Mohamad Alipour

About

The Alipour Research Group at the University of Illinois Urbana-Champaign specializes in advancing Digital Twins that combine sensing, computing, and visualization to create smart and resilient natural and built environments. We achieve this by developing innovative remote sensing and other technologies.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Data Mining
• Physics
• Geology
• Data science
• Systems engineering
• Electrical engineering
• Database

Selected publications

• Optimized structural inspection path planning for automated unmanned aerial systems

  Automation in Construction · 2024-09-13 · 24 citations

  articleOpen accessSenior author

  Automation in Unmanned Aerial Systems (UAS)-based structural inspections has gained significant traction given the scale and complexity of infrastructure. A core problem in UAS-based inspection is electing an optimal flight path to achieve the mission objectives while minimizing flight time. This paper presents an effective two-stage method that guarantees coverage as a constraint to ensure damage detectability, while minimizing path length as an objective. A genetic algorithm first determines v…

  PublisherDOI

• Bayesian inversion of GPR waveforms for sub-surface material characterization: An uncertainty-aware retrieval of soil moisture and overlaying biomass properties

  Remote Sensing of Environment · 2024-08-12 · 13 citations

  articleOpen accessSenior author

  Accurate estimation of sub-surface properties such as moisture content and depth of soil and vegetation layers is crucial for applications spanning sub-surface condition monitoring, precision agriculture, and effective wildfire risk assessment. Soil in nature is often covered by overlaying vegetation and surface organic material, making its characterization challenging. In addition, the estimation of the properties of the overlaying layer is crucial for applications like wildfire risk assessment…

  PublisherDOI

• FUELVISION: A multimodal data fusion and multimodel ensemble algorithm for wildfire fuels mapping

  International Journal of Applied Earth Observation and Geoinformation · 2025-03-12 · 7 citations

  articleOpen access

  Accurate assessment of fuel conditions is a prerequisite for fire ignition and behavior prediction, and risk management. The method proposed herein leverages diverse data sources – including L8 optical imagery, S1 (C-band) Synthetic Aperture Radar (SAR) imagery, PL (L-band) SAR imagery, and terrain features – to capture comprehensive information about fuel types and distributions. An ensemble model was trained to predict landscape-scale fuels – such as the ’Scott and Burgan 40’ – using the as-re…

  PublisherDOI

• Platypus: Sub-mm Micro-Displacement Sensing with Passive Millimeter-wave Tags As "Phase Carriers"

  2023 · 7 citations

  Micro-displacement measurement is a crucial task in industrial systems such as structural health monitoring, where millimeter-level displacement of specific points on the structure or machinery displace can jeopardize the integrity of the structure and potentially leading to catastrophic damage or collapse. Traditionally, such displacements on large structures are measured using visual sensing platforms or advanced surveying equipment. However, they either fall short in varying weather and light…

  DOI

• Wildfire Fuels Mapping through Artificial Intelligence-based Methods: A Review

  Earth-Science Reviews · 2025-02-07 · 5 citations

  reviewOpen access

  Understanding fire behavior is a crucial step in wildfire risk assessment and management. Accurate and near real-time knowledge of the spatio-temporal characteristics of fuels is critical for analyzing pre-fire risk mitigation and managing active-fire emergency response. Geospatial modeling and land cover mapping using remote sensing combined with artificial intelligence techniques can provide fuel information at regional scales with high accuracy and resolution, as evidenced by the extensive re…

  PublisherDOI

Frequent coauthors

• Devin K. Harris

  University of Virginia

  86 shared

• Mehrdad Shafiei Dizaji

  University of Massachusetts Lowell

  28 shared

• I. Ab Aziz

  22 shared

• Yuxiang Zhao

  Jiangnan University

  21 shared

• Kaiyuan Wang

  Zhejiang Ocean University

  16 shared

• Adam C. Watts

  16 shared

• Osman E. Ozbulut

  15 shared

• Tianshu Li

  14 shared

Labs

• Alipour Research Group @IllinoisPI

Education

• PhD, Civil and Environmental Engineering

  University of Virginia

  2019

Awards & honors

• Faculty Fellow, National Center for Supercomputing Applicati…

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