Thomas Oommen (GMES, ICC), Ricardo Eiris, (CEGE, ICC), and Beth Veinott (CLS, ICC) are among eight Michigan Tech researchers who have submitted a a record number of eight concept papers for proposed research projects with the Federal Railroad Administration.
The Federal Railroad Administration (FRA) requested that Michigan Tech submit a record number of eight concept papers for proposed research projects as part of their 2021 Broad Agency Announcement.
In addition, Tech is a subcontractor for two more concept paper proposals. The paper submittal was coordinated by the Rail Transportation Program and the range of topics speaks to the diversity of Michigan Tech’s expertise applicable to the rail transportation. The PIs are looking forward to FRA decisions on how many of these papers advance to full proposals.
Each of the 10 projects had a different principal investigator (PI), representing six university departments/institutes and several more co-PIs.
The project titles and their PIs include:
Hyper- and Multi-spectral Sensing and Deep Learning for Automated Identification of Roadbed Condition, (PI, Thomas Oommen, GMES).
Wire Arc Additive Manufacturing (WAAM) for Weld Enhanced Cast Steel Coupler Knuckles (PI, Paul Sanders, MSE).
IoT Assisted Data-analytics Framework Enables Assessment of Location Based Ride Quality (LBRQ) (PI, Sriram Malladi, MEEM).
RailStory: Using Web-based Immersive Storytelling to Attract the Next Generation of Young Women in Rail (PI, Ricardo Eiris, CEGE).
A Risk Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm Hazards and Sea Level Rise (PI, Yousef Darestani, CEGE).
Rail Corridor Life-Cycle Assessment (LCA) Framework, Factors and Models to Support Project Evaluation and Multi-Modal Comparisons (PI, Pasi Lautala, CEGE).
Development of Infrared Thermography for Effective Rail Weld Inspection (PI, Qingli Dai, CEGE).
Enabling Longer-distance, AI-enabled Drone-based Grade Crossing Assessment in Potentially GPS Denied Environments (PI, Colin Brooks).
Multi-Site Simulation to Examine Driver Behavior Impact of Integrated Rail Crossing Violation Warning (RCVW) and In-Vehicle Auditory/Visual Alert (IVAA) System (PI, Elizabeth Veinott, subcontract with Virginia Tech).
Evaluation of Non-traditional Methods of Reducing Emissions in Short Line Railroad Operations (PI, Jeremy Worm, subcontract with ASLRRA).
Assistant Professor Sidike Paheding, Applied Computing, has been awarded a one-year MSGC Research Seed Grant for his project, “Monitoring Martian landslides using deep learning and data fusion.”
Professor Thomas Oommen, Geological and Mining Engineering and Sciences, is Co-PI of the project. The grant will support part-time employment of two students during the award period.
This grant is supported in part by funding provided by the National Aeronautics and Space Administration (NASA), under award number 80NSSC20M0124, Michigan Space Grant Consortium (MSGC).
The MSGC Research Seed Grant Program supports junior faculty and research scientists at MSGC affiliate institutions. The program also helps mid-career and senior faculty develop new research programs. The objective of this program is to allow award recipients to develop the research expertise necessary to propose research activities in new areas to other federal or nonfederal sources.
His research interests cover a variety of topics in machine learning, deep learning, computer vision, and remote sensing. He has authored/coauthored close to 100 research articles, including several top peer-review journal papers. He is an invited member of Tau Beta Pi (Engineering Honor Society).
Thomas Oommen, Timothy C. Havens, Guy Meadows (GLRC), and Himanshu Grover (U. Washington) have been awarded funding in the NSF Civic Innovation Challenge for their project, “Helping Rural Counties to Enhance Flooding and Coastal Disaster Resilience and Adaptation.”
The six-month project award is $49,999.
Vision. The vision of the new project is to develop methods that use remote sensing data resources and citizen engagement (crowdsourcing) to address current data gaps for improved flood hazard modeling and visualization that is transferable to rural communities.
Objective. The objective of the Phase-1 project is to bring together community-university partners to understand the data gaps in addressing flooding and coastal disaster in three Northern Michigan counties.
Guy Meadows is director of the Marine Engineering Laboratory (Great Lakes Research Center), the Robbins Professor of Sustainable Marine Engineering, and a research professor in the Mechanical Engineering-Engineering Mechanics department. His research interests include large scale field experimentation in the Inland Seas of the Great Lakes and coastal oceans; nearshore hydrodynamics and prediction; autonomous and semi-autonomous environmental monitoring platforms (surface and sub-surface); underwater acoustic remote sensing; and marine engineering.
Himanshu Grover is an asssistant professor at University of Washington. His research focus is at the intersection of land use planning, community resilience, and climate change.
About the Civic Innovation Challenge
The NSF Civic Innovation Challenge is a research and action competition that aims to fund ready-to-implement, research-based pilot projects that have the potential for scalable, sustainable, and transferable impact on community-identified priorities.
Professor Thomas Oommen (DataS, GMES, EPSSI) is the principal investigator on a one-year project that has been awarded a $41K research and development contract with the University of Nebraska-Omaha.
The project is titled “Flood Hazard Map to Water Management & Planning.”
Oommen’s research focuses on developing improved susceptibility characterization and documentation of geo-hazards (e.g. earthquakes, landslides) and spatial modeling of georesource (e.g. mineral deposits) over a range of spatial scales and data types.
To achieve his research interests, he has adopted an inter-disciplinary research approach joining aerial/satellite based remote sensing for obtaining data, and artificial intelligence/machine learning based methods for data processing and modeling.
