The 3rd UN World Conference on Disaster Risk Reduction in Sendai
PUBLIC FORUM
第三回国連防災世界会議フォーラム

Fast Estimation of the disaster debris
for immediate recovery from the disaster
被災からの早期復旧へ向けた
高速な災害廃棄物量推定

Forum Abstract
フォーラム概要
Laboratory of Multidisciplinary Research on the Circulation of Waste Resources,
Sendaikankyo Co. Endowed Lab, Graduate School of Environmental Studies, Tohoku
University
東北大学大学院環境科学研究科・資源循環複合新領域(仙台環境開発株式会社寄付講座)

Mar 15, 2015, Sendai Civic Auditorium, Meeting room 1,2015 年 3 月 15 日 9:00~11:00 仙台市市民会館会議室 1

Sendai Civic Auditorium, Cofference Room No.1
4-1, Sakuragaoka Koen, Aoba-ku, Sendai.
Tel 022-262-4721

Analysis of aerial photographs and satellite images for the estimationof mega-disaster waste in devastated areasDate:

9:00−11:00, March 15, 2015

Venue:

Sendai Civic Auditorium Meeting room1, 4-1, Sakuragaoka Koen, Aoba-Ku, Sendai

Programme
9:00～ 9:10

Opening

9:10～ 9:15

Opening Address
Azuma Ohuchi (Professor, Graduate School of Environmental Studies, Tohoku
University)

9:15～ 9:35

Analysis of Remote Sensing Data to Estimate Amount of Disaster Waste
Yoichi Kageyama (Professor, Graduate School of Engineering and Resource Science,
Akita University)

9:35～10:05

Estimation Amount of Disaster Debris by Analyzing the Aerial Images
Masahito Yamamoto (Professor, Graduate School of Information Science andTechnology, Hokkaido University)

10:05～10:25

Issues of Disaster Waste Transport Planning
Kunihiro Kishi (Associate Professor, Graduate School of Engineering, Hokkaido
University)

10:25 ～ 10:45

Building of Water Resource Management System with Integrated Water Cycling
Simulation
Hiroshi Yamamura (Assistant Professor, Faculty of Science and Engineering, Department
of Integrated Science and Engineering for Sustainable Society, Chuo University)

10:45

ClosingThe 3rd UN World Conference on Disaster Risk Reduction in Sendai PUBLIC FORUM（March 15, 2015）[ID:163]

Analysis of aerial photographs and satellite images for theestimation of mega-disaster waste in devastated areas
Opening address
Azuma Ohuchi
Professor of Laboratory of Multidisciplinary Research on the Circulation of Waste Resources,(Sendaikankyo Co. Endowed Lab, Graduate School of Environmental Studies, Tohoku University)

Abstract:
Areas devastated by mega-disasters suffer from severe damages of their infrastructures. Lack of
information prevents fast reconstruction of the affected areas. The analysis of aerial photographs
and satellite images can be employed to gather information of the affected areas necessary for theirreconstruction. The results can be widely used for planning the tasks required for recovery such asscheduling the waste removal and the business recovery process for companies, etc. The aim of
this forum is to introduce our effort to detect the damaged building and to estimate the amount ofdisaster debris (waste) by using aerial photographs and by satellite images, and to discuss the future
perspective of applying the results to disaster waste transport planning and water resource
management system.

1

The 3rd UN World Conference on Disaster Risk Reduction in Sendai PUBLIC FORUM

Analysis of aerial photographs and satellite images for theestimation of mega-disaster waste in devastated areas
Analysis of Remote Sensing Data to Estimate Amount ofDisaster Waste

Yoichi Kageyama
Professor, Graduate School of Engineering and Resource Science, Akita University
The coastal area in Miyagi Prefecture,

Abstract:
When a large-scale disaster occurs such as

Japan was selected as the study area, andthe East Japan Great Earthquake, there is a

Thailand Earth Observation System (THEOS)

need to estimate the amount of disasterdata acquired on March 14, 2011 was used for

wastes promptly and to perform subsequent

analysis. The ground sample of the THEOS isrestoration activities quickly and effectively

15 m for bands 1 through 4.

[1]. We are sure that a disaster waste

Fig.1 shows the flowchart of data analysis.

estimation using remote sensing data is the
firstprioritytoaffectallFirst, five classes— Buildings, Sea, Flooded

subsequentsoil, Vegetation and Soil — were set and thenprocessing. However, the digital number

forty pixels were extracted per class as the(DN) of each pixel represents the average
land

cover

conditions.

That

is,

supervised data. Second, the multispectral

the

bands

information provided by a pixel should be

providedanestimateoftheoccupancies in a pixel by fuzzy reasoning [2].

represented as one-pixel mix-class instead ofThird, pure pixels and mixels were classified

one-pixel one-class. This pixel is referred tousing the DNs and the texture. When the classas a mixel [2]; both mixels and pure pixels

occupancy, obtained by the DNs, of any pureshould be considered in order to accurately

class except "Flooded soil" class wasn’t lessclassify land cover conditions. It is alsothan the threshold T1, the pixel was classified

necessary to develop a method for extracting

into the class that was characterized by thedetailed information buried in mixels oflargest value. Subsequently, if the classremote sensing data. Therefore, the aim ofoccupancy of the "Flooded soil", obtained bythis talk is to introduce a method forthe texture, wasn’t less than the threshold T2,

unmixing mixels that uses the DNs andthe pixel was classified as the pure pixel oftexture features appeared in remote sensing

the "Flooded soil". Pixels not classified in thedata [3].

2Set five land cover classes and acquire supervised data
Estimate class occupancies in each pixels using the DNs and the textures
Flooded soil
class is include in the
resultYES

NO
Ratio obtained using
DNs＞T1
NO

YES

YES

Ratio obtained using
textures＞T2
NOClassify it as a pure pixel

Re-estimate class occupancies for top 2 classes of the above processing
Magnify mixels on the basis of the class occupancies and their location information

230pixels

Fig.1 Flowchart of data analysis.

Buildings
Vegetation

Sea

Flooded soil

Soil

Fig.3 Classification result.

230pixels

pure pixels, although the original image
consists of the mixels and pure pixels.
690pixels

Therefore, the boundaries of the classes andthe land cover conditions are clear. Fig.3
shows the classification result. The proposed
method is in good agreement with themanually classified map; the matching rate of
the proposed method is 90.44%. The result
suggests that the proposed approach using

690pixels

mixels provides reasonable results.

Fig.2 Magnification result.

References[1]T. Takaya, S. Sato, A. Ohuchi, “Consideration about the amount presumption
system of disaster wastes learned fromthe East Japan Great Earthquake”， The
74th, National Convention of IPSJ, 3B-4
(2012).
[2]M. Nishida, K. Otsuka, R. Tabata,“Estimation of Class Mixture Proportionof Mixel due to Fuzzy Reasoning,” Trans.
IEE Japan, Vol.116-C, pp.359-366 (1996).
[3]Y. Kageyama, K. Hisa, A. Ohuchi, T.
Takaya, M. Nishida, “Method for unmixing

above-mentioned process were mixels. Fourth,for the mixels, class occupancies for top 2
classes were re-estimated using textures.
Finally, mixels were magnified on the basis of
the class occupancies and their location
information.
Fig.2 shows a magnification result. One
pixel in the original image is divided into nine

mixels of THEOS data to estimate amount ofdisaster waste”, Journal of Japan Society for
Fuzzy Theory and Intelligent Informatics,

pixels by the proposed method; all the pixelsof the magnification result are expressed as

Vol.26, No.2 (in press).

3㻹㼛㼠㼕㼢㼍㼠㼕㼛㼚㻱㼟㼠㼕㼙㼍㼠㼑㻌㼠㼔㼑㻌㼍㼙㼛㼡㼚㼠㻌㼛㼒㻌㼐㼕㼟㼍㼟㼠㼑㼞㻌㼣㼍㼟㼠㼑㼟 㻾㼑㼏㼛㼢㼑㼞㼥㻌㼍㼚㼐㻌㼞㼑㼏㼛㼚㼟㼠㼞㼡㼏㼠㼕㼛㼚㻌㼜㼘㼍㼚㻌㼘㼑㼍㼞㼚㼑㼐㻌㼒㼞㼛㼙㻌㼠㼔㼑㻌㻱㼍㼟㼠㻌㻶㼍㼜㼍㼚㻌㻳㼞㼑㼍㼠㻌㻌㻱㼍㼞㼠㼔㼝㼡㼍㼗㼑㻭㼚㼍㼘㼥㼟㼕㼟㻌㼛㼒㻌㼍㼑㼞㼕㼍㼘㻌㼜㼔㼛㼠㼛㼓㼞㼍㼜㼔㼟㻌㼍㼚㼐㻌㼟㼍㼠㼑㼘㼘㼕㼠㼑㻌㼕㼙㼍㼓㼑㼟㻌㼒㼛㼞㻌㼠㼔㼑㻌㼑㼟㼠㼕㼙㼍㼠㼕㼛㼚㻌㼛㼒㻌㼙㼑㼓㼍㻙㼐㼕㼟㼍㼟㼠㼑㼞㻌㼣㼍㼟㼠㼑㻌㼕㼚㻌㼐㼑㼢㼍㼟㼠㼍㼠㼑㼐㻌㼍㼞㼑㼍㼟䠝 㼚㼑㼑㼐㻌㼠㼛㻌㼑㼟㼠㼕㼙㼍㼠㼑㻌㼠㼔㼑㻌㼍㼙㼛㼡㼚㼠㻌㼛㼒㻌㼐㼕㼟㼍㼟㼠㼑㼞㻌㼣㼍㼟㼠㼑㼟㻌㼜㼞㼛㼙㼜㼠㼘㼥㻌㼍㼚㼐㻌㼠㼛㻌㼜㼑㼞㼒㼛㼞㼙㻌㼟㼡㼎㼟㼑㼝㼡㼑㼚㼠㻌㼞㼑㼟㼠㼛㼞㼍㼠㼕㼛㼚㻌㼍㼏㼠㼕㼢㼕㼠㼕㼑㼟㻌㼝㼡㼕㼏㼗㼘㼥㻌㼍㼚㼐㻌㼑㼒㼒㼑㼏㼠㼕㼢㼑㼘㼥㻌㻭㼚㼍㼘㼥㼟㼕㼟㻌㼛㼒㻌㻾㼑㼙㼛㼠㼑㻌㻿㼑㼚㼟㼕㼚㼓㻌㻰㼍㼠㼍㻌㼠㼛㻌㻱㼟㼠㼕㼙㼍㼠㼑㻌㻭㼙㼛㼡㼚㼠㻌㼛㼒㻌㻰㼕㼟㼍㼟㼠㼑㼞㻌㼃㼍㼟㼠㼑 䠮䡁䡉䡋䡐䡁

䠯䡁䡊䡏䡅䡊䡃

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䊼㻵㼚㼟㼠㼍㼘㼘㻌㼠㼑㼙㼜㼛㼞㼍㼞㼥㻌㼜㼘㼍㼏㼑㼟㻼㼞㼑㼜㼍㼞㼑㻌㼙㼍㼏㼔㼕㼚㼑㼟㻌㼍㼚㼐㻌㼣㼛㼞㼗㼑㼞㼟

