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Connectivity, Information & Intelligence Lab

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About Us

Pioneering AI and computer vision solutions for resilient infrastructure in a changing climate. We harness satellite data and advanced analytics to monitor, predict, and safeguard the complex networks that power our cities, our economy, and our future.

Research

Research

Here are some examples of our ongoing research projects in Applied Machine Learning for monitoring and managing infrastructure and natural resources.

GridEyeS: Space-level AI for Infrastructure Monitoring

GridEyeS is an innovative vegetation monitoring framework that leverages satellite imagery and machine learning to streamline infrastructure maintenance. By automating vegetation analysis along roads and power lines, GridEyeS reduces the need for costly and time-consuming ground patrols, helicopter, or drone inspections. Supported by the European Space Agency, in collaboration with StormGeo, eSmart Systems, Linja (a Norwegian electric company), and Nova Scotia Power (a Canadian electric utility), GridEyeS aims to revolutionize asset monitoring practices for utility companies worldwide, ultimately improving safety and efficiency. This cutting-edge solution showcases the potential of AI and remote sensing in managing critical infrastructure.

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DisasterView: Disaster Impact Assessment using High Resolution Satellite Images

DisasterView is a groundbreaking project that harnesses the power of AI and high-resolution satellite imagery to rapidly assess the impact of natural disasters. By developing advanced machine learning algorithms to automatically analyze multispectral and SAR data, DisasterView provides emergency responders with crucial, timely information about affected terrain, infrastructure, and populations. This innovative framework enables more efficient allocation of limited resources in the aftermath of disasters, ultimately saving lives and accelerating recovery efforts. In collaboration with the Resilient Infrastructure & Disaster Response Center (RIDER) at Florida State University, USA, DisasterView successfully assessed the impacts of different Hurricanes in the Tallahassee area, Florida, demonstrating the immense potential of AI-driven disaster response solutions.

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TreeWatcher: Tree Species Classification Using Satellite Imagery

TreeWatcher is an innovative project that revolutionizes tree species classification by leveraging cutting-edge machine learning techniques and high-resolution multispectral satellite imagery. This groundbreaking approach offers a cost-effective and efficient alternative to traditional field surveys and expensive remote sensing methods, such as LiDAR and aerial imaging. By accurately identifying tree species from space, TreeWatcher provides critical information for diverse applications, including forestry management, wildfire prevention, and infrastructure risk assessment. Validated in Norwegian and Italian forests, TreeWatcher demonstrates the immense potential of AI-driven solutions in overcoming the challenges of monitoring vegetation in vast, inaccessible areas, ultimately promoting more informed decision-making and sustainable land use practices.

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SatFuse: SAR - Optical Fusion for Multi-Modal Remote Sensing 

SatFuse is a pioneering project that combines the strengths of synthetic aperture radar (SAR) and optical satellite imagery to enhance remote sensing capabilities. By developing innovative pixel-wise and feature-wise methods for optical-SAR image co-registration, SatFuse enables the creation of rich, multimodal datasets that provide unparalleled insights into Earth's surface. This fusion of complementary information from SAR and optical sensors allows for more accurate and comprehensive monitoring of forest structure and risk assessment. In collaboration with Kongsberg Satellite Services (KSAT), SatFuse's cutting-edge approach to data integration pushes the boundaries of remote sensing, paving the way for improved decision-making in forestry management, environmental conservation, and beyond.

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AI4Hydro: AI Powered Forecasting for Hydro-Power 

AI4Hydro is a cutting-edge project that revolutionizes hydropower scheduling by harnessing the power of artificial intelligence. In collaboration with the Department of Electric Energy at NTNU, we are developing AI-powered short-term scheduling tools that integrate hydrological and meteorological data to address the complex spatiotemporal interdependencies among cascaded reservoirs. By digitalizing hydropower scheduling, AI4Hydro aims to quantify the benefits of this transformation and create a comprehensive digital platform that seamlessly integrates AI into various stages of hydropower scheduling models. This project explores the coupling principles between strategic and operational modeling, ultimately enhancing the efficiency and resilience of the Norwegian power system in the face of climate change.

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Co-Resilience: Multi-Domain Infrastructure Networks Resilience

Most studies on resilience treat it as a single dimension attribute of a system or investigate the different dimensions of the resilience separately without considering its multi-domain nature. We developed an advanced causal inference approach combined with machine learning to characterize the spatiotemporal and multi-domain vulnerability of an urban infrastructure, coined as "co-resilience.'' We performed resilience assessments for combined electricity and transportation networks by considering the meteorological, topographic, and demographic attributes of Tallahassee, Florida after Hurricane Hermine (2016) and Hurricane Michael (2018).

