reflection problems. Thus, we apply a randomized algorithm for quickly finding an approximate nearest-neighbor matches
between image patches Barnes et al. 2009, as shown in Figure 6.
Figure 5. DEM generation from terrestrial LiDAR data
Figure 6. PatchMatch processing for ortho image generation
3. EXPERIMENT
We conducted experiments involving the daily and annual Sabo infrastructure inspection work in a sediment-retarding basin
consisting of dikes, bridges, and debris barriers in Fukushima, Japan see Figure 7.
Figure 7. Study area
3.1 Attribute data acquisition
In attribute data acquisition, we record conditions of infrastructures, such as cracks, damages and displacements,
based on checklists distributed by Japanese Ministry of Land, Infrastructure, Transport and Tourism MLIT. We assigned
these checklists to meta-data and main data, as shown in Figure 8. Then, we input text data and images to record the conditions
of infrastructures with some mobile devices, as shown in Figure 9.
Figure 8. Checklists in structure inspection based on MLIT’s guidelines
Figure 9. Mobile devices tablet PCs and smart phone In addition, omni-directional images are also acquired to record
attribute data of the conditions of infrastructures. These images are used to improve the integrity in infrastructure inspection
with augmented reality applications in office works. We used two types of cameras, such as THETA m15 RICOH
and QBiC PANORAMA Elmo. These cameras were mounted on a monopod, as shown in Figure 10 and Figure 11. We also
used a GPS logger N-241, HOLUX to get position data with omni-directional images.
Acquired omni-directional images were stitched to be panoramic images and movies. These images and movies are
viewed with a head-mount display Oculus Rift, as shown in Figure 12.
Fukushima station
Arakawa river
Debris barrier dam Bridge
Revetment Road
Higashi-karasugawa, Tsuchiyu-onsen
iPad 1st YOGA TABLET 8
Xperia Z2 Tablet
Xperia VL
- iOS - Android
- Android
- Android - CPU:1 GHzB
- CPU:1.2 GHz
- CPU:2.3 GHz
- CPU:1 GHz - RAM:256 MB
- RAM: 1 GB
- RAM: 3 GB
- RAM: 16 GB - 1024×768 px
- 1280×800 px
- 1920×1200 px
- 1280×720 px - Acceleration , Compass
- GPS, Acceleration, Gyro, Compass
- GPS, Acceleration, Gyro, Compass
- GPS, acceleration, gyro, compass
Colored points Filtering
DEM 3D data
+ Color
Base patch
Missing data
Reference patch candidates
Reconstruction Reference
Reconstructed patch
Meta data
砂防指定地 1. ⼯作物 新築，改築，移転⼜ 除却
2. ⼟地 掘削，盛⼟，切⼟等 形状を変更す ⾏為 3. ⼟⽯⼜ 砂 き 採取，集積⼜ 投棄
4. ⽴⽊⽵ 伐採 5. 樹根，芝草⼜ 埋も ⽊ 採取
6. ⽊⽵，⼟⽯等 滑下⼜ 地引きによ 運搬 7. ゴミ，産業廃棄物等 不法投棄
8. 指定地標識 有無 9. 指定地標識 発錆によ 劣化等 変状
10. 流域 荒廃 11. ⼭腹 崩壊
12. 地す 等 変状
13. 流域 倒⽊ 14. 渓床
不安定⼟砂 蓄積 15. そ 他
えん堤⼯ 1. 堤体 破損
2. 堤体 クラック 3. 堤体 漏⽔
4. 堤体 変位 5. 周辺地⼭ 崩壊
6. 周辺地⼭ 漏⽔ 7. 周辺地⼭ 地す
等 変状 8. 基礎地盤 洗掘
9. 基礎地盤 変位 10. ⼟砂 異常堆砂
11. ⿂道 破損 12. 防護柵等 付属施設 破損
13. そ 他 床固⼯
1. 床固 破損 2. 床固 クラック
3. 床固 変位 4. ⼟砂，枯 草等によ 流下能⼒ 低下
5. ⿂道 破損 6. 防護柵等 付属施設 破損
7. そ 他
護岸⼯ 1. 護岸 開⼝、破損
2. 護岸 クラック 3. 護岸 沈下，吸い出し等 変状
4. 根⼊ 部 洗掘等 変状 5. ⼟砂，枯 草等によ 流下能⼒ 低下
6. 防護柵等 付属施設 破損 7. そ 他
親⽔設備⼯ 1. 親⽔設備 開⼝、破損
2. 親⽔設備 クラック 3. 親⽔設備 沈下，吸い出し等 変状
4. 根⼊ 部 洗掘等 変状 5. ⾼⽔敷 陥没等 変状
6. 深い淵，急流 瀬等，危険性 ⾼い河川 変状 7. 防護柵等 付属施設 破損
8. 標識，看板等 破損 9. そ 他
管理⽤道路 1. 開⼝，陥没等 変状
2. 雑草 繁茂 3. そ 他
Main data
- Checklists - The degree of emergency A,B,C or D
- Identifier - Project name
- Address - Regionʼs name
- Officeʼs name - Inspectorʼs name
- Weather - Date
- Time
This contribution has been peer-reviewed. Editors: S. Zlatanova, G. Sithole, M. Nakagawa, and Q. Zhu
doi:10.5194isprsarchives-XL-3-W3-257-2015 259
Figure 10. Panoramic video camera THETA
Figure 11. Panoramic video camera QBiC
Figure 12. Panoramic video viewer
3.2 Base map generation