䊼㻵㼚㼟㼠㼍㼘㼘㻌㼜㼞㼕㼙㼍㼞㼥㻌㼐㼡㼙㼜㼟㻼㼞㼛㼏㼑㼟㼟㼕㼚㼓㻌㼙㼑㼠㼔㼛㼐㻌㼍㼚㼐㻌㼟㼥㼟㼠㼑㼙䊼㻱㼟㼠㼕㼙㼍㼠㼑㻌㼠㼞㼑㼍㼠㼙㼑㼚㼠㻌㼏㼛㼟㼠㼟㻌㼍㼚㼐㻌㼜㼑㼞㼕㼛㼐

Flowchart of 㼐㼕㼟㼍㼟㼠㼑㼞㻌wastes disposal plan 䈂䈂㻌㼀㻚㻌㼀㼍㼗㼍㼥㼍㻘㻌㻿㻚㻌㻿㼍㼠㼛㻘㻌㻭㻚㻌㻻㼔㼡㼏㼔㼕㻘㻌䇾㻯㼛㼚㼟㼕㼐㼑㼞㼍㼠㼕㼛㼚㻌㼍㼎㼛㼡㼠㻌㼠㼔㼑㻌㼍㼙㼛㼡㼚㼠㻌㼜㼞㼑㼟㼡㼙㼜㼠㼕㼛㼚㻌㼟㼥㼟㼠㼑㼙㻌㼛㼒㻌㼐㼕㼟㼍㼟㼠㼑㼞㻌㼣㼍㼟㼠㼑㼟㻌㼘㼑㼍㼞㼚㼑㼐㻌㼒㼞㼛㼙㻌㼠㼔㼑㻌㻱㼍㼟㼠㻌㻶㼍㼜㼍㼚㻌㻳㼞㼑㼍㼠㻌㻱㼍㼞㼠㼔㼝㼡㼍㼗㼑䇿㻘㼀㼔㼑 㻣㻠㼠㼔㻘㻌㻺㼍㼠㼕㼛㼚㼍㼘㻌㻯㼛㼚㼢㼑㼚㼠㼕㼛㼚㻌㼛㼒㻌㻵㻼㻿㻶㻘㻌㻟㻮㻙㻠㻌㻔㻞㻜㻝㻞㻕㻚ᮾ໭኱Ꮫ䛿➨䠏ᅇᅜ㐃㜵⅏ୡ⏺఍㆟䜢ᨭ᥼䛧䛶䛔䜎䛩䚹㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌 㼀㼔㼑㻌㼏㼛㼍㼟㼠㼍㼘㻌㼍㼞㼑㼍㻌㼕㼚㻌㻹㼕㼥㼍㼓㼕㻌㻼㼞㼑㼒㼑㼏㼠㼡㼞㼑㻘㻌㻶㼍㼜㼍㼚㻼㼡㼞㼜㼛㼟㼑
㻰㼑㼢㼑㼘㼛㼜㻌㼍㻌㼑㼟㼠㼕㼙㼍㼠㼕㼛㼚㻌㼙㼑㼠㼔㼛㼐㻌㼛㼒㻌㼍㼙㼛㼡㼚㼠㻌㼛㼒㻌㼐㼕㼟㼍㼟㼠㼑㼞㻌㼣㼍㼟㼠㼑㼟㼎㼥㻌㼞㼑㼙㼛㼠㼑㻌㼟㼑㼚㼟㼕㼚㼓㻌㼐㼍㼠㼍㼃㼔㼑㼚㻌㼠㼔㼑㻌㼞㼑㼟㼛㼘㼡㼠㼕㼛㼚㻌㼛㼒㻌㼠㼔㼑㻌㼐㼍㼠㼍㻌㼕㼟㻌㼕㼙㼜㼞㼛㼢㼑㼐㻘㻌㼐㼑㼠㼍㼕㼘㼑㼐㻌㼘㼍㼚㼐㻌㼏㼛㼢㼑㼞㻌㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼏㼍㼚㻌㼎㼑㻌㼛㼎㼠㼍㼕㼚㼑㼐㻌㼡㼚㼐㼑㼞㻌㼠㼔㼑㻌㼏㼛㼚㼐㼕㼠㼕㼛㼚㼟㻌㼛㼒㻌㼠㼔㼑㻌㼣㼕㼐㼑㻌㼞㼍㼚㼓㼑㻌㼍㼚㼐㼔㼕㼓㼔㻌㼞㼑㼟㼛㼘㼡㼠㼕㼛㼚㻮㼛㼠㼔㻌㼙㼕㼤㼑㼘㼟㻌㼍㼚㼐㻌㼜㼡㼞㼑㻌㼜㼕㼤㼑㼘㼟㻌㼟㼔㼛㼡㼘㼐㻌㼎㼑㻌㼏㼛㼚㼟㼕㼐㼑㼞㼑㼐㻌㼕㼚㻌㼛㼞㼐㼑㼞㻌㼠㼛㻌㼍㻌㼙㼑㼠㼔㼛㼐㻌㼒㼛㼞㻌㼑㼤㼠㼞㼍㼏㼠㼕㼚㼓㻌㼐㼑㼠㼍㼕㼘㼑㼐㻌㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼎㼡㼞㼕㼑㼐㻌㼕㼚㻌㼍㼏㼏㼡㼞㼍㼠㼑㼘㼥㻌㼏㼘㼍㼟㼟㼕㼒㼥㻌㼘㼍㼚㼐㻌㼏㼛㼢㼑㼞㻌㼏㼛㼚㼐㼕㼠㼕㼛㼚㼟㻚㻌㼍㻌㼙㼑㼠㼔㼛㼐㻌㼒㼛㼞㻌㼑㼤㼠㼞㼍㼏㼠㼕㼚㼓㻌㼐㼑㼠㼍㼕㼘㼑㼐㻌㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼎㼡㼞㼕㼑㼐㻌㻰㼑㼢㼑㼘㼛㼜㻌㼍㻌㼙㼑㼠㼔㼛㼐㻌㼒㼛㼞㻌㼡㼚㼙㼕㼤㼕㼚㼓㻌㼙㼕㼤㼑㼘㼟㻌㼠㼔㼍㼠㻌㼡㼟㼑㼟㻌㼠㼔㼑㻌㻰㻺㼟㻌㼍㼚㼐㻌㼠㼑㼤㼠㼡㼞㼑㼟㼕㼚㻌㼙㼕㼤㼑㼘㼟㻌㼛㼒㻌㼞㼑㼙㼛㼠㼑㻌㼟㼑㼚㼟㼕㼚㼓㻌㼛㼒㻌㼞㼑㼙㼛㼠㼑㻌㼟㼑㼚㼟㼕㼚㼓㻌㼐㼍㼠㼍㻚㼀㻴㻱㻻㻿㻌㻹㻿 㼐㼍㼠㼍㻌㼍㼏㼝㼡㼕㼞㼑㼐㻌㼛㼚㻌㻹㼍㼞㼏㼔㻌㻝㻠㻘㻌㻞㻜㻝㻝㻔㻾㻘㻳㻘㻮㻧㻌㼎㼍㼚㼐㻌㻟㻘㼎㼍㼚㼐㻌㻞㻘㻌㼎㼍㼚㼐㻌㻝㻕㼍㼜㼜㼑㼍㼞㼑㼐㻌㼕㼚㻌㼞㼑㼙㼛㼠㼑㻌㼟㼑㼚㼟㼕㼚㼓㻌㼐㼍㼠㼍

4㻹㼍㼜㻦㻌 㼔㼠㼠㼜㻦㻛㻛㼣㼣㼣㻚㼟㼑㼗㼍㼕㼏㼔㼕㼦㼡㻚㼖㼜㻛㼍㼠㼘㼍㼟㻛㼖㼍㼜㼍㼚㻛㼜㼞㼑㼒㼑㼏㼠㼡㼞㼑㻛㼙㼍㼜㼋㼜㻛㼜㼋㼍㼞㼑㼍㼋㼠㼛㼔㼛㼗㼡㻚㼔㼠㼙㼘㻰㼍㼠㼍㻌㼡㼟㼑㼐㼀㻴㻱㻻㻿㻌㻹㻿 㼐㼍㼠㼍 㼀㻴㻱㻻㻿㻔㼀㼔㼍㼕㼘㼍㼚㼐㻌㻱㼍㼞㼠㼔㻌㻻㼎㼟㼑㼞㼢㼍㼠㼕㼛㼚㻌㻿㼥㼟㼠㼑㼙㻕 㻭㼏㼝㼡㼕㼞㼑㼐㻌㼛㼚㻌㻹㼍㼞㼏㼔㻌㻝㻠㻘㻌㻞㻜㻝㻝 㼀㼔㼑㻌㼓㼞㼛㼡㼚㼐㻌㼟㼍㼙㼜㼘㼑㻌㼛㼒㻌㼠㼔㼑㻌㼀㻴㻱㻻㻿㻌㼕㼟㻌㻝㻡㻌㼙㻌㼒㼛㼞㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼔㼞㼛㼡㼓㼔㻌㻠㻚 㻿㼑㼘㼑㼏㼠㻌㼍㼚㻌㼍㼞㼑㼍㻌㼛㼢㼑㼞㼒㼘㼛㼣㼑㼐㻦㻌㻭㼞㼑㼍㼟㻌㻭㻌㼍㼚㼐㻌㻮 㻻㼎㼟㼑㼞㼢㼍㼠㼕㼛㼚㻌㼣㼕㼐㼠㼔㻌㼕㼟㻌㻥㻜㼗㼙 㻿㼕㼦㼑㻌㼛㼒㻌㼠㼔㼑㻌㼟㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㼕㼟㻌㻞㻟㻜㻌㽢㻞㻟㻜㻌㼜㼕㼤㼑㼘㼟㼀㻴㻱㻻㻿 䈂㻭㼞㼑㼍㻌㻭
㼃㼍㼢㼑㼘㼑㼚㼓㼠㼔㻔䃛㼙㻕

