1‐ Introduction

2-­ Sensor networks, and applications

3­‐ Sensor network deployment, algorithms, and geometrical issues

4-­ Sensor’s sensing models

5­‐ Communication models in sensor networks

6- Spatial coverage in geosensor networks

7­‐ Sensor network deployment optimization algorithms

7­‐1­‐ Global approaches

7-1-1- Simulated Annealing (SA)

7­‐1-2­‐ Covariance Matrix Adaptation Evolution Strategy (CMA-ES)

7-1-3- Genetic Algorithms (GA)

7­‐2­‐ Local approaches

7­‐2­‐1­‐ Vector-based algorithms

7-2-2- Voronoi-based algorithms

- Voronoi diagram and Delaunay triangulation geometric structures 

- Role of Voronoi diagram and Delaunay triangulation on the estimation of coverage in sensor network deployment

7-2-3- Minimax algorithm

7-2-4- Hybrid algorithms

8- The concept of context-aware in sensor network deployment

8-1- Categories of context and contextual information modeling in sensor networks

8-2 Context-aware functions and architectures in sensor networks 

8-3- Introducing a conceptual framework for sensor network deployment using Voronoi Diagram and contextual information

8.4- Introducing a local context-aware optimization algorithm 

9- Integration of spatial information in sensor deployment methods

10- Sensor network deployment optimization based on 3D city models

11- Coverage estimation using 3D city models in sensor networks

12- Impact of geospatial information quality of the sensor network coverage estimation.

Sensor networks are groups of low cost and multi-­‐function sensors collaborating with each other to sense different spatio­‐temporal phenomena in the environment. The efficiency of sensor networks may be constrained by the environment in which they are deployed as well as the sensors’ specifications such as limitations of their sensing range, battery power, connection ability, memory, and computation capabilities. These constraints affect the network coverage and may result in holes in the sensing area. Then, an efficient deployment of a sensor network can improve the coverage of the network. Sensors could be deployed either randomly or based on a predefined distribution over the region of interest. Several optimization methods (i.e., global or local, deterministic, or stochastic) have been proposed to increase the coverage of sensor networks. Some methods use general optimization techniques, whereas some others consider the problem as a geometric issue and use the structures and tools existing in computational geometry. Most of optimization algorithms often rely on oversimplified sensor models, and do not consider contextual information or spatial models in their deployment optimization methods. On the other hand, spatial models are simplified representations of a complex reality, and hence are inherently uncertain. So, the impact of the spatial data quality on the optimization of a sensor network and its spatial coverage is an important issue.

This workshop is intended to provide participants with an overview on the fundamental concepts related to the geosensor networks and their deployment and applications. More specifically the following main topics will be covered during the workshop

1­‐ Fundamentals of sensor networks 

2­‐ Existing algorithms on sensor network optimization

3‐ Context-­‐aware optimization framework for geosensor networks

4­‐ Impact of the quality of spatial data on optimization of sensor networks

The workshop will provide participants with the opportunity to exchange and discuss with the organizer on the latest development in the field of geosensor networks and related issues. All attendees to the workshop will receive a certificate for their participation in the activity.

Prof. Mir Abolfazl Mostafavi

He is full professor at the Department of geomatics, Laval University since 2004. He is Scientific Director of  Centre for Research in Geomatics (CRG) (2010-2013) Director of the Mobile Geospatial Augmented Reality Laboratory (REGARD) at Laval University. Since March 2013, he leads the Convergence Network (réseau convergence de l’intelligence geospatial pour innovation) at Laval University which mission is mainly geospatial knowledge and technology innovation and transfer. He is the chair of an international working group (ISPRS ICWG II/IV) on Semantic Interoperability and Ontology for Geospatial Information within the International Society of Photogrammetry and remote Sensing. He has been a member of the GEOIDE network for more than 10 years. His research activities are mainly focused Geographic information sciences. He currently leads research activities in spatial data modeling and representation, dynamic GIS, spatial ontologies, semantic interoperability, semantic sensor web, geossensor networks, sapatial accessibility, spatial data quality and uncertainty, geosimulation and geospatial virtual and augmented reality.

