This session will feature a range of remote sensing and GIS projects primarily from our student attendees.
1:00 PM – 1:15 PM – Mapping Building Entry and Exit Points for Pedestrian Navigation: Technologies, Workflows, and Insights from Downtown Atlanta
In most navigation systems, pedestrian routes are oversimplified – typically assuming people can move freely across open spaces or along sidewalks. However, real-world pedestrian mobility is far more complex. Factors such as inaccessible building entrances, missing elevators, or closed stairwells can significantly alter travel time, especially for people with disabilities, parents with strollers, or those unfamiliar with the area.
To address these challenges and enhance pedestrian mobility and accessibility in complex urban environments, the Georgia Tech team is leading a data-driven initiative focused on mapping detailed building entry and exit points in Downtown Atlanta. This work is a foundational step toward building a comprehensive, impedance-aware pedestrian network that reflects real-world travel constraints often overlooked in traditional pedestrian planning.
Our presentation showcases a robust workflow that combines low-cost data collection technologies, machine vision (MV), and advanced spatial analysis to identify, classify, and integrate pedestrian-accessible infrastructure such as elevators, stairs, and ramp systems into the network. These vertical circulation elements are modeled as unique link types with variable impedance values based on accessibility, elevation change, and status.
A key innovation is the introduction of multimodal pedestrian paths (e.g., bridges, pedestrian plazas, tunnels) as a new, distinct class of network elements. These are separated from traditional at-grade paths to better represent path complexity and context-specific travel impedance.
This project not only improves route precision for navigation apps and urban planning tools but also enhances mobility by accounting for a wider range of physical access scenarios. The techniques presented are scalable and cost-efficient, making them practical for deployment in other dense urban areas.
Ira Pathak, Georgia Tech
1:15 PM – 1:30 PM – PINN-TSE: A Hybrid Model for Improved Traffic State Estimation and Communication
Traffic congestion and inefficiencies cause safety, time, and environmental problems. Traditional traffic management systems struggle to provide accurate and reliable data. This paper introduces the Physics-Informed Neural Network-Based Traffic State Estimator (PINN-TSE), a new framework that combines physics-based traffic flow models with machine learning and natural language processing (NLP). PINN-TSE uses both physical rules and data-driven techniques to predict traffic density and speed accurately. It balances accuracy and physical consistency using a custom loss function. Large Language Models (LLMs) are used to create user-friendly traffic insights through a chat-based web app, answering questions about specific locations and times. Tested on real-world data from the US-101 highway, PINN-TSE outperforms data-driven models by 60% for density and 76% for speed predictions. It also reduces shockwave speed prediction error to 8%. The system can identify traffic jams and suggest alternative travel plans, demonstrating its practical value for real-world traffic management. This approach contributes to smart transportation by improving safety, efficiency, and sustainability.
Tewodros Gebre, North Carolina A&T State University
Leila Hashemi Beni, North Carolina A&T State University
1:30 PM – 1:45 PM – Geographic Object-Based Image Analysis (GEOBIA) Techniques for High Urban Density Mapping in Downtown Chicago, Illinois, USA
Remote sensing data, including multispectral imagery (MSI) and lidar, are increasingly available at high spatial resolution (HSR). There is an expectation that remote sensing data analysis scales alongside increased resolutions. One type of analysis is geographic object-based image analysis (GEOBIA) which creates image objects from pixels using segmentation algorithms. This analysis provides object properties including reflectance values, spatial relationships between objects, object textures and patterns and elevation and intensity values. While GEOBIA techniques have become more well defined and are increasingly published, there is a gap in the literature exploring GEOBIA in high density urban environments for land use land cover (LULC) classifications and extraction of features within cast shadows. The downtown area of Chicago offers a unique area of interest to explore these techniques. This presentation will cover image segmentation, creation of image interpretation keys, ruleset development and thematic accuracy assessment of initial and refined classifications within Chicago, Illinois, USA.