㻜㻚㻠
㻮㼘㼡㼑

㻻㼎㼟㼑㼞㼢㼍㼠㼕㼛㼚㻌㼎㼍㼚㼐㻌㼛㼒㻌㼀㻴㻻㻱㻿㻌㻹㻿㻜㻚㻡㻮㼍㼚㼐㻌㻝

㻳㼞㼑㼑㼚

㻜㻚㻢

㻜㻚㻣
㻾㼑㼐

㻝
㻺㼑㼍㼞㻙㼕㼚㼒㼞㼍㼞㼑㼐㻮㼍㼚㼐㻌㻞

㻮㼍㼚㼐㻌㻟

㻮㼍㼚㼐㻌㻠

㻞㻭㼞㼑㼍㻌㻮

㼀㻴㻱㻻㻿 㻹㻿 㼐㼍㼠㼍㻌㻔㻾㻘㻳㻘㻮㻧㻌㼎㼍㼚㼐㻌㻟㻘㻌㼎㼍㼚㼐㻌㻞㻘㻌㼎㼍㼚㼐㻌㻝㻕䈂㻌㻳㻵㻿㼀㻰㻭㻌㼃㻱㻮 㼟㼕㼠㼑䠖 㼔㼠㼠㼜㻦㻛㻛㼣㼣㼣㻚㼓㼕㼟㼠㼐㼍㻚㼛㼞㻚㼠㼔㻻㼡㼠㼘㼕㼚㼑㻌㼛㼒㻌㼡㼚㼙㼕㼤㼕㼚㼓㻌㻹㼕㼤㼑㼘㼟㼁㼚㼙㼕㼤㼕㼚㼓㻌㼙㼕㼤㼘㼟㻲㼘㼛㼣㼏㼔㼍㼞㼠㻌㼛㼒㻌㼠㼔㼑㻌㼜㼞㼛㼜㼛㼟㼑㼐㻌㼙㼑㼠㼔㼛㼐㻯㼛㼚㼏㼑㼜㼠㻌㼛㼒㻌㼜㼕㼤㼑㼘㼟㻌㼕㼚㻌㼞㼑㼙㼛㼠㼑㻌㼟㼑㼚㼟㼕㼚㼓㻌㼐㼍㼠㼍㻵㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼜㼞㼛㼢㼕㼐㼑㼐㻌㼎㼥㻌㼍㻌㼜㼕㼤㼑㼘㻌㼟㼔㼛㼡㼘㼐㻌㼎㼑㻌㼞㼑㼜㼞㼑㼟㼑㼚㼠㼑㼐㻌㼍㼟㻌㼛㼚㼑㻙㼜㼕㼤㼑㼘㻌㼛㼚㼑㻙㼏㼘㼍㼟㼟㻱㼟㼠㼕㼙㼍㼠㼑㻌㼠㼔㼑㻌㼛㼏㼏㼡㼜㼍㼚㼏㼕㼑㼟㻌㼕㼚㻌㼍㻌㼜㼕㼤㼑㼘㻌㼎㼥㻌㼒㼡㼦㼦㼥㻌㼞㼑㼍㼟㼛㼚㼕㼚㼓䈂

Sea

Soil

㻯㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼕㼟㻌㼜㼡㼞㼑㻌㻯㼛㼚㼏㼑㼜㼠㻌㼛㼒㻌㼜㼡㼞㼑㻌㼜㼕㼤㼑㼘㻌㼍㼚㼐㻌㼙㼕㼤㼑㼘

㻡㻌㼙

㼅㻱㻿

㼁㼚㼙㼕㼤㼕㼚㼓㻌㼙㼕㼤㼘㼟㻺㻻㼁㼚㼙㼕㼤㼕㼚㼓㻌㼙㼕㼤㼑㼘㼟㻌㼡㼟㼕㼚㼓㻌㻵㼙㼍㼓㼑㻌㻾㼑㼟㼛㼘㼡㼠㼕㼛㼚㻌㻭㼘㼓㼛㼞㼕㼠㼔㼙㻌㼒㼛㼞㻌㻹㼕㼤㼑㼘䈂䈂㼁㼚㼙㼕㼤㼕㼚㼓㻌㼙㼕㼤㼑㼘㼟㻌㼙㼍㼗㼑㼟㻌㼕㼠㻌㼜㼛㼟㼟㼕㼎㼘㼑㻌㼠㼛㻌㼛㼎㼠㼍㼕㼚㻌㼐㼑㼠㼍㼕㼘㼑㼐㻌㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼎㼡㼞㼕㼑㼐㻌㼕㼚㻌㼠㼔㼑㻌㼞㼑㼟㼛㼘㼡㼠㼕㼛㼚㻌㼛㼒㻌㼞㼑㼙㼛㼠㼑㻌㼟㼑㼚㼟㼕㼚㼓㻌㼐㼍㼠㼍㻝㻡㻌㼙㻱㼟㼠㼕㼙㼍㼠㼕㼚㼓㻌㼏㼘㼍㼟㼟㻌㼛㼏㼏㼡㼜㼍㼚㼏㼕㼑㼟㻯㼘㼍㼟㼟㼕㼒㼥㻌㼜㼡㼞㼑㻌㼜㼕㼤㼑㼘㼟㻌㼍㼚㼐㻌㼙㼕㼤㼑㼘㼟㻹㼕㼤㼑㼘㻌㼛㼒㻌㼟㼑㼍㻌㼍㼚㼐㻌㼟㼛㼕㼘

㻹㼕㼤㼑㼐㻌㻼㼕㼤㼑㼘㻌㻔㻹㼕㼤㼑㼘㻕㻵㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼜㼞㼛㼢㼕㼐㼑㼐㻌㼎㼥㻌㼍㻌㼜㼕㼤㼑㼘㻌㼟㼔㼛㼡㼘㼐㻌㼎㼑㻌㼞㼑㼜㼞㼑㼟㼑㼚㼠㼑㼐㻌㼍㼟㻌㼛㼚㼑㻙㼜㼕㼤㼑㼘㻌㼙㼕㼤㻙㼏㼘㼍㼟㼟㻭㼏㼝㼡㼕㼞㼑 㼟㼡㼜㼑㼞㼢㼕㼟㼑㼐㻌㼐㼍㼠㼍㻼㼡㼞㼑㻌㼜㼕㼤㼑㼘㻌㼛㼒㻌㼟㼑㼍

㻼㼡㼞㼑㻌㻼㼕㼤㼑㼘

5㻻㼡㼠㼜㼡㼠㻌㼍㼟㻌㼜㼡㼞㼑㻌㼜㼕㼤㼑㼘䈂㻌㻌㻌㻹㻚㻌㻺㼕㼟㼔㼕㼐㼍㻘㻌㻷㻚㻌㻻㼠㼟㼡㼗㼍㻘㻌㻾㻚㻌㼀㼍㼎㼍㼠㼍㻘㻌䇾㻱㼟㼠㼕㼙㼍㼠㼕㼛㼚㻌㼛㼒㻌㻯㼘㼍㼟㼟㻌㻹㼕㼤㼠㼡㼞㼑㻌㻼㼞㼛㼜㼛㼞㼠㼕㼛㼚㻌㼛㼒㻌㻹㼕㼤㼑㼘㻌㼐㼡㼑㻌㼠㼛㻌㻲㼡㼦㼦㼥㻌㻾㼑㼍㼟㼛㼚㼕㼚㼓㻘䇿㻌㼀㼞㼍㼚㼟㻚㻌㻵㻱㻱㻌㻶㼍㼜㼍㼚㻘㻌㼂㼛㼘㻚㻝㻝㻢㻙㻯㻘㻌㼜㼜㻚㻟㻡㻥㻙㻟㻢㻢㻌㻔㻝㻥㻥㻢㻕㻚㻌䈂㻌䈂㻌㼅㻚㻷㼍㼓㼑㼥㼍㼙㼍㻘㻌㻹㻚㻺㼕㼟㼔㼕㼐㼍㻘㻌㻵㼙㼍㼓㼑㻌㻾㼑㼟㼛㼘㼡㼠㼕㼛㼚㻌㻭㼘㼓㼛㼞㼕㼠㼔㼙㻌㼒㼛㼞㻌㻹㼕㼤㼑㼐㻌㻼㼕㼤㼑㼘㻌㼕㼚㻌㻾㼑㼙㼛㼠㼑㻌㻿㼑㼚㼟㼕㼚㼓㻌㻰㼍㼠㼍㻘㻌㼀㼞㼍㼚㼟㻚㻌㻵㻱㻱㻌㻶㼍㼜㼍㼚㻘㻌㼂㼛㼘㻚㻝㻞㻝㻙㻯㻘㻌㻺㼛㻚㻡㻘㻌㼜㼜㻚㻥㻢㻝㻙㻥㻢㻢㻌㻔㻞㻜㻜㻝㻕㻱㼟㼠㼕㼙㼍㼠㼕㼛㼚㻌㼛㼒㻌㼏㼘㼍㼟㼟㻌㼛㼏㼏㼡㼜㼍㼚㼏㼕㼑㼟㻭㼏㼝㼡㼕㼟㼕㼠㼕㼛㼚㻌㼛㼒㻌㼟㼡㼜㼑㼞㼢㼕㼟㼑㼐㻌㼐㼍㼠㼍㻭㼏㼝㼡㼕㼟㼕㼠㼕㼛㼚㻌㼛㼒㻌㼟㼡㼜㼑㼞㼢㼕㼟㼑㼐㻌㼐㼍㼠㼍 㼒㼕㼢㼑㻌㼏㼘㼍㼟㼟㼑㼟䇶 㻮㼡㼕㼘㼐㼕㼚㼓㼟㻘㻌㻿㼑㼍㻘㻌㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘㻘㻌㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚㻌㼍㼚㼐㻌㻿㼛㼕㼘㻌䇶 㼣㼑㼞㼑㻌㼟㼑㼠㻚 㼒㼛㼞㼠㼥㻌㼜㼕㼤㼑㼘㼟㻌㼣㼑㼞㼑㻌㼑㼤㼠㼞㼍㼏㼠㼑㼐㻌㼜㼑㼞㻌㼏㼘㼍㼟㼟㻌㼍㼟㻌㼠㼔㼑㻌㼟㼡㼜㼑㼞㼢㼕㼟㼑㼐㻌㼐㼍㼠㼍㻚㻭㼢㼑㼞㼍㼓㼑㻌㼍㼚㼐㻌㼢㼍㼞㼕㼍㼚㼏㼑㻌㼛㼒㻌㼠㼔㼑㻌㻰㻺㼟 㼛㼒㻌㼠㼔㼑㻌㼟㼡㼜㼑㼞㼢㼕㼟㼑㼐㻌㼐㼍㼠㼍 㻭㼚㻌㼍㼢㼑㼞㼍㼓㼑㻌㼍㼚㼐㻌㼢㼍㼞㼕㼍㼚㼏㼑㻌㼛㼒㻌㼠㼔㼑㻌㻰㻺㼟㻌㼍㼚㼐㻌㼠㼔㼑㻌㼠㼑㼤㼠㼡㼞㼑㼟㻌㼣㼑㼞㼑㻌㼏㼛㼙㼜㼡㼠㼑㼐㻘㻌㼎㼍㼚㼐㼞㼑㼟㼜㼑㼏㼠㼕㼢㼑㼘㼥㻚㻮㼡㼕㼘㼐㼕㼚㼓㼟㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚㻿㼑㼍㻮㼍㼚㼐 㻝

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㻾㼍㼠㼑㻌㼛㼒㻌㼘㼍㼚㼐㻌㼏㼛㼢㼑㼞㻌㼇㻑㼉㼏㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㼇㻑㼉

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㼀㼛㼠㼍㼘

㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻮㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻭㼏㼘㼍㼟㼟㻾㼍㼠㼑㻌㼛㼒㻌㼘㼍㼚㼐㻌㼏㼛㼢㼑㼞㻌㼇㻑㼉㼏㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㼇㻑㼉

