Yoshiki Ogawa

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Verified

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• Doctor of Engineering
• Professor (Associate) at The University of Tokyo

Remote sensing image semantic segmentation of land use, benefitted from the development of deep learning and consequently made considerable progress in terms of inferencing accuracy and speed. However, the effective training of semantic segmentation models for remote sensing imagery necessitates extensively detailed pixel-level annotations, and gat...

The built year and structure of individual buildings are crucial factors for estimating and assessing potential earthquake and tsunami damage. Recent advances in sensing and analysis technologies allow the acquisition of high-resolution street view images (SVIs) that present new possibilities for research and development. In this study, we develope...

We present an integrated framework that utilizes high‐resolution seamless simulations of disasters and national economies for estimating the economic impacts of disasters. The framework consists of three components: a physics‐based simulator to simulate the disaster and estimate the response of the infrastructure; a tool that estimates the losses s...

Developing a model to evaluate urban streetscapes based on subjective perceptions is important for quantitative understanding. However, previous studies have only considered limited types of subjective perceptions, neglecting the relationships between them. Further, accurately measuring subjective perception with low computational costs for large-s...

Deep learning-based building extraction methods have widespread applications in diverse fields. However, the evaluation of large-scale extraction results remains challenging, due to traditional evaluation metrics rely on manually created ground-truth samples and the lack of comprehensive reference-building data for developing countries. To address...

For accurate urban planning, three-dimensional (3D) building models with a high level of detail (LOD) must be developed. However, most large-scale 3D building models are limited to a low LOD of 1–2, as the creation of higher LOD models requires the modeling of detailed building elements such as walls, windows, doors, and roof shapes. This process i...

Promoting walking is crucial for sustainable development and fosters individual health and well-being. Therefore, comprehensive investigations of factors that make walking attractive are vital. Previous research has linked streetscapes at eye-level to walking preferences, which usually focuses on simple linear relationships, neglecting the complex...

Major disasters affect economies in highly complex ways due to the strong interrelationship of economic entities to each other and their heavy dependence on infrastructures. As an example, damages to the lifeline and transportation networks, accessed by firms and their suppliers and consumers, brought upon by disasters are highly heterogeneous, cau...

Currently, the Ministry of Land, Infrastructure, Transport, and Tourism (Japan) is in the process of developing an open 3D city model known as PLATEAU. Abundant measurement data related to buildings, including maps produced by private companies and mobile mapping system point clouds, have been collected to enhance the value of the 3D city model. To...

The authors used a data-driven reinforcement learning model for the post-disaster rapid recovery of human mobility, considering human-mobility recovery rate, road connectivity, and travel cost as the recovery components, to generate the reward framework. Each component has relative importance with respect to the others. However, if the preference i...

The walkability of an urban environment is a critical aspect of urban design and planning, and has a direct impact on the quality of life for residents. Therefore, it is essential to conduct a systematic evaluation of the pedestrian environment to improve the walkability of a city. In recent years, there has been a growing emphasis on the applicati...

Land price is an important economic factor in producing meaningful references for regional planners by assisting them in urban planning, economic decision-making, and land resource allocation. However, related studies in land price analysis were mainly focused on the factors of site area and plot ratio, analysis of the potential impact of streetsca...

People flow trend estimation is crucial to traffic and urban safety planning and management. However, owing to privacy concerns, the collection of individual location data for people flow statistical analysis is difficult; thus, an alternative approach is urgently needed. Furthermore, the trend in people flow is reflected in streetscape factors, ye...

Building footprint is a primary dataset of an urban geographic information system (GIS) database. Therefore, it is essential to establish a robust and automated framework for large-scale building extraction. However, the characteristic of remote sensing images complicates the application of the instance segmentation method based on the Mask R-CNN m...

In response to the declining population and aging infrastructure in Japan, local governments are implementing compact city policies in the form of location normalization plans. To optimize the reorganization of urban public infrastructure, it is important to provide detailed and accurate forecasts of the distribution of urban populations and househ...

Since around 2017, the Japanese government has been actively considering Evidence-Based Policymaking (EBPM) to make various statistics accurate and to plan and select effective policies with limited budgets and resources. Using big data in planning smart cities has realized its full potential and “smartening” urban governance achieved in specific c...

In the event of a flood, affected firms will experience production stoppages, resulting in supply chain (SC) disruptions and indirect economic damage to firms even outside the affected area. To mitigate the economic damage, it is essential for each firm in the entire SC to acquire a behavioural strategy in line with its recovery status. To achieve...

Building footprints are one of the primary data sources in the urban geographic information system (GIS) database. They are crucial for applications such as urban planning and population estimation. However, high-quality remote sensing imagery is not always available as open-sourced data for a wide area. Therefore, a versatile method was proposed i...

