Takehiro Kashiyama

Real-time people movement estimation in large disasters from several kinds of mobile phone data

Recently, an understanding of mass movement in urban areas immediately after large disasters, such as the Great East Japan Earthquake (GEJE), has been needed. In particular, mobile phone data is available as time-varying data. However, much more detailed movement that is based on network flow instead of aggregated data is needed for appropriate rescue on a real-time basis. Hence, our research aims to estimate real-time human movement during large disasters from several kinds of mobile phone data. In this paper, we simulate the movement of people in the Tokyo metropolitan area in a large disaster situation and obtain several kinds of fragmentary movement observation data from mobile phones. Our approach is to use data assimilation techniques combining with simulation of population movement and observation data. The experimental results confirm that the improvement in accuracy depends on the observation data quality using sensitivity analysis and data processing speed to satisfy each condition for real-time estimation.

Particle filter for real-time human mobility prediction following unprecedented disaster

Real-time estimation of human mobility following a massive disaster will play a crucial role in disaster relief. Because human mobility in massive disasters is quite different from their usual mobility, real-time human location data is necessary for precise estimation. Due to privacy concerns, real-time data is anonymized and a popular form of anonymization is population distribution. In this paper, we aim to estimate human mobility following an unprecedented disaster using such population distribution data. To overcome technical obstacles including high dimensionality, we propose novel particle filter by devising proposal distribution. Our proposal distribution provides states considering both prediction model and acquired observation. Therefore, particles maintain high likelihood. In the experiments, our methods realized more accurate estimation than the baselines, and its estimated mobility was consistent with the survey researches. The computational cost is significantly low enough for real-time operations. The GPS data collected on the day of the Great East Japan Earthquake is used for the evaluation.

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images: Road damage detection and classification

Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only focus on the detection of the presence or absence of damage. However, in a real-world scenario, when the road managers from a governing body need to repair such damage, they need to clearly understand the type of damage in order to take effective action. In addition, in many of these previous studies, the researchers acquire their own data using different methods. Hence, there is no uniform road damage dataset available openly, leading to the absence of a benchmark for road damage detection. This study makes three contributions to address these issues. First, to the best of our knowledge, for the first time, a large-scale road damage dataset is prepared. This dataset is composed of 9,053 road damage images captured with a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. In order to generate this dataset, we cooperated with 7 municipalities in Japan and acquired road images for more than 40 hours. These images were captured in a wide variety of weather and illuminance conditions. In each image, we annotated the bounding box representing the location and type of damage. Next, we used a state-of-the-art object detection method using convolutional neural networks to train the damage detection model with our dataset, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage dataset, our experimental results, and the developed smartphone application used in this study are publicly available (this https URL).

Extraction of Road Maintenance Criteria using Machine Learning and Spatial Information

Infrastructure maintenance requires extensive financial and human resources, and a lack of these resources---and, in particular, a shortage of experts---is a problem in many countries and regions around the world. In response to such circumstances, there is considerable research on infrastructure damage-detection methods using camera images and machine-learning. However, even if a large number of damaged parts are found using such methods, the decision whether to repair damaged areas is nevertheless determined empirically, by taking into account several factors such as road statistics and the regional characteristics. For these reasons, the current situation is that municipalities that lack experts cannot make comprehensive decisions regarding repairs. Therefore, in this research, we extracted maintenance management standards and automated decision-making using the decisions made by local government officials regarding damaged roads in Japan. We focused on roads, because roads are considered to be one of the most influential infrastructure. In order to do so, we cooperated with six municipalities in Japan. We combined statistical information regarding damaged roads with regional characteristics. As a result, in a very understandable way, we were then able to reproduce the decisions made by experts with an accuracy of 0.75. Our research has the potential to enable automated decision-making in the future.