㻾㼍㼠㼑㻌㼛㼒㻌㼘㼍㼚㼐㻌㼏㼛㼢㼑㼞㻌㼇㻑㼉㼏㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㼇㻑㼉

㻮㼡㼕㼘㼐㼕㼚㼓㼟

㻥㻚㻝㻞

㻤㻟㻚㻝㻣

㻡㻚㻥㻞

㻡㻤㻚㻝㻥

㻥㻠㻚㻟㻡

㻿㼑㼍

㻝㻣㻚㻠㻢

㻥㻥㻚㻤㻟

㻞㻥㻚㻝㻜

㻥㻠㻚㻟㻡

㻠㻝㻚㻞㻤

㻥㻞㻚㻟㻜

㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘

㻠㻞㻚㻟㻠

㻥㻠㻚㻜㻠

㻠㻝㻚㻞㻤
㻞㻤

㻥㻞㻚㻟㻜

㻥㻤㻚㻠㻜

㻞㻚㻡㻣

㻥㻢㻚㻞㻥

㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚

㻞㻚㻡㻞

㻥㻤㻚㻠㻜

㻞㻚㻡㻣

㻥㻢㻚㻞㻥

㻤㻝㻚㻝㻢

㻞㻝㻚㻝㻠

㻣㻥㻚㻡㻟

㻿㼛㼕㼘

㻞㻤㻚㻡㻢

㻤㻝㻚㻝㻢

㻞㻝㻚㻝㻠
㻝㻠

㻣㻥㻚㻡㻟

㻤㻤㻚㻜㻞

㼀㼛㼠㼍㼘

㻥㻜㻚㻠㻠

㼀㼔㼑㻌㼜㼞㼛㼜㼛㼟㼑㼐㻌㼙㼑㼠㼔㼛㼐㻌㼕㼟㻌㼕㼚㻌㼓㼛㼛㼐㻌㼍㼓㼞㼑㼑㼙㼑㼚㼠㻌㼣㼕㼠㼔㻌㼠㼔㼑㻌㼙㼍㼚㼡㼍㼘㼘㼥㻌㼏㼘㼍㼟㼟㼕㼒㼕㼑㼐㻌㼙㼍㼜

8

㻥㻜㻚㻠㻠

㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻮䠖㼞㼍㼠㼑㼟㻌㼛㼒㻌㻮㼡㼕㼘㼐㼕㼚㼓㼟㻌㼍㼚㼐㻌㻿㼛㼕㼘㻌㼍㼞㼑㻌㼘㼛㼣㻤㻤㻚㻜㻞㻰㼕㼒㼒㼕㼏㼡㼘㼠㼥㻌㼕㼚㻌㼑㼤㼠㼞㼍㼏㼠㼕㼚㼓㻌㼐㼑㼠㼍㼕㼘㼑㼐㻌㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㼟㼕㼚㼏㼑㻌㼠㼔㼑㼥㻌㼍㼞㼑㻌㼏㼞㼛㼣㼐㼑㼐㻌㼣㼕㼠㼔㻌㼍㼞㼠㼕㼒㼕㼏㼕㼍㼘㻌㼟㼠㼞㼡㼏㼠㼡㼞㼑㼟㻌㼔㼍㼢㼕㼚㼓㻌㼠㼔㼑㻌㼢㼍㼞㼕㼛㼡㼟㻌㼒㼑㼍㼠㼡㼞㼑㼟㻯㼛㼙㼜㼍㼞㼕㼟㼛㼚㻌㼣㼕㼠㼔㻌㼠㼔㼑㻌㼞㼑㼟㼡㼘㼠㼟㻌㼎㼥㻌㼠㼔㼑㻌㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㻯㼛㼙㼜㼍㼞㼕㼟㼛㼚㻌㼣㼕㼠㼔㻌㼠㼔㼑㻌㼞㼑㼟㼡㼘㼠㼟㻌㼎㼥㻌㼠㼔㼑㻌㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻭㻰㼕㼒㼒㼕㼏㼡㼘㼠㼥㻌㼕㼚㻌㼡㼚㼐㼑㼞㼟㼠㼍㼚㼐㼕㼚㼓㻌㼠㼔㼑㻌㼎㼛㼡㼚㼐㼍㼞㼕㼑㼟㻌㼛㼒㻌㼠㼔㼑㻌㼏㼘㼍㼟㼟㼑㼟㻌㼍㼚㼐㻌㼠㼔㼑㻌㼘㼍㼚㼐㻌㼏㼛㼢㼑㼞㻌㼏㼛㼚㼐㼕㼠㼕㼛㼚㼟㻢㻥㻜㻌㼜㼕㼤㼑㼘㼟㻞㻟㻜㼜㼕㼤㼑㼘
㼜㼕㼤㼑㼑㼘

㻢㻥㻜㻌㼜㼕㼤㼑㼘㼟㻞㻟㻜㻌㼜㼕㼤㼑㼘㼟㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻭㻞㻟㻜㼜㼕㼤㼑㼘
㻞㻟㻜

ศゎ๓⏬ീ
⏬

㻞㻟㻜㻌㼜㼕㼤㼑㼘㼟㻻㼞㼕㼓㼕㼚㼍㼘㻌㼕㼙㼍㼓㼑㻼㼞㼛㼜㼛㼟㼑㼐㻌㼙㼑㼠㼔㼛㼐 㻺㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌 㻭㼑㼞㼕㼍㼘㻌㼜㼔㼛㼠㼛㼓㼞㼍㼜㼔䈂㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㻢㻥㻜㻌㼜㼕㼤㼑㼘㼟㻢㻥㻜㼜㼕㼤㼑㼘
㻢䈂㻌㻳㼑㼛㼟㼜㼍㼠㼕㼍㼘㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㻭㼡㼠㼔㼛㼞㼕㼠㼥㻌㼛㼒㻌㻶㼍㼜㼍㼚㻌䠖㼔㼠㼠㼜㻦㻛㻛㼟㼍㼕㼓㼍㼕㻚㼓㼟㼕㻚㼓㼛㻚㼖㼜㻛㼔㻞㻟㼠㼍㼕㼔㼑㼕㼥㼛㻙㼦㼛㼞㼠㻛㻹㼍㼓㼚㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻹㼍㼓㼚㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼠㼔㼑㻌㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㻹㼍㼓㼚㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻹㼍㼓㻹㼍㼓㼚㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼠㼔㼑㻌㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㻹㼍㼓㼚㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼠㼔㼑㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㻯㼛㼙㼜㼍㼞㼕㼟㼛㼚㻌㼣㼕㼠㼔㻌㼠㼔㼑㻌㼞㼑㼟㼡㼘㼠㼟㻌㼎㼥㻌㼠㼔㼑㻌㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㻯㼛㼙㼜㼍㼞㼕㼟㼛㼚㻌㼣㼕㼠㼔㻌㼠㼔㼑㻌㼞㼑㼟㼡㼘㼠㼟㻌㼎㼥㻌㼠㼔㼑㻌㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㻱㼢㼍㼘㼡㼍㼠㼕㼛㼚㻌㼛㼒㻌㼠㼔㼑㻌㼏㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻢㻥㻜㻌㼜㼕㼤㼑㼘㼟㻞㻟㻜㻌㼜㼕㼤㼑㼘㼟㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻮㻹㼍㼚㼥㻌㼜㼕㼤㼑㼘㼟㻌㼍㼞㼑㻌㼏㼘㼍㼟㼟㼕㼒㼕㼑㼐㻌㼍㼟㻌㼙㼕㼤㼑㼘㼟

㻞㻟㻜㻌㼜㼕㼤㼑㼘㼟㻻㼞㼕㼓㼕㼚㼍㼘㻌㼕㼙㼍㼓㼑㻰㼕㼒㼒㼕㼏㼡㼘㼠㼥㻌㼕㼚㻌㼡㼚㼐㼑㼞㼟㼠㼍㼚㼐㼕㼚㼓㻌㼠㼔㼑㻌㼎㼛㼡㼚㼐㼍㼞㼕㼑㼟㻌㼛㼒㻌㼠㼔㼑㻌㼏㼘㼍㼟㼟㼑㼟㻌㼍㼚㼐㻌㼠㼔㼑㻌㼘㼍㼚㼐㻌㼏㼛㼢㼑㼞㻌㼏㼛㼚㼐㼕㼠㼕㼛㼚㼟㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻮㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻭㻯㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼠㼔㼑㻌㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㻌㻢㻥㻜㻌㼜㼕㼤㼑㼘㼟㻹㼍㼓㼚㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻹㼍㼓㼚㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼠㼔㼑㻌㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚

9㼜㼡㼞㼑㻌㼜㼕㼤㼑㼘㼟䠖㻹㼕㼤㼑㼘㼟䠖

㻮㼡㼕㼘㼐㼕㼚㼓㼟㻔㻭㻕㻌㻭䞉㻮㻮䞉㻰㻿㼑㼍㻔㻮㻕
㻭䞉㻯㻮䞉㻱㻲㼘㼛㼛㼐㼑㼐㻌㻿㼛㼕㼘㻔㻯㻕㻌㻭䞉㻰㻯䞉㻰㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚㻔㻰㻕㻿㼛㼕㼘㻔㻱㻕
㻭䞉㻱
㻮䞉㻯㻌
㻯䞉㻱
㻰䞉㻱
㻻㼠㼔㼑㼞㼟

㻯㼛㼙㼜㼍㼞㼕㼟㼛㼚㻌㼣㼕㼠㼔㻌㼠㼔㼑㻌㼞㼑㼟㼡㼘㼠㼟㻌㼎㼥㻌㼠㼔㼑㻌㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㻿㼠㼡㼐㼥㻌㼛㼚㻌㼡㼟㼑㼒㼡㼘㼚㼑㼟㼟㻌㼛㼒㻌㼠㼔㼑㻌㼏㼛㼙㼎㼕㼚㼍㼠㼕㼛㼚㻌㼛㼒㻌㼠㼑㼤㼠㼡㼞㼑㼟㻌㼍㼚㼐㻌㻰㻺㼟㻯㼍㼘㼏㼡㼘㼍㼠㼕㼛㼚㻌㼛㼒㻌㼠㼔㼑㻌㼏㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㼛㼒㻌㼠㼔㼑㻌㼜㼞㼛㼏㼑㼟㼟㼕㼚㼓㻌㼠㼔㼍㼠㻌㼡㼟㼑㼟㻌㻰㻺㼟㻯㼍㼘㼏㼡㼘㼍㼠㼕㼛㼚㻌㼛㼒㻌㼠㼔㼑㻌㼏㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㼛㼒㻌㼠㼔㼑㻌㼚㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㻯㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㻔㼏㼘㼍㼟㼟㻌㼛㼒㻌㼠㼔㼑㻌㻿㼑㼍㻌㼕㼚㻌㼠㼔㼑㻌㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘㻌㼕㼟㻌㼜㼑㼞㼙㼕㼠㼠㼑㼐㻕㻯㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㻔㼏㼘㼍㼟㼟㻌㼛㼒㻌㼠㼔㼑㻌㻿㼑㼍㻌㼕㼚㻌㼠㼔㼑㻌㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘㻌㼕㼟㻌㼜㼑㼞㼙㼕㼠㼠㼑㼐㻕㼏㼘㼍㼟㼟㻮㼡㼕㼘㼐㼕㼚㼓㼟㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻭㻌㼇㻑㼉㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻮㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻭㻼㼞㼛㼜㼛㼟㼑㼐㻌 㻺㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌 㻼㼞㼛㼜㼛㼟㼑㼐㻌 㻺㼑㼍㼞㼑㼟㼠㻌㼚㼑㼕㼓㼔㼎㼛㼞㻌㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㼕㼚㼠㼑㼞㼜㼛㼘㼍㼠㼕㼛㼚㼙㼑㼠㼔㼛㼐
㼙㼑㼠㼔㼛㼐
㻤㻟㻚㻝㻣
㻞㻤㻚㻥㻠
㻡㻤㻚㻝㻥
㻝㻚㻤㻤