People flow data are important for economic prediction and urban planning development. In this study, we deployed a Swin Transformer on panoramic street view images and constructed a wide-area people flow trend model using image data. First, for people flow trend prediction, we concentrated the flow data of a wide area and counted it into mesh unit...

In this paper, we propose a method for regional analysis using image recognition technology based on deep learning and big data of street images captured by omnidirectional cameras on vehicles. Specifically, we first construct a classification method of regions based on street images using a pretrained deep learning model (VGG16) for image recognit...

In response to the declining population and aging infrastructure in Japan, local governments are implementing compact city policies such as the location normalization plan. To optimize the reorganization of urban public infrastructure, it is important to provide detailed and accurate forecasts of the distribution of urban populations and households...

Record-breaking heavy rain occurred in Western Japan from June 28 to July 8, 2018. Many roads in Hiroshima and Okayama Prefecture were disrupted simultaneously. The government desired the rapid recovery of disrupted human mobility; however, the restoration of roads that were frequently used by citizens was delayed for a week after this flooding. Th...

In recent years, supply chain (SC) disruptions caused by production stoppages at bottleneck firms have been frequent due to disasters. To develop a business continuity plan (BCP) to prevent SC disruptions, it is necessary to identify the bottleneck firms. In this study, we developed a total of 7 models for extracting bottleneck firms using machine...

We present a novel approach for utilizing big data to estimate economic impacts of disasters by performing end-to-end simulations of disasters and economy at 1:1 scale. In order to realize such fine-grained economic simulations, we developed a high-performance computing extension for Agent Based Economic Models (ABEMs) and seamlessly integrated it...

This paper proposes a new method to join building footprint GIS data with the relevant buildings in a street-view image, taken by a vehicle-mounted camera. This is achieved by segmenting buildings in the street-view images and identifying the relevant building coordinates in the image. The building coordinates on the image are then estimated from t...

In urban planning of rural cities, such as Location Normalization Plan, it is important to forecast the detailed population and household distribution in the future. This study aims to develop a household transition model, which is a microsimulation of household units, and estimated household data for 2015 in all cities of Japan. Estimated househol...

From the viewpoint of disaster mitigation planning, there is an urgent need to develop a method to estimate both the structure and built year of a building. In this paper, we develop a model to estimate them using facade images together with attributes from a real estate database. Specifically, images are fed to a convolutional neural network (CNN)...

For the establishment of precise disaster prevention measures in response to the Nankai megathrust earthquakes predicted to occur in the future, it is necessary to conduct numerous earthquake simulations and evaluate the vulnerability of the urban environment quantitatively. This vulnerability is evaluated on the basis of factors such as the extent...

The structure and built-year of a building are significant factors related to the probability of building collapse during a large-scale earthquake. Therefore, they are used as parameters in building collapse probability models based on the results of field surveys. In other words, it is important to grasp the structure and built-year of each buildi...

Considering the 2011 Great East Japan Earthquake, by utilizing GPS based large-scale people flow data, we developed a home-return model considering city variables that can estimate the rate of people who will have returned home on any number of days after an earthquake tsunami disaster. We obtained high accuracy with the sparse logit model in this...

With natural disasters have become large scale, diversified, and frequent, the indirect economic damage due to interruption of supply chain tends to be large. Therefore, it is important to recover as quickly as possible for companies after disasters. In this paper, we use reinforcement learning to optimize a company's action strategy so that it can...

Using various types of big data on the Nankai Trough earthquake and tsunami that struck Kochi, we describe a method of simulating how economic damage to inter-enterprise transactions propagates through the supply chain and how subsequent recovery occurs. First, we enter the human losses and material damage caused by the earthquake and tsunami to es...

In order to enhance the crisis management response using the example of the Nankai Trough Earthquake in Kochi, we developed micro data that can be used to estimate high-definition damage based on big data accumulated with improved observation technology. People flow data generated by interpolating probe data from mobile phones equipped with GPS wit...

This chapter introduces the development flow of a database for evaluation of initial risk and readiness of large-scale earthquakes on 60 million buildings all over Japan to integrate various census data and building scale micro geo data. In addition, we develop the data environment to be able to estimate damage situations by earthquakes in various...

Assessments of the human damage caused by the tsunami are required in order to consider disaster prevention at such a regional level. Hence, there is an increasing need for the assessments of human damage caused by earthquakes. However, damage assessments in japan currently usually rely on static population distribution data, such as statistical ni...

Assessments of the human damage caused by the tsunami are required in order to consider disaster prevention at such a regional level. Hence, there is an increasing need for the assessments of human damage caused by earthquakes. However, damage assessments in japan currently usually rely on static population distribution data, such as statistical ni...