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Co-Mobility: Causal Inference for Complex Multi-Layer Networks

Urban mobility and electricity demand are multidimensional characteristics of city infrastructure including places, people, and information. The emergence of electric vehicles creates even more interdependency between electricity and transportation networks. Therefore, the study of electricity and transportation systems should go beyond an individual network and merge with other networks. However, understanding relationships between driving patterns, electricity demand, weather conditions, and demographics is a complex problem that is out of reach for classic machine learning. We tackle such a complex problem by introducing the "Co-Mobility" concept based on the causal Bayesian multi-network models. We have implemented our framework on actual data from the City of Tallahassee, Florida.

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Team

Team

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Prof. Reza Arghandeh

 

Director, Ci2Lab

Professor in Data Science, Department of Comp Sci. and Elect Eng.

Leader, Data Science Group

Western Norway University of Applied Science (HVL)

Lead Data Scientist, StormGeo 

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Dr. Mojtaba Yousefi

Associate Professor, Department of Comp Sci. and Elect Eng.

Western Norway University of Applied Science (HVL)

Academic Advisory Board

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Dr. Eren Erman Ozguven

 

Associate Professor, Department of Civil & Environmental Eng.

Florida State University

Webpage

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Dr. Behzad Najafi

 

Assistant Professor, Energy Department

Politecnico di Milano

Webpage

Current Members

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Michele Gazzea

PhD Candidate

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email: Michele.Gazzea@hvl.no 

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Amir Miraki

PhD Candidate

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email: amir@hvl.no 

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Abdul Hanan

PhD Candidate

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email: Abdul.Hanan@hvl.no 

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Mehak Khan

Postdoctoral Researcher

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email: Mehak.Khan@hvl.no 

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Mira Kenzhebay

PhD Candidate

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email: Meruyert.Kenzhebay@hvl.no 

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Lukas Schild

PhD Candidate

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email: Lukas.Schild@hvl.no 

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Rune Mæstad

MS 

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email: rune.maestad@gmail.com

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Mathias Larsen

MS 

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email: Mathias.Larsen@student.uib.no 

Alumni

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Simon Sanca

PhD Candidate

Department of Civil Eng

 

Western Norway Uni of App Sci

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Malin Iversen

MS 

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

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Dr. Alican Karaer

 

PhD 

Department of Civil & Environmental Eng.

 

FSU-FAMU 

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Dr. Mahyar Ghorbanzadeh 

PhD 

Department of Civil & Environmental Eng.

 

FSU-FAMU 

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Sindre Aalhus

MS

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

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Atle Føli

MS 

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Jose Guillen Cordova

Dr. Jose David Cordova

Ph.D. 

Department of Electrical and Computer Engineering

Florida State University

Lalitha Madhavi K.S

Dr. Lalitha Madhavi K.S

Ph.D.

Department of Electrical and

Computer Engineering

Florida State University

Mostafa Gilanifar

Dr. Mostafa Gilanifar

 

Ph.D. 

Department of Industrial Engineering

Florida State University

Mehmet Baran Ulak

Dr. Mehmet Baran Ulak

Ph.D. 

Department of Civil and Environmental Engineering

 

Florida State University

Andres Sanchez

MS

Department of Electrical and

Computer Eng

 

 Florida State University

 

Ali Sayghe

Ali Sayghe

PhD

Department of Electrical and Computer Engineering

Florida State University

Xiaorui Liu

XiaoRui Liu

MS

Department of Electrical and

Computer Engineering

 

Florida State University

Ayberk Kocatepe

Dr. Ayberk Kocatepe

PhD

Connetics Transportation Group

Donlin Cai

 DongLin Cai

 MS

Dept of Electrical and Computer  Engineering​

 

Florida State University

Hasan H. Karabağ

MS

Department of Civil and

Environmental Engineering

Florida State University

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    Matthias Stifter

      Visiting PhD Student

      Dept of Electrical Engineering​

     

      Technical University of Vienna, 

      Austria

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Mengmeng Xiao

Visiting PhD Student

Dept of Electrical Engineering​

Huazhong University of Science and Technology, China

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    Davide Pinzan

      Visiting MS Student

      Dept of Electrical Engineering​

     

      University of Padova, Italy

    

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    Luca Di Narzo

      Visiting MS Student

      Dept of Mechanical Engineering​

     

      Politecnico di Milano, Italy

    

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    Monica Depalo

      Visiting MS Student

      Dept of Mechanical Engineering​

     