㻯㼘㼍㼟㼟

㻼㼞㼛㼜㼛㼟㼑㼐
㼙㼑㼠㼔㼛㼐

㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍 㻮㻌㼇㻑㼉㼁㼟㼑㻌㼛㼒㻌㻰㻺㼟㻼㼞㼛㼜㼛㼟㼑㼐
㼙㼑㼠㼔㼛㼐

㼁㼟㼑㻌㼛㼒㻌㻰㻺㼟㻮㼡㼕㼘㼐㼕㼚㼓㼟

㻤㻟㻚㻝㻣

㻤㻟㻚㻝㻣

㻡㻤㻚㻝㻥

㻡㻢㻚㻠㻝

㻿㼑㼍

㻥㻥㻚㻤㻟

㻥㻥㻚㻣㻥

㻥㻠㻚㻟㻡

㻥㻠㻚㻤㻡

㻿㼑㼍

㻥㻥㻚㻤㻟

㻥㻥㻚㻥㻥

㻥㻠㻚㻟㻡

㻥㻡㻚㻟㻤

㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘

㻥㻠㻚㻜㻠

㻣㻜㻚㻢㻤

㻥㻞㻚㻟㻜

㻞㻣㻚㻣㻜

㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘

㻥㻠㻚㻜㻠

㻤㻥㻚㻠㻟

㻥㻞㻚㻟㻜

㻣㻡㻚㻤㻟

㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚

㻥㻤㻚㻠㻜

㻥㻜㻚㻥㻢

㻥㻢㻚㻞㻥

㻠㻟㻚㻜㻥

㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚

㻥㻤㻚㻠㻜

㻥㻥㻚㻥㻥

㻥㻢㻚㻞㻥

㻤㻣㻚㻝㻜

㻿㼛㼕㼘

㻤㻝㻚㻝㻢

㻞㻟㻚㻞㻣

㻣㻥㻚㻡㻟

㻟㻠㻚㻡㻢

㻿㼛㼕㼘

㻤㻝㻚㻝㻢

㻤㻟㻚㻝㻣

㻣㻥㻚㻡㻟

㻥㻞㻚㻤㻢

㼀㼛㼠㼍㼘

㻥㻜㻚㻠㻠

㻡㻤㻚㻢㻣

㻤㻤㻚㻜㻞

㻠㻣㻚㻡㻢

㼀㼛㼠㼍㼘

㻥㻜㻚㻠㻠

㻤㻤㻚㻞㻥

㻤㻤㻚㻜㻞

㻤㻠㻚㻞㻢

㻟㻝㻚㻣㻣㻑

㻠㻜㻚㻠㻢㻑

㼁㼟㼑㻌㼛㼒㻌㼠㼑㼤㼠㼡㼞㼑㼟㻌㼍㼚㼐㻌㻰㻺㼟㻌㼕㼟㻌㼓㼛㼛㼐㻌㼒㼛㼞㻌㼏㼘㼍㼟㼟㼕㼒㼥㼕㼚㼓㻌㼠㼔㼑㻌㼏㼘㼍㼟㼟㻌㼛㼒㻌㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘㻚㼀㼔㼑㻌㼜㼞㼛㼜㼛㼟㼑㼐㻌㼙㼑㼠㼔㼛㼐㻌㼕㼟㻌㼜㼛㼟㼟㼕㼎㼘㼑㻌㼠㼛㻌㼍㼏㼏㼡㼞㼍㼠㼑㼘㼥㻌㼏㼘㼍㼟㼟㼕㼒㼥㻌㼠㼔㼑㻌㼘㼍㼚㼐㻌㼏㼛㼢㼑㼞㻌㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㻿㼠㼡㼐㼥㻌㼛㼚㻌㼡㼟㼑㼒㼡㼘㼚㼑㼟㼟㻌㼛㼒㻌㼠㼔㼑㻌㼏㼛㼙㼎㼕㼚㼍㼠㼕㼛㼚㻌㼛㼒㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻠㻿㼠㼡㼐㼥㻌㼛㼚㻌㼡㼟㼑㼒㼡㼘㼚㼑㼟㼟㻌㼛㼒㻌㼠㼔㼑㻌㼏㼛㼙㼎㼕㼚㼍㼠㼕㼛㼚㻌㼛㼒㻌㼠㼑㼤㼠㼡㼞㼑㼟㻌㼍㼚㼐㻌㻰㻺㼟㻯㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼡㼟㼕㼚㼓㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻟㻌㼟㼑㼠㻌㼕㼚㻌㼠㼔㼑㻌㼢㼕㼟㼕㼎㼘㼑㻌㼞㼍㼚㼓㼑㻯㼍㼘㼏㼡㼘㼍㼠㼕㼛㼚㻌㼛㼒㻌㼠㼔㼑㻌㼏㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㼛㼒㻌㼠㼔㼑㻌㼜㼞㼛㼏㼑㼟㼟㼕㼚㼓㻌㼠㼔㼍㼠㻌㼡㼟㼑㼟㻌㻰㻺㼟㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻭㻯㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㻔㼏㼘㼍㼟㼟㻌㼛㼒㻌㼠㼔㼑㻌㻿㼑㼍㻌㼕㼚㻌㼠㼔㼑㻌㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘㻌㼕㼟㻌㼜㼑㼞㼙㼕㼠㼠㼑㼐㻕㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻭㻌㼇㻑㼉㻯㼘㼍㼟㼟㻼㼞㼛㼜㼛㼟㼑㼐
㼙㼑㼠㼔㼛㼐

㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍 㻮㻌㼇㻑㼉㼁㼟㼑㻌㼛㼒㻌㻰㻺㼟㻼㼞㼛㼜㼛㼟㼑㼐
㼙㼑㼠㼔㼛㼐

㼁㼟㼑㻌㼛㼒㻌㻰㻺㼟㻮㼡㼕㼘㼐㼕㼚㼓㼟

㻤㻟㻚㻝㻣

㻤㻟㻚㻝㻣

㻡㻤㻚㻝㻥

㻡㻢㻚㻠㻝

㻿㼑㼍

㻥㻥㻚㻤㻟

㻥㻥㻚㻥㻥

㻥㻠㻚㻟㻡

㻥㻡㻚㻟㻤

㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘

㻥㻠㻚㻜㻠

㻤㻥㻚㻠㻟

㻥㻞㻚㻟㻜

㻣㻡㻚㻤㻟

㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚

㻥㻤㻚㻠㻜

㻥㻥㻚㻥㻥

㻥㻢㻚㻞㻥

㻤㻣㻚㻝㻜

㻿㼛㼕㼘

㻤㻝㻚㻝㻢

㻤㻟㻚㻝㻣

㻣㻥㻚㻡㻟

㻥㻞㻚㻤㻢

㼀㼛㼠㼍㼘

㻥㻜㻚㻠㻠

㻤㻤㻚㻞㻥

㻤㻤㻚㻜㻞

㻤㻠㻚㻞㻢

㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘㻌㼕㼚㻌㼠㼔㼑㻌㻹㼍㼚㼡㼍㼘㼘㼥㻌㼏㼘㼍㼟㼟㼕㼒㼕㼑㼐㻌㼙㼍㼜㻌㼕㼟㻌㼑㼤㼜㼞㼑㼟㼟㼑㼐㻌㼍㼟㻌㼠㼔㼑㻌㼏㼘㼍㼟㼟㻌㼛㼒㻌㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚㻯㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼠㼔㼑㻌㼡㼟㼑㻌㼛㼒㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻟㻴㼕㼓㼔㻌㼏㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑 㼞㼍㼠㼑㻌㼕㼟㻌㼛㼎㼠㼍㼕㼚㼑㼐㻌㼣㼔㼑㼚㻌㼡㼟㼕㼚㼓㻌㻰㻺㼟㻺㼑㼑㼐㻌㼠㼛㻌㼐㼑㼢㼑㼘㼛㼜㻌㼍㼚㻌㼍㼘㼓㼛㼞㼕㼠㼔㼙㻌㼠㼛㻌㼟㼑㼘㼑㼏㼠㻌㼠㼔㼑㻌㼜㼞㼛㼏㼑㼟㼟㻌㼕㼚㻌㼍㼏㼏㼛㼞㼐㼍㼚㼏㼑㻌㼣㼕㼠㼔㻌㼠㼔㼑㻌㼒㼘㼛㼛㼐㻌㼟㼕㼠㼡㼍㼠㼕㼛㼚㻌㼛㼒㻌㼠㼔㼑㻌㼟㼠㼡㼐㼥㻌㼍㼞㼑㼍㻮㼡㼕㼘㼐㼕㼚㼓㼟10㻿㼑㼍㻹㼍㼚㼡㼍㼘㼘㼥㻌㼏㼘㼍㼟㼟㼕㼒㼕㼑㼐㻌㼙㼍㼜㻌㼏㼞㼑㼍㼠㼑㼐㻌㼡㼟㼕㼚㼓㻌㼠㼔㼑㻌㼍㼑㼞㼕㼍㼘㻌㼜㼔㼛㼠㼛㼓㼞㼍㼜㼔 䈂 㼍㼚㼐㻌㼠㼔㼑㻌㼙㼍㼜㻌䈂㻌䈂㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚䈂㻌㻳㼑㼛㼟㼜㼍㼠㼕㼍㼘㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㻭㼡㼠㼔㼛㼞㼕㼠㼥㻌㼛㼒㻌㻶㼍㼜㼍㼚㻌䠖㼔㼠㼠㼜㻦㻛㻛㼟㼍㼕㼓㼍㼕㻚㼓㼟㼕㻚㼓㼛㻚㼖㼜㻛㼔㻞㻟㼠㼍㼕㼔㼑㼕㼥㼛㻙㼦㼛㼞㼠㻛䈂䈂㻌㼆㼑㼚㼞㼕㼚 㻰㼕㼓㼕㼠㼍㼘㻌㻭㼠㼘㼍㼟㻌㼆㼕㻝㻠㻿㼛㼕㼘㻿㼠㼡㼐㼥㻌㼛㼚㻌㼡㼟㼑㼒㼡㼘㼚㼑㼟㼟㻌㼛㼒㻌㼠㼔㼑㻌㼏㼛㼙㼎㼕㼚㼍㼠㼕㼛㼚㻌㼛㼒㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻠㻿㼠㼡㼐㼥㻌㼛㼚㻌㼡㼟㼑㼒㼡㼘㼚㼑㼟㼟㻌㼛㼒㻌㼠㼔㼑㻌㼏㼛㼙㼎㼕㼚㼍㼠㼕㼛㼚㻌㼛㼒㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻠㻯㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼡㼟㼕㼚㼓㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻟㻌㼟㼑㼠㻌㼕㼚㻌㼠㼔㼑㻌㼢㼕㼟㼕㼎㼘㼑㻌㼞㼍㼚㼓㼑㻯㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼡㼟㼕㼚㼓㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻟㻌㼟㼑㼠㻌㼕㼚㻌㼠㼔㼑㻌㼢㼕㼟㼕㼎㼘㼑㻌㼞㼍㼚㼓㼑㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻮㻭㼢㼑㼞㼍㼓㼑㻌㼛㼒㻌㻰㻺㼟㻌㼛㼒㻌㻿㼡㼜㼑㼞㼢㼕㼟㼑㼐㻌㼐㼍㼠㼍㻮㼍㼚㼐㻌㻝

