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Tohoku University岡谷貴之課

Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI).

Building-damage mapping using remote sensing images plays a critical role in providing quick and accurate information for the first responders after major earthquakes. In recent years, there has been an increasing interest in generating post-earthquake building-damage maps automatically using different artificial intelligence (AI)-based frameworks. These frameworks in this domain are promising, yet not reliable for several reasons, including but not limited to the site-specific design of the methods, the lack of transparency in the AI-model, the lack of quality in the labelled image, and the use of irrelevant descriptor features in building the AI-model. Using explainable AI (XAI) can lead us to gain insight into identifying these limitations and therefore, to modify the training dataset and the model accordingly. This paper proposes the use of SHAP (Shapley additive explanation) to interpret the outputs of a multilayer perceptron (MLP)—a machine learning model—and analyse the impact of each feature descriptor included in the model for building-damage assessment to examine the reliability of the model. In this study, a post-event satellite image from the 2018 Palu earthquake was used. The results show that MLP can classify the collapsed and non-collapsed buildings with an overall accuracy of 84% after removing the redundant features. Further, spectral features are found to be more important than texture features in distinguishing the collapsed and non-collapsed buildings. Finally, we argue that constructing an explainable model would help to understand the model’s decision to classify the buildings as collapsed and non-collapsed and open avenues to build a transferable AI model

UAV strategies validation and remote sensing data for damage assessment in post-disaster scenarios

The recent seismic swarms, occurred in Italy since August 2016, outlined the importance of deepen Geomatics researches for the validation of new strategies aimed at rapid-mapping and documenting differently accessible and complex environments, as in urban contexts and damaged built heritage. In the emergency response, the crucial exploitation of technological advances should obtain and efficiently organize high-scale reliable geospatial data for the early warning, impact, and recovery phases. Fulfilling these issues, among others, the Copernicus EMS, has played by now an important role in immediate and extensive damage reconnaissance, as in the case of Centre Italy. Nevertheless, the use of remote sensing data is still affected by a problem of point-of-view, scale and detectable detail. Nadir images, airborne or satellite, in fact, strongly limited the confidence level of these products. 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 production. To overcome these limits, the introduction of UAV platforms for photogrammetric purposes, has proven to be a sustainable approach in terms of time savings, operators’ safety, reliability and accuracy of results: the nadir and oblique integration can provide large multiscale models, with the fundamental information related to the façades conditions. The presented research, conducted within the Central Italy earthquakes events, will focus on potentialities and limits of UAV photogrammetry in the two documented sites: Pescara del Tronto and Accumoli. Here, the aim is not limited to describe a series of strategies for georeferencing, blocks orientation and multitemporal co-registration solutions, but also to validate the implemented pipelines as a workflow that could be integrated in the operative intervention for emergency response in early impact activities. Thus, it would be possible to use this 3D metric products as a reference-data for significative improvements of reliability in typical visual inspection and mapping, flanking the traditional nadir airborne- or satellite-based products. The UAV acquisitions performed in two damaged villages are displayed, in order to underline the implication of the spatial information embedded in DSM reconstruction and 3D models, supporting more reliable damage assessments

Damage Detection caused by Natural Disaster using Image Processing Technique

Earthquakes are among the foremost grievous geological disaster that may cause loss of life and property harm. One of the censorious issues after earthquakes or natural hazard is damage detection. Damage recognition of destructed area by human being eyes not to be so effective since it takes longer time to grasp damage by inspection patrolling. But satellite images as well as image processing technique play crucial role in detecting damage because of their easy and rapid processing capacities. Now those more and diverse types of remote sensing and satellite data become accessible and various methods are applied for better damage detection. This work includes a comprehensive review of this method one type of damages- linear type damage in which changes to be evaluated in- between pre and post event data. DOI: 10.17762/ijritcc2321-8169.160411

Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset

The case of the 2005 Kashmir earthquake

The use of Very High Resolution (VHR) satellite panchromatic image is nowadays an effective tool to detect and investigate surface effects of natural disasters. We specifically examined the capabilities of VHR images to analyse earthquake features and detect changes based on the combination of visual inspection and automatic classification tools. In particular, we have used Quickbird (0.6m spatial resolution) images for detecting the three main coseismic surface features: damages, ruptures and landslides. The present approach has been applied to the 8 October 2005, Mw7.6 Kashmir, Pakistan, earthquake. We have focused our study in and around the main urban areas hit by the above earthquake specifically at Muzaffarabad and Balakot towns. The automatic classification techniques provided the best results wherever dealing with the damage to man-made structures and landslides. On the other hand, the visual inspection method demonstrated in addressing the identification of rupture traces and associated features. The synoptic view (concerning landslide, more than 190 millions of pixels have been automatically classified), the spatiotemporal sampling and the fast automatic damage detection using satellite images provided a reliable contribution to the prompt response during natural disaster and for the evaluation of seismic hazard as well

Optimization of Three-Dimensional (3D) Multi-Sensor Models For Damage Assessment in Emergency Context: Rapid Mapping Experiences in the 2016 Italian Earthquake