      Politecnico di Milano, Italy

    

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Oscar Sommervold

MS Student

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email:Oscar.Sommervold@student.uib.no 

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Adrian Solheim

MS Student

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email:adrsolheim@protonmail.com 

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Sindre Larsen

MS Student

Department of Comp Sci,  

Electrical Eng, & Math Sci

 

Western Norway Uni of App Sci

Email:S.Larsen@student.uib.no

Projects

Projects

Development of innovative complex predictive maintenance system (EA-Predictive),

Dr. Arghandeh PI, EEA and Norway Grants, 2021-2024

Intelligent dispatching, and optimal operation of cascade hydropower plants based on big spatiotemporal data (IntHydro),

Dr. Arghandeh Co-PI, , Research Council of Norway, 2021-2024

Prediction of ignition and spread of wildfires in Scandinavia: from experiments to models (PREWISS),,

Dr. Arghandeh Partner, Research Council of Norway, 2021-2025

Transnational Partnership for Excellent Research and Education in Disruptive Technologies for a Resilient Future (DTRF),

Dr. Arghandeh Co-PI, , Research Council of Norway, 2021-2023

AI for Sustainable Energy,

Dr. Arghandeh PI, HVL University Project, 2020-2022

Satellite Technologies Feasibility Study for Power Lines

Dr. Arghandeh PI, Statnett & StormGeo, 2021-2021

Towards a FAIR and Open Data Ecosystem in the Low Carbon Energy Research Community

Dr. Arghandeh Co-PI, EU HORIZON 2020, 2020-2022

Information Mining with a Span of Fuzzy and Causality Approaches

Dr. Arghandeh Co-PI, the University and College Network for Western Norway, 2019-2020

GridEyeS - Smart Grid Eye, from Space to Sky

Dr. Arghandeh PI, European Space Agency, 2019-2020

IBM Faculty Award, IoT Technology for Smart Buildings

Dr. Arghandeh PI, IBM, 2018-2019

User-Centered Heterogeneous Data Fusion for Multi-Networked City Mobility UHDNetCity

Dr. Arghandeh PI and Dr. Ozguven Co-PI, U.S. National Science Foundation, 2016-2019

Resilient Alaskan Distribution System Improvements using Automation and Energy Storage

Dr. Arghandeh Co-PI, U.S. Department of Energy, 2017-2020

Bridging the Digital Divide for the Well-Being of Aging Populations in Smart Cities

Dr. Arghandeh Partner, U.S. National Science Foundation, 2017-2018

Open-source Distributed Control Platform for HIL-based Testing Advanced Ship Power Systems

Dr. Arghandeh Co-PI, U.S. Office of Naval Research, 2016-2017

High Resolution Monitoring System for Distribution Networks with Micro-Synchrophasors

Dr. Arghandeh PI, Florida State University Office of Research, FYA Program, 2016

Data and Model Integration for Multidimensional System Observability

Dr. Arghandeh PI, Florida State University Office of Research, 2015

News

News

This section is not updated often. A lot of cool things are happening in Ci2Lab; updates coming here soon!

Oct 2022

Prof. Arghandeh book "Big Data Application in Power Systems" is translated to Korean!

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Jun 2022

We officially joined the ITU and WMO joint Focus Group on AI for Natural Disaster Management (FG-AI4NDM). We lead a use case on Use Case on Situational Awareness System for Disaster Response using Space-based AI (SARA).

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Mar 2022

We are excited to start our new project funded by the EEA-Norway Grants called Development of innovative complex predictive maintenance system (EA-Predictive)

We will work closely with Energy Advice company in Lithuania.

Here is the press release.

June 2021

Prof. Arghandeh will deliver the keynote speech for the 2021 International Conference & Exposition on Modern Energy and Power Systems on June 16.

June 2021

Our paper "Post-Hurricane Vegetative Debris Assessment using Spectral Indices Derived from Satellite Imagery" is accepted for publication in the Transportation Research Record.

May 2021

Our paper "City Transportation Network Vulnerability to Disasters: The Case of Hurricane Hermine in Florida" is accepted for publication in the Environmental Hazards journal.

May 2021

Our paper "Automated Satellite-based Assessment of HurricaneImpacts on Roadways" is accepted for publication in the IEEE Transactions on Industrial Informatics.

April 2021

Our paper "Resilience Characterization for Multi-Layer Infrastructure Networks" is accepted for publication in the IEEE Intelligent Transportation Systems Magazine.