㻲㼘㼛㼛㼐㼑㼐㻌㻿㼛㼕㼘㻌㼕㼚㻌㼠㼔㼑㻌㻹㼍㼚㼡㼍㼘㼘㼥㻌㼏㼘㼍㼟㼟㼕㼒㼕㼑㼐㻌㼙㼍㼜㻌㼕㼟㻌㼑㼤㼜㼞㼑㼟㼟㼑㼐㻌㼍㼟㻌㼠㼔㼑㻌㼏㼘㼍㼟㼟㻌㼛㼒㻌㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚㻯㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼠㼔㼑㻌㼡㼟㼑㻌㼛㼒㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻟㻮㼡㼕㼘㼐㼕㼚㼓㼟㻿㼑㼍㻹㼍㼚㼡㼍㼘㼘㼥㻌㼏㼘㼍㼟㼟㼕㼒㼕㼑㼐㻌㼙㼍㼜㻌㼏㼞㼑㼍㼠㼑㼐㻌㼡㼟㼕㼚㼓㻌㼠㼔㼑㻌㼍㼑㼞㼕㼍㼘㻌㼜㼔㼛㼠㼛㼓㼞㼍㼜㼔 䈂 㼍㼚㼐㻌㼠㼔㼑㻌㼙㼍㼜㻌䈂㻌䈂㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚㻿㼠㼡㼐㼥㻌㼛㼚㻌㼡㼟㼑㼒㼡㼘㼚㼑㼟㼟㻌㼛㼒㻌㼠㼔㼑㻌㼏㼛㼙㼎㼕㼚㼍㼠㼕㼛㼚㻌㼛㼒㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻠㻮㼍㼚㼐㻌㻠

㻲㼘㼛㼛㼐㻌㻿㼛㼕㼘

㻞㻡㻚㻥㻜

㻟㻜㻚㻞㻤

㻠㻜㻚㻠㻤

㻞㻞㻚㻣㻜

㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚

㻞㻡㻚㻜㻟

㻞㻥㻚㻝㻡

㻟㻤㻚㻤㻡

㻠㻠㻚㻠㻟

㻺㼛㻌㼐㼕㼒㼒㼑㼞㼑㼚㼏㼑㼟㻌㼕㼚㻌㻮㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻟㻰㼕㼒㼒㼑㼞㼑㼚㼏㼑㻌㼕㼚㻌㼞㼑㼒㼘㼑㼏㼠㼍㼚㼏㼑㻌㼕㼟㻌㼍㼜㼜㼑㼍㼞㼑㼐㼕㼚㻌㼠㼔㼑㻌㼚㼑㼍㼞㻙㼕㼚㼒㼞㼍㼞㼑㼐㻌㼞㼑㼓㼕㼛㼚㻰㼕㼒㼒㼕㼏㼡㼘㼠㻌㼠㼛㻌㼍㼏㼏㼡㼞㼍㼠㼑㼘㼥㻌㼏㼘㼍㼟㼟㼕㼒㼥㻌㼏㼘㼍㼟㼟㼑㼟㻌㼎㼥㻌㼡㼟㼕㼚㼓㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻟㻰㻺㼟㻌㼕㼚㻌㼎㼍㼚㼐㻌㻠㻌㼕㼟㻌㼡㼟㼑㼒㼡㼘㻌㼒㼛㼞㻌㼠㼔㼑㻌㼏㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㻌㻯㼛㼚㼏㼘㼡㼟㼕㼛㼚㼟㻯㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻌㼞㼑㼟㼡㼘㼠㻌㼎㼥㻌㼡㼟㼕㼚㼓㻌㼎㼍㼚㼐㼟㻌㻝㻌㼠㼛㻌㻟㻌㼟㼑㼠㻌㼕㼚㻌㼠㼔㼑㻌㼢㼕㼟㼕㼎㼘㼑㻌㼞㼍㼚㼓㼑㻰㼑㼢㼑㼘㼛㼜㼙㼑㼚㼠㻌㼍㻌㼙㼑㼠㼔㼛㼐㻌㼒㼛㼞㻌㼡㼚㼙㼕㼤㼕㼚㼓㻌㼙㼕㼤㼑㼘㼟㻌㼠㼔㼍㼠㻌㼡㼟㼑㼟㻌㼠㼔㼑㻌㻰㻺㼟㻌㼍㼚㼐㻌㼠㼑㼤㼠㼡㼞㼑㼟㻌㼍㼜㼜㼑㼍㼞㼑㼐㻌㼕㼚㻌㼞㼑㼙㼛㼠㼑㻌㼟㼑㼚㼟㼕㼚㼓㻌㼐㼍㼠㼍㻌㼠㼛㻌㼑㼟㼠㼕㼙㼍㼠㼑㻌㼍㼙㼛㼡㼚㼠㻌㼛㼒㻌㼐㼕㼟㼍㼟㼠㼑㼞㻌㼣㼍㼟㼠㼑㼟㻯㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㻔㼏㼘㼍㼟㼟㻌㼛㼒㻌㼠㼔㼑㻌㻿㼑㼍㻌㼕㼚㻌㼠㼔㼑㻌㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘㻌㼕㼟㻌㼜㼑㼞㼙㼕㼠㼠㼑㼐㻕㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻭㻌㼇㻑㼉㻮㼍㼚㼐㻌㻟

㼁㼟㼑㻌㼛㼒㻌㼎㼍㼚㼐㻌㼐㼍㼠㼍㻌㼕㼚㻌㼠㼔㼑㻌㼎㼛㼠㼔㻌㼢㼕㼟㼕㼎㼘㼑㻌㼍㼚㼐㻌㼚㼑㼍㼞㻙㼕㼚㼒㼞㼍㼞㼑㼐㻌㼞㼑㼓㼕㼛㼚㻌㼕㼟㻌㼓㼛㼛㼐㻌㼒㼛㼞㻌㼍㼏㼏㼡㼞㼍㼠㼑㻌㼏㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻿㼛㼕㼘䈂㻌㻳㼑㼛㼟㼜㼍㼠㼕㼍㼘㻌㻵㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻌㻭㼡㼠㼔㼛㼞㼕㼠㼥㻌㼛㼒㻌㻶㼍㼜㼍㼚㻌䠖㼔㼠㼠㼜㻦㻛㻛㼟㼍㼕㼓㼍㼕㻚㼓㼟㼕㻚㼓㼛㻚㼖㼜㻛㼔㻞㻟㼠㼍㼕㼔㼑㼕㼥㼛㻙㼦㼛㼞㼠㻛䈂䈂㻌㼆㼑㼚㼞㼕㼚 㻰㼕㼓㼕㼠㼍㼘㻌㻭㼠㼘㼍㼟㻌㼆㼕㻝㻠㻯㼘㼍㼟㼟㻮㼍㼚㼐㻌㻞

