapped victims. Several works dealing with the automated detection of building damages have been published in the last decade. The increasingly widespread availability of inexpensive UAV platforms has also driven their recent adoption for rescue operations (i.e., search and rescue). Their deployment, however, remains largely limited to visual image inspection by skilled operators, limiting their applicability in time-constrained real conditions. This paper proposes a new solution to autonomously map building damages with a commercial UAV in near real-time. The solution integrates different components that allow the live streaming of the images on a laptop and their processing on the fly. Advanced photogrammetric techniques and deep learning algorithms are combined to deliver a true-orthophoto showing the position of building damages, which are already processed by the time the UAV returns to base. These algorithms have been customized to deliver fast results, fulfilling the near real-time requirements. The complete solution has been tested in different conditions, and received positive feedback by the FR involved in the EU funded project INACHUS. Two realistic pilot tests are described in the paper. The achieved results show the great potential of the presented approach, how close the proposed solution is to FR’ expectations, and where more work is still needed.

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“…Building damage maps have been recurrently used in the response and recovery phase of the disaster management cycle. Damaged buildings may be a proxy for victim localization (Dell'Acqua and Gamba, 2012) and their identification can also aid to plan and delineate recovery activities (Eguchi et al, 2009). Remote sensing has been extensively used to perform the damage assessment of a given region affected by a disastrous event (Dell'Acqua and Gamba, 2012;Dong and Shan, 2013;Gerke and Kerle, 2011;Vetrivel et al, 2017).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Satellite Image Classification of Building Damages Using Airborne and Satellite Image Samples in a Deep Learning Approach

Duarte

Nex

Kerle

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4&thinsp;% the overall satellite image classification of building damages.

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Utilizing New Technologies in Managing Hazards and Disasters

Cited by 18 publications

References 25 publications

Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

Satellite Image Classification of Building Damages Using Airborne and Satellite Image Samples in a Deep Learning Approach

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