***

Meysam Argany

He has received the B.Sc. degree in surveying and Geomatics engineering and the M.Sc. degree (with the first rank among all graduate students) in remote sensing from the Faculty of Engineering, University of Tehran, Tehran, Iran, in 2004 and 2007, respectively. He is currently working toward the Ph.D. degree in the Geographic Information System (GIS) under the supervision of Dr. Mostafavi and Dr. Gagné at the Département des Sciences Géomatiques, Faculté de Foresterie, de Géographie et de Géomatique, Université Laval, Québec, QC, Canada. He was a Remote Sensing Specialist at Mahab Consulting Engineers, Tehran, Iran and a Research Assistant with the Remote Sensing Laboratory, Geomatics Department, University of Tehran, Tehran, Iran. His current research interests are modeling, analysis, and optimization of algorithms for deployment and coverage analysis of geosensor networks. Mr. Argany is a member of the Canadian GEOmatics for Informed Decisions Network of Centres of Excellence (GEOIDE).

Structure of the talk:

1. 1.     Fundamentals
   - Theory pinhole camera model
   - Camera calibration / undistortion
   - Pose estimation
   - Bundle adjustment
   
   2. 3D - Sparse Reconstruction
   - n-View feature matching
   - Multi ray intersection
   
   3. 3D - Dense Reconstruction
   - Epipolar Geometry / Fundamental matrix
   - Planesweep approach
   - Generation of Disparity Space Image
   - Image Matching - cost functions
   - Adaptive Support Windows
   - Cost aggregation / Regularization techniques
     (WTA, SGM, MRF, Total Variation)
   
   4. Post-Processing
   - Consistency check (LR-check)
   - Outlier filter
   - Interpolation
   - Sub-disparity refinement
   - 2.5D depthmap fusion
   - 3D fusion
   
   5. 3D Modeling
   - Meshing of point clouds / DSMs
   - Mesh simplification for data reduction
   - Multi-view texturing

 

Georg Kuschk received his Dipl.-Inform. degree at Martin-Luther Universitaet Halle, Germany in 2008, before spending some time in the augmented reality industry. Since 2011 he has been a computer vision researcher at the Institute of Remote Sensing of the German Aerospace Center (DLR) in Oberpfaffenhofen near Munich and is working on his PhD thesis.

http://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-5242/8788_read-25799/sortby-lastname/

Part 1: Georeferencing ( by Dr. Mehdi Ravanbakhsh)

• High Resolution Sensor Characteristics/ What changed with High Resolution Satellite imagery


• Rigorous sensor model: Coordinate systems & transformations


• Adaptation of rigorous sensor model to pushbroom scanners


• Practical motivation for a generic sensor model


• Vendor specific adjustment/ ALOS PRISM


• Overview of RPC sensor orientation model


• Original motivation for rational functions


• RPC bias error compensation


• Georeferencing  experience with 1m satellites


• Characteristics of GeoEye-1


• Accuracy tests with GeoEye-1 stereo & triplet imagery, Hobart


• Georeferencing of GeoEye-1 stereo imagery,Tsukuba


• Georeferencing of WorldView-2 stereo, Melbourne


• Strip Adjustment of HRSI: Concept and approach


• Strip Adjustment: Merged camera and orbit data


• Strip Adjustment: Long strips of ALOS PRISM


• Strip Adjustment: Long strips of QuickBird 


• Concluding Remarks

Part 2: DSM generation (by Dr. Karsten Jacobsen)

• Matching based on optical satellite imagery


• Cross correlation, least squares matching


• Pixel based matching


• Definition of accuracy, frequency distribution of discrepancies


• Experience with image matching – problems, limitations, accuracy


• Matching based on satellite SAR


• INSAR, Radargrammetry


• Comparison with matching based on optical images


• Characteristics and accuracy of existing height models


• Filtering of digital surface models

A BriefcBiography of Dr. Karsten Jacobsen:

 

-     Studied Surveying Engineering, Leibniz University of Hannover

-     Doctoral thesis about bundle block adjustment, Leibniz University Hannover 1980

-     Author of program system BLUH for bundle block adjustment, orientation of satellite scenes, analysis and post-processing of height models

-     Retired from position as Academic Director of the Institute of Photogrammetry and Geoinformation, Leibniz University Hannover, but in research and development still active as before

• 1996 – 2000 chairman of the Working Group I/3 “Sensors and Platforms for Topographic Survey” of the International Society of Photogrammetry and Remote Sensing – founding chair of this working group which is continued up to now with slightly different titles


• 2000 - 2004 chairman of the Working Group I/5 “Platform and Sensor Integration” of the International Society of Photogrammetry and Remote Sensing.


