| Budget Amount *help |
¥10,790,000 (Direct Cost: ¥8,300,000、Indirect Cost: ¥2,490,000)
Fiscal Year 2020: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2019: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2018: ¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
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| Outline of Final Research Achievements |
3D point cloud data acquisition has been rapidly progressive. The 3D point cloud data is expected to be widely utilized. Dealing with the 3D point cloud data is still challenging due to its massive data and updating strategy. This study developed a data compression method for 3D point cloud data obtained from MMS and aerial lasers scanner, and so on. The study also developed data updating methods, including change detection and geographic feature number/location estimation. The proposed framework was comprehensively evaluated from both compression and updating viewpoints. Accordingly, the proposed methodology for maintaining and updating essential data is shown.
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