Members of the Michigan Tech Transportation Institute (MTTI) were active at the
Among the many Michigan Tech students and faculty who attended and presented at the 2020 Transportation Research Board (TRB) Annual Meeting held recently in Washington, DC. was Thomas Oommen (GMES), who gave a lecture on “Remote terrain Strength for Mobility Characterization” at the meeting’s lectern Session 1384: Integration of Remote Sensing Techniques and Classical Instrumentation. Oommen is a member of the ICC’s Center for Data Sciences.
The Transportation Research Board (TRB) 99th Annual Meeting was held January 12–16, 2020, in Washington, D.C. More than 13,000 transportation professionals from around the world were expected to attendd.
The meeting program covered all transportation modes, with more than 5,000 presentations in nearly 800 sessions and workshops, addressing topics of interest to policy makers, administrators, practitioners, researchers, and representatives of government, industry, and academic institutions. A number of sessions and workshops focused on the spotlight theme for the 2020 meeting: A Century of Progress: Foundation for the Future.
Thomas Oommen (DataS), associate professor of geological and mining engineering and sciences, was recently featured in a Michigan Tech Unscripted Research Blog titled, ” Geohazards on the Horizon.”
Oommen was part of a US team in Mumbai this August working on disaster management curriculum with the Tata Institute of Social Sciences (TISS), the only institution in all of Mumbai—one of the world’s largest cities with 19 million people—to offer a degree in disaster management.
Research conducted by Michigan Tech doctoral candidate James Bialas and faculty members Thomas Oommen (DataS/GMES/CEE) and Timothy Havens (DataS/CS) made news in the Michigan Ag Connection, August 7, 2019. The item is a re-posting of the Michigan Tech Unscripted article, “Found in Translation, which was posted on the Michigan Tech website July 12, 2019.
With close to 2,000 working satellites currently orbiting the Earth, and about a third of them engaged in observing and imaging o
ur planet,* the sheer volume of remote sensing imagery being collected and transmitted to the surface is astounding. Add to this images collected by drones, and the estimation grows quite possibly beyond the imagination.
How on earth are science and industry making sense of it all? All of this remote sensing imagery needs to be converted into tangible information so it can be utilized by government and industry to respond to disasters and address other questions of global importance.
In the old days, say around the 1970s, a simpler pixel-by-pixel approach was used to decipher satellite imagery data; a single pixel in those low resolution images contained just one or two buildings. Since then, increasingly higher resolution has become the norm and a single building may now occupy several pixels in an image.
A new approach was needed. Enter GEOBIA– Geographic Object-Based Image Analysis— a processing framework of machine-learning computer algorithms that automate much of the process of translating all that data into a map useful for, say, identifying damage to urban areas following an earthquake.
In use since the 1990s, GEOBIA is an object-based, machine-learning method that results in more accurate classification of remotely sensed images. The method’s algorithms group adjacent pixels that share similar, user-defined characteristics, such as color or shape, in a process called segmentation. It’s similar to what our eyes (and brains) do to make sense of what we’re seeing when we look at a large image or scene.
In turn, these segmented groups of pixels are investigated by additional algorithms that determine if the group of pixels is, say, a damaged building or an undamaged stretch of pavement, in a process known as classification.
The refinement of GEOBIA methods have engaged geoscientists, data scientists, geographic information systems (GIS) professionals and others for several decades. Among them are Michigan Tech doctoral candidate James Bialas, along with his faculty advisors, Thomas Oommen(GMERS/DataS) and Timothy Havens (ECE/DataS). The interdisciplinary team’s successful research to improve the speed and accuracy of GEOBIA’s classification phase is the topic of the article “Optimal segmentation of high spatial resolution images for the classification of buildings using random forests” recently published in the International Journal of Applied Earth Observation and Geoinformation.
The team’s research started with aerial imagery of Christchurch, New Zealand, following the 2011 earthquake there.
“The specific question we looked at was, how do we translate the information we get from the crowd into labels that are coherent for an object-based image analysis?” Bialas said, adding that they specifically looked at the classification of city center buildings, which typically makes up about fifty percent of an image of any city center area.
After independently hand-classifying three sets of the same image data with which to verify their results (see images below), Bialas and his team started looking at how the image segmentation size affects the accuracy of the results.
“At an extremely small segmentation level, you’ll see individual things on building roofs, like HVAC equipment and other small features, and these will each become a separate image segment,” Bialas explained, but as the image segmentation parameter expands, it begins to encompass whole buildings or even whole city blocks.
“The big finding of this research is that, completely independent of the labeled data sets we used, our classification results stayed consistent across the different image segmentation levels,” Bialas said. “And more importantly, within a fairly large range of segmentation values, there was pretty much no impact on results. In the past several decades a lot of work has done trying to figure out this optimum segmentation level of exactly how big to make the image objects.”
“This research is important because as the GEOBIA problem becomes bigger and bigger—there are companies that are looking to image the entire planet earth per day—a massive amount of data is being collected,” Bialas noted, and in the case of natural disasters where response time is critical, for example, “there may not be enough time to calculate the most perfect segmentation level, and you’ll just have to pick a segmentation level and hope it works.”
This research is part of a larger project that is investigating how crowdsourcing can improve the outcome of geographic object-based image analysis, and also how GEOBIA methods can be used to improve the crowdsourced classification of any project, not just earthquake damage, such as massive oil spills and airplane crashes.
One vital use of of crowdsourced remotely sensed imagery is creating maps for first responders and disaster relief organizations. This faster, more accurate GEOBIA processing method can result in more timely disaster relief.
*Union of Concerned Scientists (UCS) Satellite Database
Thomas Oommen (DataS/GMES/EPSSI) is Principal Investigator on a project that has received a $39,999 research and development grant from the US Department of State. The project is titled, “Developing and Improving Disaster Management Studies Course in India.” This is a one-year project.