㻿㼠㼡㼐㼥㻌㼍㼞㼑㼍㻌㻮㻌㼇㻑㼉㼁㼟㼑㻌㼛㼒㻌㼎㼍㼚㼐㼟㻝㻌㼠㼛㻌㻠

㼁㼟㼑㻌㼛㼒㻌㼎㼍㼚㼐㼟㻝㻌㼠㼛㻌㻟

㼁㼟㼑㻌㼛㼒㻌㼎㼍㼚㼐㼟㻝㻌㼠㼛㻌㻠

㼁㼟㼑㻌㼛㼒㻌㼎㼍㼚㼐㼟㻝㻌㼠㼛㻌㻟

㻮㼡㼕㼘㼐㼕㼚㼓㼟

㻤㻟㻚㻝㻣

㻠㻝㻚㻞㻤

㻡㻤㻚㻝㻥

㻝㻞㻚㻡㻢

㻿㼑㼍

㻥㻥㻚㻤㻟

㻢㻝㻚㻝㻞

㻥㻠㻚㻟㻡

㻥㻜㻚㻜㻟

㻲㼘㼛㼛㼐㼑㼐㻌㼟㼛㼕㼘

㻥㻠㻚㻜㻠

㻡㻣㻚㻤㻣

㻥㻞㻚㻟㻜

㻢㻡㻚㻜㻞

㼂㼑㼓㼑㼠㼍㼠㼕㼛㼚㻥㻤㻚㻠㻜㻝㻜㻜㻚㻜㻜

㻥㻢㻚㻞㻥

㻥㻞㻚㻞㻟

㻿㼛㼕㼘

㻤㻝㻚㻝㻢

㻟㻣㻚㻣㻤

㻣㻥㻚㻡㻟

㻣㻠㻚㻢㻠

㼀㼛㼠㼍㼘

㻥㻜㻚㻠㻠

㻡㻞㻚㻝㻝

㻤㻤㻚㻜㻞

㻣㻝㻚㻤㻡

 㻭㻌㼙㼑㼠㼔㼛㼐㻌㼒㼛㼞㻌㼡㼚㼙㼕㼤㼕㼚㼓㻌㼙㼕㼤㼑㼘㼟 㼠㼔㼍㼠㻌㼡㼟㼑㼟㻌㻰㻺㼟㻌㼍㼚㼐㻌㼠㼑㼤㼠㼡㼞㼑㼟㻌㼣㼛㼡㼘㼐㻌㼎㼑㻌㼍㼜㼜㼞㼛㼜㼞㼕㼍㼠㼑 㼒㼛㼞㻌㼑㼟㼠㼕㼙㼍㼠㼕㼚㼓㻌㼘㼍㼚㼐㻌㼏㼛㼢㼑㼞㻌㼕㼚㼒㼛㼞㼙㼍㼠㼕㼛㼚㻚 㻲㼛㼞㻌㼍㼞㼑㼍㼟㻌㼟㼑㼠㻌㼕㼚㻌㼠㼔㼑㻌㼟㼠㼡㼐㼥㻘 㼏㼛㼚㼏㼛㼞㼐㼍㼚㼏㼑㻌㼞㼍㼠㼑㻌㼛㼎㼠㼍㼕㼚㼑㼐㻌㼎㼥㻌㼠㼔㼑㻌㼜㼞㼛㼜㼛㼟㼑㼐㻌㼙㼑㼠㼔㼛㼐㻌㼕㼟㻌㼍㼎㼛㼡㼠㻌㻥㻜㻌㻑㻘㻌㼍㼚㼐㻌㼠㼔㼑㻌㼜㼞㼛㼜㼛㼟㼑㼐㻌㼙㼑㼠㼔㼛㼐㻌㼕㼟㻌㼍㼎㼘㼑㻌㼠㼛㻌㼕㼙㼜㼞㼛㼢㼑㻌㼠㼔㼑㻌㼞㼑㼟㼛㼘㼡㼠㼕㼛㼚㻌㼛㼒㻌㼠㼔㼑㻌㼀㻴㻱㻻㻿㻌㼐㼍㼠㼍㻚 㼁㼚㼙㼕㼤㼕㼚㼓㻌㼙㼕㼤㼑㼘㼟㻌㼡㼟㼕㼚㼓㻌㼠㼔㼑㻌㼎㼍㼚㼐㻌㼐㼍㼠㼍㻌㼕㼚㻌㼠㼔㼑㻌㼢㼕㼟㼕㼎㼘㼑㻌㼍㼚㼐㻌㼚㼑㼍㼞㻙㼕㼚㼒㼞㼍㼞㼑㼐㻌㼞㼑㼓㼕㼛㼚㻌㼕㼟㻌㼓㼛㼛㼐㻌㼒㼛㼞㻌㼍㼏㼏㼡㼞㼍㼠㼑㻌㼏㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㻚 㻭㻌㼙㼑㼠㼔㼛㼐㻌㼒㼛㼞㻌㼡㼚㼙㼕㼤㼕㼚㼓㻌㼙㼕㼤㼑㼘㼟㻌㼜㼞㼛㼜㼛㼟㼑㼐㻌㼕㼚㻌㼠㼔㼑㻌㼟㼠㼡㼐㼥㻌㼑㼚㼍㼎㼘㼑㼟㻌㼠㼔㼑㻌㻴㼕㼓㼔㻌㼏㼛㼕㼚㼏㼕㼐㼑㼚㼏㼑㻌㼞㼍㼠㼑㻌㼣㼍㼟㻌㼛㼎㼠㼍㼕㼚㼑㼐㼁㼟㼑㻌㼛㼒㻌㼎㼍㼚㼐㻌㼐㼍㼠㼍㻌㼕㼚㻌㼠㼔㼑㻌㼢㼕㼟㼕㼎㼘㼑㻌㼍㼚㼐㻌㼚㼑㼍㼞㻙㼕㼚㼒㼞㼍㼞㼑㼐㻌㼞㼑㼓㼕㼛㼚㻌㼣㼛㼡㼘㼐㻌㼎㼑㻌㼍㼜㼜㼞㼛㼜㼞㼕㼍㼠㼑 㼒㼛㼞㻌㼍㼏㼏㼡㼞㼍㼠㼑㻌㼏㼘㼍㼟㼟㼕㼒㼕㼏㼍㼠㼕㼛㼚㼕㼙㼜㼞㼛㼢㼑㼙㼑㼚㼠㻌㼛㼒㻌㼠㼔㼑㻌㼞㼑㼟㼛㼘㼡㼠㼕㼛㼚㻌㼛㼒㻌㼞㼑㼙㼛㼠㼑㻌㼟㼑㼚㼟㼕㼚㼓㻌㼐㼍㼠㼍㻌㼣㼔㼑㼚㻌㼍㻌㼘㼍㼞㼓㼑㻙㼟㼏㼍㼘㼑㻌㼐㼕㼟㼍㼟㼠㼑㼞㻌㼛㼏㼏㼡㼞㼟㻚11

The 3rd UN World Conference on Disaster Risk Reduction in Sendai PUBLIC FORUM

Estimation Amount of Disaster Debris by Analyzing
the Aerial Images
Masahito Yamamoto
Professor, Graduate School of Information Science and Technology, Hokkaido University

Abstract:
In a massive earthquake, it becomes difficultfrom the coordination values of the house in

to know the situation of damage immediately

trimmed image. The images are transformed

the image so as to include just one house in a

because the accesses to the affected areas arefrom the color images to gray scale images andlost at the same time. Especially Tsunami

the edges in the images are extracted by theleaves much debris and breaks all thesobel filter. Then the line can be extracted byaccessible roads and houses. In order toapplying the medhod by Duda and Hart. Our

recover the cities from the disaster, it is

proposed method is shown in the followings.

required to arrange the infrastructure such as1) Image data are preparedground transportations and decide the location

2) Regions of houses are trimmed from theof a large number of temporary storages as

imagessoon as possible.

3) Characteristics are extracted from the

The aim of this presentation is to introduce

imagesour efforts to detect the damaged building and4) Classification that each house is collapsed

to estimate the amount of disaster debris

or not is performed(waste) by using aerial photographs just after5) Amounts of disaster debris are estimatedthe disaster and to discuss about the future
perspective and the limitation of the proposed

Fig 1 shows the result of estimating collapsed

methods.

houses. We can find that many houses are

In this study, we focus on the detection ofcollapsed and our proposed method almost cancollapsed houses caused by the disaster. If we

detect collapsed houses. Table 1 show thecan know the situation for the area of collapsed

actual percentage of Fig 1.

houses immediately, the amount of disaster
wastes or debris can be estimated. In order todetect whether a certain house is collapsed or

Table 1 Detection Results

not, we used aerial photographs and map data.
At first, our proposed method focuses on ahouse on the map image. Next, the system
trims aerial photograph in the same position

12

It is shown that our proposed method canestimate the disaster debris with high accuracy
comparing with existing methods.

References[1]

Hirayama,N.,

Kawata,Y,

Okumura,Y.

Quantitative Estimation of Disaster Debris forOperational Management after the Great East
Japan Earthquake. Japan Society of Material
Cycles and Waste Management Vol.23,No.1:3-9.
2012
[2]

The

Great

Reconstruction

East

SupportJapan

Earthquake

Research

Archive,

http://fukkou.csis.u-tokyo.ac.jp

Fig. 1[3] Cabinet Office, Geverment of Japan,

Classified Areas

http://www.bousai.go.jp/taisaku/pdf/shishin002.
pdf

From the table, we can see that the percentage
of correct answer is 0.667, Precision is 0.602,
and Recall is 0.670. In this evaluation method,
two types of reasons caused incorrectclassifications. One pattern is the proposed
method decides house “collapsed”, but actual
damage is not collapsed. This error occurs inthat the threshold of detecting straight lines by
Hough transform is not appropriate. Hence inorder to overcome the problem, it is necessary
to combine a different technique from linedetection. On the other hand, there are somecases that the proposed method decides thehouse is “not-collapsed”, but the actual
damage is collapsed. The reason why thesemisclassifications are occurred is that somehouses are collapsed with the flood above thefloor level and so roofs are not damages.

13

Background¾ For the recovery from the disaster, it isvery important to know the damage
status of the area

Estimation Amount of Disaster Debris byAnalyzing the Aerial Images

Estimation of disaster debris

Masahito Yamamoto
Professor,
Graduate School of Information Science andTechnology, Hokkaido University

Base unit

Temporal storages

Temporal facilitiesᅜ㐃㜵⅏ୡ⏺఍㆟

Estimation of disaster Debris

Damagearea

Wide-area treatment2ᅜ㐃㜵⅏ୡ⏺఍㆟

Transportation

2015/3/15

1

2015/3/15

Planning for recovering

Purpose¾ Automatic detection whether a certain building is destroyed
or notAmountof Debris

Destroyed?
or not?

For General house

0.62[ton/m2][Takatsuki et al.,1995][Hirayama et al., 2010]ᅜ㐃㜵⅏ୡ⏺఍㆟

14

2015/3/15
ᅜ㐃㜵⅏ୡ⏺఍㆟

4

2015/3/15

3

We can estimate the amount ofdisaster debris considering fordestroyed area

Procedure

Experimental Results
Precision of our proposed method are examined
Trimming of the map image

Experimental conditions are follows• Data set

Trimming of the areal image• Areal images after the disaster• Map data

• Images (three typical types of cities or towns)
Line extraction

Estimation of the amount of disaster debris

2015/3/15

5

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ᅜ㐃㜵⅏ୡ⏺఍㆟

6

Damaged area ؒ 12m2
Estimated amount ؒ7.5ton• Minami-Sanriku town
• Rikuzen-Takata city
• Kamaishi city

Extraction of characteristics
Detection of destroyed or notᅜ㐃㜵⅏ୡ⏺఍㆟

Evaluation criteria

Image Data

Archive of recovery support survey• Supporting group surveyed the damaged area after disaster• Disaster damage status of each building is available

Three types of cities or towns
Rikuzen-Takata city

Minami-Sanriku town

After disaster(11/3/12-13)

Red 䠖Washed away
Pink 䠖Totally destroyed
Blue : Seriously damaged
Green 䠖Partially damaged

Map data

After disaster (11/4/1)

Map data

Kamaishi city

Damaged area of Minami-Sanriku town

15

After disaster(11/5-12/3)

Map data

2015/3/15
ᅜ㐃㜵⅏ୡ⏺఍㆟

8ᅜ㐃㜵⅏ୡ⏺఍㆟

7

2015/3/15

Results (1/3) – Minami-Sanriku Townᐇ㦂⤖ᯝ䛻䛚䛡䜛ྛ㡿ᇦ䛾ซ౛ᐇ㝿䛾ಽቯ᝟ሗ
ಽቯ
㠀ಽቯ
Ar
Ao
Ag
Ab

 ṇ⟅⋡䠖ᥦ᱌ᡭἲ䛜⿕ᐖ䛾ุู䛻ᡂຌ䛧䛯㡿ᇦ䛾๭ྜ
S=Ar+Ab 㐺ྜ⋡ArPAr  Ao ෌⌧⋡

R

Actual Damages

Estimation

Ar
Ar  Ag

Destroyed

Non-Dest.

Toral

Destroyed

0.822(Re)0.012(Or)0.834

Non-Dest.

0.166(Gr)

0.000(Bl)

0.166

ィ

0.988

0.012

1.000

ᅜ㐃㜵⅏ୡ⏺఍㆟ᅜ㐃㜵⅏ୡ⏺఍㆟

Results (3/3) – Kamaishi city

Results (2/3) – Rikuzen-Takata city

Example of destroyed building

Example of destroyed building
Map data

Map data

Areal image

Actual Damages

ᅜ㐃㜵⅏ୡ⏺఍㆟

Areal image

Actual Damages

Destroyed

Non-Dest.

Toral

Destroyed

0.826(Re)0.083(Or)0.909

Non-Dest.

0.066(Gr)0.025(Bl)

0.091

ィ

0.892

0.108

1.000

Accuracy䠖0.850
Precision䠖0.908
Recall 䠖0.926

Estimation

11

2015/3/15

Accuracy䠖0.823
Precision䠖0.986
Recall 䠖0.832

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9

2015/3/15

Estimation

Areal image

10

ᥦ᱌ᡭἲ

Map data

16

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ᅜ㐃㜵⅏ୡ⏺఍㆟

Destroyed

Non-Dest.

Toral

Destroyed

0.288(Re)0.191(Or)0.479

Non-Dest.