April 2021

Our group will presents three papers in the ASCE International Conference on Transportation & Development

Paper 1: Evaluating the Relationship between Vehicle Travels and Carbon Monoxide (CO) Concentrations among Florida Counties under the Impact of COVID-19 Precautions

Paper 2: Post-Hurricanes Tree Debris Detection using Satellite Imagery and Deep-Learning

Paper 3: Traffic Forecasting Using Sentinel-5P Air Pollution Data and Historical Weather Records: A Remote Sensing Approach

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Aug 2019

The Smart Grid Eye, from Space to Sky (GridEyeS) is funded by the European Space Agency. The overall goal of this study is to create an end to end electric grid monitoring platform using satellite images combined with high-resolution drone data to create multi-layer situational awareness, especially in the case of extreme weather events in the electric grid. 

More info

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Sep 2018, Dr. Arghandeh joined the Western Norway University of Applied Sciences, as a Professor in Data Science and AI.

March 2018

Dr. Arghandeh honored for book publication in Florida State University

January 2018 

Dr. Arghandeh and Dr.Ozguven visit of NASA Challenger Learning Center

and talk on BigData for SmartCities

January 2018 

Dr. Arghandeh's book "Big Data Application in Power System" published by Elsevier

October 2017

Dr. Ozguven, published an article in Tampa Bay Times

 

October 2017

Dr. Ozguven, published an article in Tallahassee Democrat

 

Oct 2017

Dr. Ozguven on WTXL TV talking about our research on Evacuating senior people and their furry friends 

 

Oct 2017

Resilient Distribution Systems Lab Call Awards, Office Of The Under Secretary For Science And Energy, U.S. Department of Energy

 

Oct 2016

Radio Interview," the progression and development of DigiTally", Tallahassee Talk Radio Show.

 

 

Oct 2016

Featured Research,"FSU Team Tackles Urban Mobility In Smart City Era" FSU News.

 

 

Oct 2016

Featured Research," FSU Researchers Transforming Tallahassee Into a Smart City,” Florida Flambeau.

 

 

Jul 2015

News,"EECS postdoc scholar Reza Arghandeh has been selected to receive the 2015 ASME Award"

UC Berkeley.

 

 

Jul 2015

News, "ASME Power Award for FSU New Faculty" FSU College of Engineering Website.

 

 

Jun 2015,

News, " IEEE PES Award for Paper on Data Accuracy for Inverters Control" FSU College of Engineering Website.

Publications

Publications

For the updated list of publications, please check:
Reza Arghandeh Google Scholars 

Recent Papers:

2021:

City Transportation Network Vulnerability to Disasters: The Case of Hurricane Hermine in Florida, M. Ghorbanzadeh, M. Koloushani, E.E. Ozguven, A. Vanli, and R. Arghandeh, Environmental Hazards, 2021

Automated Satellite-based Assessment of HurricaneImpacts on Roadways, M Gazzea, A Karaer, N Balafkan, T Abichou, EE Ozguven, R Arghandeh, IEEE Transactions on Industrial Informatics, 2021

Developing city-wide hurricane impact maps using real-life data on infrastructure, vegetation and weather, M Chen, A Karaer, EE Ozguven, T Abichou, R Arghandeh, J Nienhius, Transportation Research Record, 2021

 

Resilience Characterization for Multi-Layer Infrastructure Networks, MB Ulak, LMK Sriram, A Kocatepe, EE Ozguven, R Arghandeh, IEEE Intelligent Transportation Systems Magazine, 2021

 

Automated Power Lines Vegetation Monitoring using High-Resolution Satellite Imagery, M Gazzea, M Pacevicius, DO Dammann, A Sapronova, TM Lunde, R Arghandeh, IEEE Transactions on Power Delivery, 2021

 

Exploring Correlations Between Vehicle Travels and Tropospheric Nitrogen Dioxide (NO2) Density Among Florida Counties Impacted by COVID-19, A Karaer, N Balafkan, M Gazzea, R Arghandeh, EE Ozguven, Transportation Research Board 100th Annual Meeting 2021

 

Post-Hurricanes Roadway Closure Detection using Satellite Imagery and Semi-Supervised Ensemble Learning, M Gazzea, A Karaer, N Balafkan, EE Ozguven, R Arghandeh, Transportation Research Board 100th Annual Meeting 2021

 

Leveraging Remote Sensing Indices for Hurricane-induced Vegetative Debris Assessment: A GIS-based Case Study for Hurricane Michael, A Karaer, B Ulak, T Abichou, R Arghandeh, EE Ozguven, Transportation Research Board 100th Annual Meeting 2021

 

CONTACT

Contact

Prof. Reza Arghandeh

reza[dot]arghandeh[at]hvl.no

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