Masahiko Nagai

UAV-Borne 3-D Mapping System by Multisensor Integration

To represent 3-D space in detail, it is necessary to acquire 3-D shapes and textures simultaneously and efficiently through the use of precise trajectories of sensors. However, there is no reliable, quick, cheap, and handy method for acquiring accurate high-resolution 3-D data on objects in outdoor and moving environments. In this paper, we propose a combination of charge-coupled device cameras, a small and inexpensive laser scanner, an inexpensive inertial measurement unit, and Global Positioning System for a UAV-borne 3-D mapping system. Direct georeferencing is achieved automatically using all of the sensors without any ground control points. A new method of direct georeferencing by the combination of bundle block adjustment and Kalman filtering is proposed. This allows objects to be rendered richly in shape and detailed texture automatically via a UAV from low altitude. This mapping system has been experimentally used in recovery efforts after natural disasters such as landslides, as well as in applications such as river monitoring.

Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management

Due to the fact that geospatial information technology is considered necessary for disaster risk management (DRM), the need for more effective collaborations between providers and end users in data delivery is increasing. This paper reviews the following: (i) schemes of disaster risk management and collaborative data operation in DRM; (ii) geospatial information technology in terms of applications to the schemes reviewed; and (iii) ongoing practices of collaborative data delivery with the schemes reviewed. This paper concludes by discussing the future of collaborative data delivery and the progress of the technologies.

Fusion of Multi-Temporal Interferometric Coherence and Optical Image Data for the 2016 Kumamoto Earthquake Damage Assessment

Earthquakes are one of the most devastating types of natural disasters, and happen with little to no warning. This study combined Landsat-8 and interferometric ALOS-2 coherence data without training area techniques by classifying the remote sensing ratios of specific features for damage assessment. Waterbodies and highly vegetated areas were extracted by the modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI), respectively, from after-earthquake images in order to improve the accuracy of damage maps. Urban areas were classified from pre-event interferometric coherence data. The affected areas from the earthquake were detected with the normalized difference (ND) between the pre- and co-event interferometric coherence. The results presented three damage types; namely, damage to buildings caused by ground motion, liquefaction, and landslides. The overall accuracy (94%) of the confusion matrix was excellent. Results for urban areas were divided into three damage levels (e.g., none–slight, slight–heavy, heavy–destructive) at a high (90%) overall accuracy level. Moreover, data on buildings damaged by liquefaction and landslides were in good agreement with field survey information. Overall, this study illustrates an effective damage assessment mapping approach that can support post-earthquake management activities for future events, especially in areas where geographical data are sparse.

A Framework of Collective Intelligence for Building Virtual Agriculture Knowledge Repository and Services

Providing information and knowledge services with collecting and maintaining weakly structured text sources is time-consuming activities. This project targets for building specific services and knowledge infrastructures to support decision-making and problem solving in Agriculture domains. It is composed of three essential components: iExtraction -- a tool that semantically extracts relevant information from textual representation sources embedded in each individual and related web site and transforms them into structured and standard format, iGrid -- a framework that supports virtual knowledge repository as well as discovers and integrates structured information distributed over different sources. Semantic Media Wiki is, then, applied to register and update of agricultural information as a semantic network dictionary. This constructed agricultural information is used for the reference information for interoperability for specialist and farmers, iVisualization -- a sophisticated tool that visually presents information in a specific semantic network model, called PMM: Problem-huMan-Method model. Moreover, in order to invite contributions from the user community in order to share the knowledge both tacit and explicit knowledge without the language barrier, it is necessary to provide more sophisticated tools and systems, such as reverse dictionary, ontology-based knowledge sharing and machine-aided translation for sustainable development of agricultural knowledge virtual repository and services .This collaboration project is currently implemented by using Rice domain as a case study. The generated PMM consists of Rice Disease Problems identification, rice huMan experts who could solve that disease problem and the Method for solving the disease problem both in corrective and preventive ways.

Large Scale Mobility Analysis: Extracting Significant Places Using Hadoop/Hive and Spatial Processing

We describe extracting people significant places for mobility analysis from a real-world large scale dataset collected by mobile operator. The total data consisted of 9.2 billion GPS points including approximately 1.5 million individual user trajectories accumulated for a year. We conducted the experiments on the dataset by using stay point extraction and density based cluster to extracting significant places from a sparse dataset. We also proposed an approach to derive types of locations especially home and work place by using classification features and inference model. The relevant features including ranking in clusters, number of days that data appeared, night time, and day time were identified and evaluated. Several inference models are evaluated in the experiment. With limited number of ground truth data, Random Forest model could achieve 99.2% accuracy for inferring home and work location. Additionally, Spatial Population Census were employed to indirectly compare the classification results with ground truth. Furthermore, to enable real-world application, we presented a technique to utilize Hadoop/Hive, a cloud computing platform, allowing full-scale data processing. As a result, the proposed method is able to discover home and work locations of users with positive results after checking the census. In addition, by using Hadoop platform, an extraction process is able to perform on the whole dataset with only about 1.53 days compared with a single application which took 32.73 days.

Application of 3D laser mapping and HEM using UAV for the landslide investigation of the Mid Niigata Prefecture Earthquake

OYO corp. conducted a landslide survey for the Niigata Chuetsu Earthquake by using airborne EM sensor slung by unmanned Aerial Vehicles (UAV) Helicopter. Acquired raw data was processed through 3D laser and CCD mapping. A drilling was followed after the survey to have data cross matching. The survey area is of 200 × 200 meter and characterized with steep slopes of about 70 degrees created by the earthquake. Due to this topography the survey area rejects any ground survey, hence OYO chooses airborne survey and successfully acquired target data set, i.e. receptivity distribution, geographical features, actual image of ground surface. The drilling confirms many colluvial deposits are on the base of mudstone body. With cross reference to 3D laser and CCD mapping it was presumed that the exact boundary plane corresponds to landslide plane. This coincides to the EM results that indicate high sensitivity layer is on the low resistively layer and the boundary depth is the same as that of the one confirmed by drilling. Thus we conclude that the airborne EM with 3D laser and CCD mapping successfully acquire underground 3D structure of survey area and proved the application is effective to analyze the landslide mechanism.