0.142(Gr)0.379(Bl)

0.521

ィ

0.430

0.579

1.000

Accuracy䠖0.667
Precision䠖0.602
Recall 䠖0.670

12

ಽቯ
㠀ಽቯ

Example of destroyed buildingConcluding Remarks

Comparison with actual amount of disaster debris

KamaishiA) Actual Debris[ton]

68.2୓

65.9୓

15.7୓

B) EstimatedDebris[ton]

57.6୓

67.2୓

17.5୓

㻮㻛㻭

0.843

1.012

1.113

2015/3/15
ᅜ㐃㜵⅏ୡ⏺఍㆟¾ Proposed method can estimate the amount of disasterdebris more precisely comparing with the previous studies¾ More precise estimation methods are required because theproposed method can not always detect the destroyed areain the condition of flood above/under floor level

2015/3/15
ᅜ㐃㜵⅏ୡ⏺఍㆟

17

14

Rikuzen-Takata

13

Minami-Sanriku

The 3rd UN World Conference on Disaster Risk Reduction in Sendai PUBLIC FORUM

Analysis of aerial photographs and satellite images for theestimation of mega-disaster waste in devastated areas
Issues of Disaster Waste Transport Planning
Kunihiro Kishi
Associate Professor, Graduate School of Engineering, Hokkaido University

1. Introduction
After the East Japan Great Earthquake

2．Operation “Teeth of a Comb”

Disaster in 2011, municipalities in disasterjust wide enough for a car to get through.

area are steadily advance reconstruction. As

From the day following the earthquake, “roadfor disaster waste disposal, about 20 million

clearance” works (Operation “Teeth of aton of disaster waste and 11 million ton ofComb”) were started[1].

tsunamidepositin239

The operation was named after the shape ofprefectures,

those

the road network under Operation, where thedisaster waste disposal completed in March

Tohoku Expressway and Route 4 run inland

2014 with the exception of a part of

Tohoku from north to south, and a number ofFukushima.

debris-covered roads to be cleared extend

municipalitiesofoccurred

The road clearance works secure a route13

In order to develop reconstruction, it isfrom the expressway and Route 4 toward theneedless to say that rapid disaster waste
disposal is quite important.
viewpointofcoast. The road clearance works were

From thetransportation,

beforeestablishing disaster waste transport system,
rapid recovery of transportation network isimportant,

such

as

road

clearancebyremoving debris.
This paper discusses issues of disaster
waste transport planning, especially focusing
on road clearance project. Operation “Teethof a Comb” and the discussion of Hokkaido
about disaster prevention planning are applied
as case studies.

Reference: Website of Tohoku Regional Bureau, MLIT

Fig.1 Outline of Operation “Teeth of a Comb”

18

Fig.2 Road clearance work in Rikuzen-Takata

almost completed by March 18, 2011: 97% of

Fig.4 Required time for road clearance (snowy

Route 45 was reopened to traffic, etc.

season)From

March 18, the project proceeded to thewinter seasons, it takes more than twice oftemporary restoration phase.

non-snowy season, because amount of debris
includes snow and ice (Fig.4).

3．Examination Case of Road Clearance
4 ． Issues of Disaster Waste Transport

Plan in Hokkaido
Hokkaido Regional Development Bureau,

Planning
Many municipalities are making the plan

Ministry of Land, Infrastructure, TransportandTourism

established

for disposal of disaster waste.

“Investigative

Ministry ofcommittee of rehabilitation for tsunami

the Environment provides a guideline fordisaster in winter” in 2013, and I joined thisdisaster waste provision[3].

This guideline

committee. This committee simulated how

includestransport

long it takes to clear debris from the roads in

However in face, it seems difficult to secure

the case of tsunami disaster in Hokkaido, andenough trucks in the case of disaster as

discussed how construction machines can be

Hokkaido Regional Development Bureau

secureddiscussed.

bywide-range

cooperationin

Hokkaido [2].

disaster

waste

plan.

Plans of Road clearance anddisaster waste disposal should be discussed

As a case study, Figure 3 is the result ofwith severe conditions and researches on

simulation about road clearance when 50 %methodology that supports making plans areof construction machines can be used.

takes 4 days and 20 hours to complete roadrequired.
References

clearance by the support of construction[1] Tohoku Regional Bureau, Ministry of Land,machines from other areas. In the case of

Infrastructure, Transport and Tourism,

It“Earthquake Memorial Museum”, URL:
http://infra-archive311.jp/[2] Hokkaido Regional Development Bureau,
Ministry of Land, Infrastructure, Transport andTourism, “Report of rehabilitation for tsunami
disaster in winter”(only in Japanese) (2014)

Fig.3 Required time for road clearance(non-snowy[3] Ministry of the Environment, “Guideline ofseason)disaster waste provision”(2014)19

Introduction•
The 3rd UN World Conference on Disaster Risk Reduction in Sendai
PUBLIC FORUM
Analysis of aerial photographs and satellite images for the estimation ofmega-disaster waste in devastated areas•

Issues of Disaster Waste Transport
Planning
2015/03/15••

Kunihiro KISHI, Ph.D
Associate Professor

Hokkaido University

1

After the East Japan Great Earthquake Disaster in 2011,
municipalities in disaster area are steadily advancereconstruction.
As for disaster waste disposal, about 20 million ton ofdisaster waste and 11 million ton of tsunami deposit
occurred in 239 municipalities of 13 prefectures, thosedisaster waste disposal completed in March 2014 with the
exception of a part of Fukushima.
In order to develop reconstruction, it is needless to say that
rapid disaster waste disposal is quite important.
From the viewpoint of transportation, before establishingdisaster waste transport system, rapid recovery oftransportation network is important, such as road clearance
by removing debris.

0

Introduction(2)2Operation “Teeth of a Comb”• Operation “Teeth of a Comb”• The discussion of Hokkaido about disasterprevention planning are applied as case studies.
• Ministry of the Environment provides a guideline fordisaster waste provision.

•• This paper discusses issues of disaster wastetransport planning, especially focusing on roadclearance project.

•

•
•

20

The road clearance– To secure a route just wide enough for a car to getthrough.
From the day following the earthquake, “road clearance”
works (Operation “Teeth of a Comb”) were started.
The operation was named after the shape of the road
network under Operation,
where the Tohoku Expressway and Route 4 run inland
Tohoku from north to south, and a number of debriscovered roads to be cleared extend from the expressway
and Route 4 toward the coast.

3

Outline of Operation “Teeth of a Comb”

Examination Case of Road Clearance
Plan in Hokkaido

45• Hokkaido Regional Development Bureau, Ministryof Land, Infrastructure, Transport and Tourism
established “Investigative committee of
rehabilitation for tsunami disaster in winter” in 2013
• I joined this committee.
• This committee simulated how long it takes to clear
debris from the roads in the case of tsunami
disaster in Hokkaido, and discussed how
construction machines can be secured by widerange cooperation in Hokkaido

6

Result of road clearance simulation

Input data for road clearance simulation

Case study) maritime area along Pacific Ocean, non-snowy
season, estimated amount of debris=110,000m3

217

Guideline for disaster waste provision by
Ministry of the Environment

8

Result of road clearance simulation(2)• Many municipalities are making the plan for
disposal of disaster waste.
• Ministry of the Environment released in 2014
• Related to disaster waste transport plan
– Cooperative framework with General
Constructors Association and Industrial Waste
Association– Make the list of trucks for disaster waste transport– Estimate amount of disaster waste and required
number of truck– Route plan considering traffic congestion

Case study) maritime area along Pacific Ocean, snowy season,
estimated amount of debris=176,000m3

10Issues of Disaster Waste Transport Planning
•

•

•
•

9

It seems difficult to secure enough trucks in the case ofdisaster– as Hokkaido Regional Development Bureau discussed.
– Competitive situation to secure construction machines indisaster area
Who should coordinate, municipalities or National
government?

Plans of Road clearance and disaster waste disposal should
be discussed with severe conditions
Researches on methodology that supports making plans are
required.
22

第 3 回国連防災世界会議 in 仙台 (2015 年 3 月 15 日)

The 3rd UN World Conference on Disaster Risk Reduction in Sendai PUBLIC FORUM

Analysis of aerial photographs and satellite images for theestimation of mega-disaster waste in devastated areas
Building of Water Resource Management System with Integrated
Water Cycling Simulation
Hiroshi Yamamura（Assistant Professor of Laboratory of Sustainable Water Metabolic System Engineering,
Faculty of Science and Engineering, Chuo University）

Abstract:
You may think that water flows from thereal fluid on the virtual. Where and how

high to low, but in fact sometimes the water

predicted by simulating the movement ofclimb toward the higher place depending onwater through the various physical andthe conditions. Where and how much waterchemical parameters described before and its

flows is determined by "hydrodynamics".

accuracy improved year by year with the

Until now, various researchers predict theincrease of the performance of computer.

water flow by the use of mathematics andThus, the computer simulation makes us

physics and have been found various natural

possible to see the current blind world which

laws. For fluid dynamics, most importantis impossible to see by our naked eyes.

much water flow is now possible to be

and fundamental law is "continuous rule",
which indicates “the volume of influent haveto that of effluent ". Except for thecontinuous rule, there are some rules toexpress the natural rules in accordance withnatural rules. For examples, momentum andenergy of the water is the other essential
water flow equation. By assembling theseequations, the water flow will be expressed

One such example is the ground water flow.

in the virtual space.

Groundwater flow is strongly influenced by

In the late 20th century, the techniques ofthecomputational

particles and the water, the water viscosity

fluid

dynamics

rapidlydeveloped and became possible to create a23interaction

between

grain

size

of

第 3 回国連防災世界会議 in 仙台 (2015 年 3 月 15 日)

model".

and certainly geology. Therefore, it is very
difficult to predict the ground water flow
through the computer simulation. Moreover,because the ground water are always
supplied from surface water, it is necessary
to create an integrated model of groundwater and surface water, which makes moredifficult to make a simulation model ofground water.

Well, let us think about what's possible by
the use of this model. First question weshould ask is that do you know how muchwater resources in the current Japan? The
amount of surface water is now possible toquantify by using the GPS or some tools.
However, the abundance of underground isis

still unknown so far, despite that somemonitored by digging some holes from thestudies showed the volume of ground water

ground into the basement and directlyis much larger than that of surface water.

measured where and how much water flow.

Now, we can predict the total amount of

In recent years, Geosphere Environmental

water

Technology

thesimulation. If we know the accurate amountdevelopment of water circulation modelof water resources, the value of water could

which

and

be visualized and appropriate doctrine would

underground water by using a multiphasebe carried out to protect water resources in

multicomponent fluid system. This modelterms of quality and quantity.

could visualize the very complex groundIn addition, the visualization of undergroundwater flow. Simulation software are now

water would be useful to predict thecommercially available as "GETFLOWS",

sediment disaster. The clarification of theand Geosphere Environmental Technology

ground water flow will inform the place that

Corporation

thewill be collapse with the heavy rain. The

visualization of ground water current of allcomputer simulation makes us see theunderground space in Japan, as the national“future” in unpredictable future world!

Traditionally,

project

thegroundwater

Corporation

includes

is

“Open

succeeded

both

now

Land,

flow

groundpreparingwater

circulation

24

resource

in

Japan

throughtheAcknowledgement
We gratefully acknowledge the financial support of
Sendaikankyokaihatsu Co.Ltd.

謝辞
本講座に多大の御支援を賜る
仙台環境開発株式会社に深甚なる感謝の意を表する