• 2004 - 2008 chairman of ISPRS WG I/5 “Geometric Modelling of Optical Spaceborne Sensors and DEM Generation”


• 2008-2012 co-chair of ISPRS WG I/4 “Geometric and Radiometric Modelling of Optical Spaceborne Sensors”


• Since 2012 co-chair of ISPRS WG I/4 “Geometric and Radiometric Modeling of Optical Airborne and Spaceborne Sensors”


• Since 2009 co-chair of German Society of Photogrammetry and Remote Sensing Working group “Sensors and Platforms”


• 2002 – 2009 chairman of EARSeL Special Interest Group “3D-Remote Sensing”, since 2009 co-chair, Founding chair of this special interest group
  
  
  
  • Honorary member of EARSeL (European Association of Remote Sensing Laboratories)
  
  
  • Major topics of research:  numerical photogrammetry, especially orientation of aerial and space imagery; DEM generation by image matching; analysis and post-processing of height models, mapping with aerial and space images.
  
  
  • Giving training and cooperation in these areas in approximately 40 countries
  
  
  • More as 300 publications in above mentioned areas
  
  
  • Reviewer for most of the English and German periodicals in remote sensing
  
  
  
  

A Brief Biography of Dr Mehdi Ravanbakhsh:

Dr Ravanbakhsh is currently a Senior Research Fellow in the School of Mathematical and Spatial Sciences at RMIT University. He received the Ph.D. degree from Leibniz University of Hannover, Hannover, Germany, in 2008, and joined the CRC for Spatial Information in the same year as a research fellow and project leader. His work over the past five years has concentrated upon various aspects of geospatial data generation including metric exploitation of aerial and satellite imagery, automated feature extraction from imagery and ranging data, and surface reconstruction from multi-view imagery and laser scanning data.

Dr Ravanbakhsh has presented over 20 seminars, oral and poster presentations at various conferences and research organisations and published more than 35 scholarly papers in ISI journals and international conferences. He has attracted research funding from governmental organisations and industry in Australia and Germany.

His research work has been recognised through a number of scholarships and awards including the prestigious American Society of Photogrammetry and Remote Sensing (ASPRS) Talbert Abrams Award (first honourable mention) in 2012 and the German Academic Exchange Service (DAAD) scholarship in 2004 and 2007.  

Dr Ravanbakhsh is an active member of professional organisations such as American and German Society of Photogrammetry and Remote Sensing, and has served as a reviewer of the Photogrammetric Engineering and Remote Sensing (PE&RS) journal published by the American Society of Photogrammetry and Remote Sensing. He has the role of the secretary of ISPRS Working Group I/4 for the period 2012-2016 and served as a scientific committee member of a number of ISPRS workshops and conferences.

 

Description

- Earth Deformations

- Rheology
--- Rheological models
----- Elastic deformations
----- Viscoelastic deformations
------- Maxwell model
------- Kelvin model

- Dislocation
--- Forward problem
--- Inverse problem

- Geodetic constraints
--- GPS
--- InSAR

- Interseismic strain accumulation and stress buildup
--- Coupling on seismogenic zones
----- locked, stick-slip
----- free slip
----- partially coupled
----- realistic models
----- Evaluation of earthquake potential
- Co-seismic displacement fields
--- Earthquake rupture
--- slow earthquakes
- Post-seismic displacement fields
--- Viscoelastic stress relaxation
--- After-slip

- Examples
--- Application of Geodetic data to constrain rheological models of
inter-, co- and post-seismic displacement fields
--- Sensitivity analysis
--- Estimation of rheological parameters
--- Makran subduction zone
--- Andean subduction zone
--- Cascadia subduction zone

- Introduction of few softwares
--- Semi-analytical solution: psgrn and pscmp from Dr. R. Wang
--- Numerical solutions: finite elements

Amir Abolghasem studied Surveying Engineering at K.N.Toosi University of Technology, Tehran, from 1983 to 1988. He graduated later in 1994 from the same faculty with M.Sc. in Geodesy.

