The University of Tokyo · Engineering
Professor Xuan Song's research lab specializes in disaster resilience and human mobility modeling, focusing on leveraging big data and artificial intelligence to understand and predict human behavior during natural disasters. The lab integrates GPS trajectory data, remote sensing imagery, and deep learning techniques to advance landslide susceptibility mapping, emergency evacuation pattern analysis, and urban transportation simulation. Key research directions include intelligent disaster response systems, large-scale human mobility prediction, and AI-driven infrastructure monitoring, particularly in the context of earthquakes and nuclear accidents in Japan. The lab also develops advanced deep learning models for industrial applications such as steel surface defect detection.
Figures are computed from collected data and may differ slightly.
This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be
The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, accurately predicting human emergency behavior and their mobility will become the critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. In this paper, we build up a large human mobility database (GPS records of 1.6 million users over one year) and
Traffic congestion causes huge economic loss worldwide in every year due to wasted fuel, excessive air pollution, lost time, and reduced productivity. Understanding how humans move and select the transportation mode throughout a large-scale transportation network is vital for urban congestion prediction and transportation scheduling. In this study, we collect big and heterogeneous data (e.g., GPS records and transportation network data), and we build an intelligent system, namely DeepTransport,
The Great East Japan Earthquake and the Fukushima nuclear accident cause large human population movements and evacuations. Understanding and predicting these movements is critical for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. In this paper, we construct a large human mobility database that stores and manages GPS records from mobile devices used by approximately 1.6 million people throughout Japan from 1 August 2010 to 31 July 2011. By min
To improve the accuracy of steel surface defect detection, an improved model of multi-directional optimization based on the YOLOv8 algorithm was proposed in this study. First, we innovate the CSP Bottleneck with the two convolutions (C2F) module in YOLOv8 by introducing deformable convolution (DCN) technology to enhance the learning and expression ability of complex texture and irregular shape defect features. Secondly, the advanced Bidirectional Feature Pyramid Network (BiFPN) structure is adop
In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale hum
The frequency and intensity of natural disasters has increased significantly in recent decades, and this trend is expected to continue. Hence, understanding and predicting human evacuation behavior and mobility will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, existing models are shallow models, and it is difficult to apply them for understanding the “deep knowledge” of human mobility. Therefore, in this study,
Recent decades have seen a significant increase in the frequency, intensity, and impact of natural disasters and other emergencies, forcing the governments around the world to make emergency response and disaster management national priorities. The growth of extremely large and complex datasets—commonly referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">big data</i> —and various advances in information and communications technology an
By mining "big GPS records" of 1.6 million users, an intelligent system automatically discovers, analyzes, and simulates population evacuations during the Great East Japan Earthquake and the Fukushima Daiichi nuclear accident.
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