Spatial Analysis, GeoAI, and Environmental Change Lab
Led by Dr. Jane Southworth, Professor, Department of Geography, University of Florida
Location: Dauer Hall 50
Earth observation for a changing world
The SAGE GeoAI Lab at the University of Florida advances research at the intersection of Earth observation, spatial analysis, GeoAI, time-series remote sensing, and environmental change. Our work uses geospatial data, remote sensing, GIS, spatial statistics, machine learning, and interdisciplinary approaches to understand how landscapes change over time and what those changes mean for ecosystems, communities, conservation, sustainability, and human wellbeing.
The lab builds on a long history of remote sensing and land change science research, originally supported through NASA-funded computing infrastructure, and previously known as ‘The NASA Lab’. Today, the SAGE GeoAI Lab reflects an expanded research identity focused on spatial analysis, artificial intelligence, Earth observation, environmental monitoring, land system science, and applied decision-making for a changing world.
Lab members include undergraduate students, master’s students, doctoral students, postdoctoral researchers, and collaborators from across the United States and the international research community. Lab members bring diverse disciplinary backgrounds, technical skills, field experiences, and research interests to collaborative projects focused on global environmental change and human–environment interactions.
What We Study
The core focus of the SAGE GeoAI Lab is to monitor, analyze, and explain environmental change across space and time. Our research examines relationships among land use and land cover change, climate variability and change, vegetation dynamics, conservation, urban and regional transformation, and social-ecological systems.
Using Earth observation data and spatial analytical approaches, the lab investigates questions such as:
- How are landscapes changing over time?
- How do climate variability and land use interact to shape vegetation dynamics?
- How can remote sensing and GeoAI improve environmental monitoring and decision-making?
- How do land change processes affect conservation, sustainability, and human wellbeing?
- How can spatial data support more resilient social-ecological systems?
Our research is grounded in theories linked to Land Change Science and Social-Ecological Systems analysis, with particular emphasis on time-series analysis, vegetation dynamics, scale and scaling, people and parks, conservation landscapes, and the use of geospatial technologies to understand complex environmental systems.
From Earth Observation to Insight
The SAGE GeoAI Lab combines Earth observation, GIS, spatial analysis, time-series analytics, machine learning, and GeoAI to move from data to insight. Our work often begins with multi-source satellite imagery and spatial data layers, then uses time-series analysis, change detection, modeling, and GeoAI to reveal patterns of environmental change.
Our workflow often includes:
Earth Observation — satellite imagery and remotely sensed environmental data
GIS/RS Data Layers — multi-source spatial data integration and harmonization
Time-Series Analysis — tracking change, trends, variability, and dynamics through time
GeoAI & Spatial Analysis — machine learning, spatial modeling, and pattern detection
Change Detection — quantifying environmental, land cover, and landscape transitions
Forecasting & Decision Support — producing maps, models, reports, and evidence-based insights
Core Research Themes
The SAGE GeoAI Lab works across a broad set of interconnected research themes:
Land Change Science: Understanding how landscapes transform through time and how those changes are linked to human and environmental drivers.
Remote Sensing: Using satellite Earth observations and geospatial data to monitor environmental conditions, land cover, vegetation, and change.
GeoAI and Machine Learning: Applying machine learning, deep learning, and spatial AI methods to Earth observation and environmental data.
Climate Variability and Change: Studying how climate variability and long-term climate change affect vegetation, land systems, livelihoods, and environmental resilience.
Conservation and Protected Areas: Assessing conservation landscapes, protected areas, people and parks, and the spatial dynamics of environmental stewardship.
Vegetation Dynamics: Analyzing vegetation change, greenness, productivity, phenology, and ecosystem responses through time-series remote sensing.
Urban and Regional Change: Tracking transitions from natural and agricultural landscapes into peri-urban and urban systems.
People–Environment Interactions: Linking environmental change to human systems, social-ecological dynamics, vulnerability, sustainability, and resilience.
Lab Community
The SAGE GeoAI Lab is an interdisciplinary research community made up of undergraduate researchers, graduate students, postdoctoral scholars, and collaborators. Lab members have come from across the United States and the international research community, bringing diverse perspectives, backgrounds, and technical strengths.
Students and researchers in the lab gain experience in remote sensing, GIS, spatial analysis, time-series methods, GeoAI, data visualization, environmental interpretation, and collaborative research communication. The lab emphasizes rigorous science, interdisciplinary thinking, peer mentoring and engagement, technical skill-building, and the translation of spatial data into useful knowledge.
Lab Location
SAGE GeoAI Lab, Dauer Hall 50, University of Florida
Current Lab Members
Lab Director
Dr. Jane Southworth, Professor, Department of Geography
- Email: jsouthwo@ufl.edu
Dr. Jane Southworth is Professor in the Department of Geography at the University of Florida. Her research focuses on human–environment interactions within the field of Land Change Science. Her work is grounded in interdisciplinary research teams that bring together social and physical scientists to address complex environmental problems. Her research strengths include remote sensing time-series analysis, machine learning and AI-based remote sensing approaches, vegetation dynamics, land use and land cover change, land change modeling, scale and scaling in remote sensing and environmental modeling, people and parks, and the impacts of climate change and variability on human–environment systems and vegetation dynamics.