Geomatics techniques offer the chance to manage very cost-effective solutions for three-dimensional (3D) modelling, from both the aerial and terrestrial point of view, with the help of range and image-based sensors. 3D spatial data that is based on integrated documentation techniques, featured by a very high-scale and an accurate metric and radiometric information nowadays are proposed here as metric databases that are applicable for assisting the operative fieldwork in the case of rapid mapping strategies. In sudden emergency contexts for damage and risk assessment, the structural consolidation and the security measures operations meet the problem of the danger and accessibility constraints of areas, for the operators, as well as to the tight deadlines needs in first aid. The use of Unmanned Aerial Vehicles (UAVs) equipped with cameras are more and more involved in aerial survey and reconnaissance missions; at the same time, the ZEB1 portable Light Detection and Ranging (LiDAR) mapping solution implemented in handle tools helped by Simultaneous Localization And Mapping (SLAM) algorithms can help for a quick preliminary survey. Both of these approaches that are presented here in the critical context of a post-seismic event, which is Pescara del Tronto (AP), deeply affected by the 2016-2017 earthquake in Central Italy. The Geomatics research group and the Disaster Recovery team (DIRECT—http://areeweb.polito.it/direct/) is working in collaboration with the Remotely Piloted Aircraft Systems (RPAS) group of the Italian Firefighter

Review article: The use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management

The number of scientific studies that consider possible applications of remotely piloted aircraft systems (RPASs) for the management of natural hazards effects and the identification of occurred damages strongly increased in the last decade. Nowadays, in the scientific community, the use of these systems is not a novelty, but a deeper analysis of the literature shows a lack of codified complex methodologies that can be used not only for scientific experiments but also for normal codified emergency operations. RPASs can acquire on-demand ultra-high-resolution images that can be used for the identification of active processes such as landslides or volcanic activities but can also define the effects of earthquakes, wildfires and floods. In this paper, we present a review of published literature that describes experimental methodologies developed for the study and monitoring of natural hazard

Towards Automated Analysis of Urban Infrastructure after Natural Disasters using Remote Sensing

Natural disasters, such as earthquakes and hurricanes, are an unpreventable component of the complex and changing environment we live in. Continued research and advancement in disaster mitigation through prediction of and preparation for impacts have undoubtedly saved many lives and prevented significant amounts of damage, but it is inevitable that some events will cause destruction and loss of life due to their sheer magnitude and proximity to built-up areas. Consequently, development of effective and efficient disaster response methodologies is a research topic of great interest. A successful emergency response is dependent on a comprehensive understanding of the scenario at hand. It is crucial to assess the state of the infrastructure and transportation network, so that resources can be allocated efficiently. Obstructions to the roadways are one of the biggest inhibitors to effective emergency response. To this end, airborne and satellite remote sensing platforms have been used extensively to collect overhead imagery and other types of data in the event of a natural disaster. The ability of these platforms to rapidly probe large areas is ideal in a situation where a timely response could result in saving lives. Typically, imagery is delivered to emergency management officials who then visually inspect it to determine where roads are obstructed and buildings have collapsed. Manual interpretation of imagery is a slow process and is limited by the quality of the imagery and what the human eye can perceive. In order to overcome the time and resource limitations of manual interpretation, this dissertation inves- tigated the feasibility of performing fully automated post-disaster analysis of roadways and buildings using airborne remote sensing data. First, a novel algorithm for detecting roadway debris piles from airborne light detection and ranging (lidar) point clouds and estimating their volumes is presented. Next, a method for detecting roadway flooding in aerial imagery and estimating the depth of the water using digital elevation models (DEMs) is introduced. Finally, a technique for assessing building damage from airborne lidar point clouds is presented. All three methods are demonstrated using remotely sensed data that were collected in the wake of recent natural disasters. The research presented in this dissertation builds a case for the use of automatic, algorithmic analysis of road networks and buildings after a disaster. By reducing the latency between the disaster and the delivery of damage maps needed to make executive decisions about resource allocation and performing search and rescue missions, significant loss reductions could be achieved

Automatic Recognition of Seismic Intensity Based on RS and GIS: A Case Study in Wenchuan Ms8.0 Earthquake of China

In recent years, earthquakes have frequently occurred all over the world, which caused huge casualties and economic losses. It is very necessary and urgent to obtain the seismic intensity map timely so as to master the distribution of the disaster and provide supports for quick earthquake relief. Compared with traditional methods of drawing seismic intensity map, which require many investigations in the field of earthquake area or are too dependent on the empirical formulas, spatial information technologies such as Remote Sensing (RS) and Geographical Information System (GIS) can provide fast and economical way to automatically recognize the seismic intensity. With the integrated application of RS and GIS, this paper proposes a RS/GIS-based approach for automatic recognition of seismic intensity, in which RS is used to retrieve and extract the information on damages caused by earthquake, and GIS is applied to manage and display the data of seismic intensity. The case study in Wenchuan Ms8.0 earthquake in China shows that the information on seismic intensity can be automatically extracted from remotely sensed images as quickly as possible after earthquake occurrence, and the Digital Intensity Model (DIM) can be used to visually query and display the distribution of seismic intensity