Connectivity, Information & Intelligence Lab
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
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.
Project Webpage | Video | Lecture | Paper 1 | Paper 2
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.
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.
Paper 1 |
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.
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.
Project Webpage | Paper 1 | Paper 2 |
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).
Project Webpage | Lecture | Paper 1 | Paper 2
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.
Team
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
​
Dr. Mojtaba Yousefi
​
Associate Professor, Department of Comp Sci. and Elect Eng.
​
Western Norway University of Applied Science (HVL)
​
Academic Advisory Board
Dr. Eren Erman Ozguven
​
Associate Professor, Department of Civil & Environmental Eng.
Florida State University
​
​
Current Members
Amir Miraki
​
PhD Candidate
Electrical Eng, & Math Sci
Western Norway Uni of App Sci
Email: amir@hvl.no
Mehak Khan
​
Postdoctoral Researcher
Electrical Eng, & Math Sci
Western Norway Uni of App Sci
Email: Mehak.Khan@hvl.no
Mira Kenzhebay
​
PhD Candidate
Western Norway Uni of App Sci
Email: Meruyert.Kenzhebay@hvl.no
Rune Mæstad
​
MS
Electrical Eng, & Math Sci
Western Norway Uni of App Sci
Email: rune.maestad@gmail.com
​
Mathias Larsen
​
MS
Electrical Eng, & Math Sci
Western Norway Uni of App Sci
Email: Mathias.Larsen@student.uib.no
​
Alumni
Malin Iversen
​
MS
Electrical Eng, & Math Sci
Western Norway Uni of App Sci
​
Dr. Alican Karaer
PhD
Department of Civil & Environmental Eng.
FSU-FAMU
​
Dr. Mahyar Ghorbanzadeh
PhD
Department of Civil & Environmental Eng.
FSU-FAMU
​
Sindre Aalhus
​
MS
Electrical Eng, & Math Sci
Western Norway Uni of App Sci
​
Atle Føli
​
MS
Electrical Eng, & Math Sci
Western Norway Uni of App Sci
​
Dr. Jose David Cordova
Ph.D.
Department of Electrical and Computer Engineering
​
Florida State University
Dr. Lalitha Madhavi K.S
Ph.D.
Department of Electrical and
Computer Engineering
​
Florida State University
Dr. Mostafa Gilanifar
Ph.D.
Department of Industrial Engineering
​
Florida State University
​
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
PhD
Department of Electrical and Computer Engineering
​
Florida State University
XiaoRui Liu
MS
Department of Electrical and
Computer Engineering
Florida State University
Dr. Ayberk Kocatepe
PhD
Connetics Transportation Group
DongLin Cai
MS
Dept of Electrical and Computer Engineering​
Florida State University
​
​
Matthias Stifter
Visiting PhD Student
Dept of Electrical Engineering​
Technical University of Vienna,
Austria
​
​
​
Mengmeng Xiao
Visiting PhD Student
Dept of Electrical Engineering​
​
Huazhong University of Science and Technology, China
​
Davide Pinzan
Visiting MS Student
Dept of Electrical Engineering​
University of Padova, Italy
​
​
Luca Di Narzo
Visiting MS Student
Dept of Mechanical Engineering​
Politecnico di Milano, Italy
​
​
Monica Depalo
Visiting MS Student
Dept of Mechanical Engineering​
Politecnico di Milano, Italy
​
​
Oscar Sommervold
​
MS Student
Electrical Eng, & Math Sci
Western Norway Uni of App Sci
Adrian Solheim
​
MS Student
Electrical Eng, & Math Sci
Western Norway Uni of App Sci
Sindre Larsen
​
MS Student
Electrical Eng, & Math Sci
Western Norway Uni of App Sci
Email:S.Larsen@student.uib.no
Projects
​
Development of innovative complex predictive maintenance system (EA-Predictive),
Dr. Arghandeh PI, EEA and Norway Grants, 2021-2024
​
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
​
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
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!
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).
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
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.
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
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.