In 1995 he moved to the University of Stuttgart and started a Ph.D. program in Geodetic Science, where he applied geodetic results as boundary conditions in a numerical elastic and viscoelastic models of co- and post-seismic deformation fields. He finished his Ph.D. in 2000 and continued his research for another 2 years at the Geodetic Institute of the University of Stuttgart.

He was awarded a postdoc fellowship by GFZ-Potsdam in 2002 and joined the Collaborative Research Project entitled "Mountain building processes in the Andean subduction zone". Here he continued the application of geodetic observations as boundary conditions of finite element models in estimating earth rheology.

In 2005 he moved to the University of Hanover and continued his research at the Geology Institute.

Early 2007 he was a research fellow at the German Geodetic Research Institute for a short timee. Immediately afterwards he was appointed by the Department of Earth and Environmental Sciences, Ludwig-Maximilians-University of Munich on a permanent position.

http://www.geologie.geowissenschaften.uni-muenchen.de/personen/wissenschaftler/abolghasem/index.html

برنامه کارگاه تخصصی پیش بینی زلزله و سنجش از دور/سامانه اطلاعات مکانی

هماهنگ کننده: دکتر مهدی زارع، دانشیار پژوهشگاه بین المللی زلزله شناسی و مهندسی زلزله

مهر ماه- 1392

مقدمه: بر اساس آخرین پژوهشهای انجام شده در ایران در زمینه کاربرد فناوری های نوین در سنجش از دور و سامانه اطلاعات مکانی برای پیش بینی زلزله، دستاورد های کسب شده در ایران توسط محققان ایرانی در این زمینه به ویژه در 5 سال اخیر در این کارگاه مرور می شود. ضمنا اخرین گزارش در زمینه ماهواره زلزله شناسی ایران که پروژه و برنامه مشترک سازمان پژوهشهای علمی و صنعتی و پژوهشگاه بین المللی زلزله شناسی  و مهندسی زلزله است در این کارگاه مورد بررسی قرار می گیرد.

 

حاضران:

1-      دکتر مهدی زارع: / پژوهشگاه بین المللی زلزله شناسی و مهندسی زلزله / عنوان: پیش بینی زلزله در ایران، دستاورد های نو بر پایه سامانه اطلاعات جدیدا توسطه داده شده در ایران و رهیافتهای جدید در پیش بینی زلزله

2-      مهندس حسن انتظاری / رئیس پزوهشکده فضایی کشور / ماهواره زلزله شناسی ایران ، بررسی اهداف و دستاورد ها

3-      دکتر حمید ذعفرانی: / پژوهشگاه بین المللی زلزله شناسی و مهندسی زلزله / پیش بینی زلزله بر پایه داده های لرزه خیزی و مدلهای هوشمند

4-      مهندس مسعود مجرب / دانجشوی دکتری، دانشکده فنی دانشگاه تهران / پیش بینی زلزله بر پایه مدل M8 ، یافته ها و دستاورد ها در فلات ایران

5-      مهندس محمد طالبی / دانجشوی دکتری، پزوهشگاه بین المللی زلزله شناسی و مهندسی زلزله / پیش بینی زلزله بر پایه روش پیشیابی در فلات ایران

 

The contents include
 -       An overview of the uncertainty issue in the context of GISci and the research and development in this area;
 -       An introduction to the mathematical foundations for handling uncertainties;
 -       Uncertain relationships and uncertainties generated in spatial analysis;
 -       Fuzzy topology-based methods which extends research beyond classical statistics;
 -       Presentation of research from different approaches, including confidence and probability distribution solutions;
 -       Solutions to the issues on presenting uncertainties by visualization of uncertainties and metadata.

When compared to classical sciences such as math, Geographical information science (GISci) is the new kind on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling for spatial data and spatial analysis is one branch of that theory.
Principles of modeling uncertainties in spatial data and spatial analyses outline the foundational principles and supplies a firm grasp of the disciplines’ theoretical underpinnings. This course delineates how GIS can be a better tool for decision making and demonstrate how the methods covered can be used to control and the data quality of GIS products. 

Dr Shi is a Professor in GIS and remote sensing, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University. He obtained his doctoral degree from University of Osnabrück in Vechta, Germany in 1994. His current research interests include GIS and remote sensing, uncertainty and spatial data quality control, image processing for high resolution satellite images. He has published some 400 research articles (including over 80 SCI papers) and 10 books.