Daniel Bartlett, The Pennsylvania State University
1:45 PM – 2:00 PM – From Roots to Leaves: Understanding Multi-Scale Trait Variation in Freshwater Wetlands
Wetlands are ecologically complex systems shaped by spatio-temporal variation in hydrologic, edaphic, and disturbance regimes, yet critical gaps remain in understanding how functional trait variation governs effects of these drivers on ecosystem resilience and stability. Hydrologic modification, nutrient enrichment, and climate change have altered disturbance regimes in many wetland systems, diminishing their resilience and leading to persistent shifts in community structure. Restoration efforts aim to reverse these trajectories, but success depends on understanding how plant communities respond to environmental change across spatial and ecological scales. Functional traits—morphological and physiological features that influence species’ fitness—offer a mechanistic lens for evaluating ecosystem resilience and predicting transitions. Drawing from trait-based ecological theory, this project integrates the response-effect trait framework to investigate the role of functional diversity across spatial (plot, patch, and landscape) and ecological (individual, species, community) scales in freshwater wetlands of the Florida Everglades. My overarching objective is to examine how plant functional traits influence the stability, resilience, and transitions of freshwater wetland communities during active restoration, using a holistic approach that integrates trait-based ecology and remote sensing. WorldView-2 imagery will be used to delineate wetland patch types based on the vegetation structure and dominant species composition across six landscapes representative of key environmental gradients. This integration of field-based trait data with remote sensing aims to enhance our understanding of ecosystem responses to hydrologic restoration at multiple spatial scales.
Carlos Pulido, Florida International University
2:00 PM – 2:15 PM – Nitrogen Dioxide Pandora Retrievals and Satellite-Based Air Quality Observations in Miami, FL
Air pollution remains a critical concern in urban environments, particularly in rapidly growing metropolitan areas like Miami. Among urban pollutants, nitrogen dioxide (NO₂) is a key indicator of anthropogenic emissions, originating primarily from fossil fuel combustion. Once in the atmosphere, NO₂ can be transported by wind, deposited onto surfaces such as vegetation, buildings, and roadways, and subsequently resuspended, thereby sustaining localized air quality issues and contributing to public health risks [1, 2]. Traditional ground-based monitoring stations provide valuable but spatially limited data, often constrained by sparse station distribution, cloud cover, and complex built environments—especially in coastal regions. Remote sensing techniques, particularly satellite-based and ground-based spectrometry, offer critical enhancements by enabling the spatial and temporal characterization of air pollutants across broader urban landscapes [3, 4]. These tools have proven vital for air quality monitoring, climate regulation assessments, urban heat island mapping, and aerosol studies [5, 6]. This study focuses on the application of remote sensing and in-situ measurements to assess NO₂ concentrations in and around the Modesto Maidique Campus (MMC) of Florida International University (FIU), located in Miami-Dade County. The campus spans approximately 234 hectares in a suburban setting and serves around 26,000 commuting students (FIU, 2023). The study area is uniquely positioned between two major natural features—the Atlantic Ocean (18 miles east) and the Everglades (3 miles west)—which significantly influence pollutant dispersion and transport. Despite these natural mitigating factors, Miami ranks among the worst U.S. cities for ozone pollution, placed 116th in the 2024 “State of the Air” report (American Lung Association, 2023). The project aims to estimate both near-surface NO₂ concentrations and tropospheric vertical column densities for 2024. Data is derived from the Pandora ground-based spectrometer and PurpleAir sensors located on FIU’s campus, supplemented by satellite observations from instruments such as TEMPO. The analysis explores seasonal, daily, and monthly NO₂ patterns, examining atmospheric variability and potential transport from nearby emission sources, including highways, industrial areas, and Miami’s network of international airports. This work is part of an ongoing initiative led by the Environmental Change Laboratory at FIU, under the guidance of Dr. Olivas, and supported by NASA’s STEAM education funding for Minority Serving Institutions. The integration of remote sensing and in-situ measurements provides a valuable framework for understanding NO₂ behavior in subtropical urban environments and contributes to capacity-building in environmental monitoring.
Luana Antunes Alexandre, Florida International University
2:15 PM – 2:30 PM – Enhancing Pedestrian Navigation with High-Precision GPS
To address the limitations of conventional pedestrian navigation systems, which often overlook accessibility constraints, this Georgia Tech-led project develops a structured, data-driven workflow for mapping ADA-compliant pedestrian infrastructure with high precision GPS units using extensive field data collection in the Atlanta metropolitan area. A core contribution of this work is the implementation of link-by-link impedance modelling for transportation navigation mobile apps where each pedestrian segment is assigned a traversal cost based on factors like accessibility by ADA user. This structured impedance layer enables routing algorithms to compute personalized, constraint-aware paths rather than defaulting to shortest-distance navigation. By integrating precise locations of pedestrian assets with high resolution-ariel imagery and machine vision techniques, this project delivers a scalable, open-source framework for building inclusive pedestrian routing systems, ultimately improving navigation accuracy and travel times in complex urban environments.
Ira Pathak, Georgia Tech
