Mentors: Rachel Layko (Geospatial Analytics PhD student), Dr. Jelena Vukomanovic

Inland and river cultural landscapes are affected by climate change impacts like river flooding, more frequent/intense storms, and rising temperatures. We are developing methods for cultural landscape climate change vulnerability assessments (CCVA) that address data gaps, inform site condition assessment, and plan targeted adaptation strategies. We seek an intern interested in forecasting climate threats to existing National Park Service natural and cultural resources.

Responsibilities may include:

• Data collection and cataloging
• Spatial (GIS) and tabular climate data analysis
• Scripting
• Flood modeling
• Design of interactive user interfaces
• Map creation and data visualization

Mentor: Dr. Vaclav (Vashek) Petras

This project builds a natural language interface for Tangible Landscape so that users can request geospatial analyses by voice, possibly in combination with physical buttons and other objects. The scope covers a set of predefined activities: selecting layers to display, triggering analyses, and performing actions in the tangible user interface. The intern will design and implement a pipeline connecting third-party or open-source speech-recognition and LLM services to these operations using function-calling APIs and context-aware prompt engineering. The intern is expected to work independently on implementation, including prototyping options, writing code, and evaluating reliability.

Preferred Qualifications:

• Demonstrated Python proficiency (coursework plus projects or contributions beyond assignments).
• Demonstrated hands-on experience building applications with LLM APIs, including function calling or tool use
• Familiarity with speech-to-text services, API integration patterns, or geospatial is a plus

Mentors: Dr. Anna Petrasova

Tangible Landscape lets people reshape a physical terrain model and immediately see geospatial analyses projected back onto it. The intern will maintain the real-time scanning, processing, and projection pipeline and extend it with improvements such as integration of new VR hardware, new types of interactions and displayed objects, or migration to newer versions of GRASS and Blender. The intern will also maintain documentation and will have opportunities to present the system at events and to visitors.

Preferred Qualifications:

• Demonstrated experience with Python
• Comfort with Linux and command-line tools
• C/C++ is an advantage
• Demonstrated interest in computer vision, 3D visualization (e.g., Blender), robotics, or open-source geospatial software

Mentors: Dr. Jen Richmond-Bryant, Abdeali Saherwala (Geospatial Analytics PhD student)

Communities throughout North Carolina have been grappling with legacy pollution from industrial sources, including polyfluorinated alkyl substances (PFAS) from Teflon manufacturing in Eastern NC and lead in Durham and Greensboro near the sites of former incinerators. Mapping is a powerful tool that can empower vulnerable communities to understand their potential exposures. The Legacy Mapper will create a map of known legacy pollution sources, dump locations, and available measurement data, and they will juxtapose data regarding community vulnerability, such as locations of schools and daycares. Once developed, the map will be published to help raise public awareness.

Preferred Qualifications: Students should have taken GIS 280 prior to applying or otherwise demonstrate that they have experience with GIS. Experience with story maps is preferable. Note that this can be accomplished through a series of concrete tasks that involve data processing, data import into ArcGIS or QGIC, and map development and refinement.

Mentors: Dr. Aaron Hipp, Dr. Katie Burford

Our research team has a series of projects focused on how availability and access to urban spaces (e.g., parks, playgrounds, soccer pitches, bike infrastructure, open streets) impact opportunities for active living in various contexts. We are using spatial data and community-engaged approaches to develop methods for locating urban spaces and measuring (in)equity in access to these spaces. We are seeking an undergraduate student to contribute to these various projects by 1) supporting method development, and 2) generating interactive maps to improve understanding of urban space access and the links with neighborhood social vulnerability indicators and active living-related health outcomes.

Preferred Qualifications:

• Familiarity with Geographic Information Systems (GIS) is highly preferred or willing to enroll in a GIS course
• Familiarity with R or Python for data processing and scripting
• Data visualization 

Mentors: Abby Wiese (Geospatial Analytics PhD student), Dr. Jelena Vukomanovic

In partnership with the U.S. Forest Service, we use geospatial analytics to examine natural disaster impacts on rural communities and economies. We have quantified parcel-level land cover change using a classification approach that captures landscape composition. We are extending this framework to define community types and guide adaptation strategies that strengthen hazard resilience for the Southeastern U.S. A key component of the project is to identify where and when historical disaster events have occurred, assess their magnitude, and make this information easily accessible to stakeholders through an interactive web-based platform.

Preferred Qualifications:

• GIS
• Strong written and verbal communication skills
• Ability to create visually appealing maps, charts or other visuals
• Coding experience preferred

Mentors: Jillian Greene (Geospatial Analytics PhD student), Dr. Mirela Tulbure

This project is recruiting an undergraduate student interested in remote sensing to join the Geospatial Analysis for Environmental Change Lab in using newly released NASA-ISRO Synthetic Aperture Radar data to map water extent under vegetation. As a research assistant on this project, you’ll gain hands-on experience in Python coding, working with satellite data, geospatial data visualization, and working as a science researcher. Your work will contribute to cutting-edge research in remote sensing model development and culminate in a presentation at the Undergraduate Research & Creativity Symposium. 

Preferred Qualifications:

• GIS experience
• Familiarity with Python
• Passion for science