Lab Members
Torit Chakraborty (PhD student)
- Email: toritchakraborty@ufl.edu
Anusha Chaudhary (PhD student)
- Email: anusha.chaudhary@ufl.edu
Patrick Gawienczuk (Masters student)
- Email: p.gawienczuk@ufl.edu
Md Muyeed Hasan (PhD student)
- Email: mdmuyeedhasan@ufl.edu
Romer Kohlhaas (Research Volunteer)
- Email: romerkohlhaas@gmail.com
Mashoukur Rahaman (PhD student)
- Email: m.rahaman@ufl.edu
Mohammad Safaei (PhD student)
- Email: safaei.mo@ufl.edu
Smriti Shrestha (incoming PhD student Fall 2026)
- Email: smritishrestha@ufl.edu
Audrey Culver Smith (PhD student – graduating August 2026, Affiliated Researcher after August 2026)
- Email: audreyculver@ufl.edu
Jessica Striley (PhD student)
- Email: jstriley@ufl.edu
Bewuket Tefera (PhD student)
- Email: bewukettefera@ufl.edu
Recent Publications and Projects
The SAGE GeoAI Lab has a long publication history in remote sensing, land change science, vegetation dynamics, savanna systems, conservation landscapes, climate variability, and human–environment interactions.
2026
Book (In Press): Erin L. Bunting, Jane Southworth, Cerian Gibbes, and Hannah V. Herrero (Editors). Remote Sensing, Big Data, and GeoAI: Exploring Applications with Geospatial Insights. Elsevier.
With lab authored chapters:
- Erin L. Bunting, Jane Southworth, Hannah V. Herrero, Cerian Gibbes, Stephanie Insalaco, and Mashoukur Rahaman (2026). Chapter 1: The Evolution of Big Data and GeoAI within the Field of Remote Sensing.
- Cerian Gibbes, Jane Southworth, Erin L. Bunting, Mashoukur Rahaman, Mohammad Safaei, Torit Chakraborty, Md Muyeed Hasan, Anusha Chaudhary, and Audrey C. Smith (2026). Chapter 2: Foundations of Remote Sensing and Earth Observation.
- Erin L. Bunting, Jane Southworth, Hannah V. Herrero, and Cerian Gibbes (2026). Chapter 4: Big Data Analytics for Geospatial Applications.
- Jane Southworth, Mohammad Safaei, Mashoukur Rahaman, Hannah V. Herrero, Audrey C. Smith, Bewuket B. Tefera, Carly S. Muir, and Ali R. Alruzuq (2026). Chapter 5: GEOAI Methods for Land Use and Land Cover Classification: Machine Learning and Deep Learning Approaches.
- Jane Southworth, Erin L. Bunting, Cerian Gibbes, and Mia Bennett (2026). Chapter 13: Ethical and Legal Considerations in Remote Sensing and Big Data: Privacy, Justice, and Governance in the GeoAI Era.
- Jane Southworth, Kati Migliaccio, and Gabriela Hamerlinck (2026). Chapter 15: Building AI Literacy Across the Curriculum: Integrating GeoAI and Big Data to Prepare Future Geospatial Leaders.
- Jane Southworth, Erin L. Bunting, Hannah V. Herrero, and Cerian Gibbes (2026). Chapter 16: Future Trends and Emerging Technologies in Remote Sensing, Big Data, and GeoAI.
Under review: Jane Southworth, Kati W. Migliacci, Gabriela Hamerlinck, Jospeh Glover, Elias Eldayrie, and Patricia Kio. Participatory Foresight for Institutional AI Transformation in Higher Education: A Campus-Wide Visioning Framework.
Under Review: Anusha Chaudhary, Julius R. Dewald, Jane Southworth, Ma Ruixan, Mohammad Safaei, Jose Szapocznik, Scott C. Brown, 2025-26. Scale Matters: Spatial and Temporal Effects of NDVI on Alzheimer’s Disease Incidence in Miami Dade County.
Under Review: Bewuket Tefera, Jane Southworth, Di Yang, Torit Chakraborty, Mohammad Safaei, and Mashoukur Rahaman. CNN-Transformer downscaling of GRACE reveals compartmentalized water storage dynamics in Stellenbosch, South Africa (2002–2024).
Hannah V. Herrero, Zoe L. Van der Walt, Erin L. Bunting, Stephanie A. Insalaco, Jack D. Spining, Dryver Z. Finch, Jane Southworth and Jason K. Blackburn (2026). Pathways to Sustainable Land Stewardship in South Africa’s Wine-Producing Regions. Sustainability, 18(8), 3825.
Jane Southworth, Kati W. Migliaccio, Sarah VanSchoick, Jospeh Glover, Elias Eldayrie, Jacob Albert, Azra Bihorac, Alexandra Bitton-Bailey, Ziynet Boz, Sid Dobrin, Ja’Net Glover, Jenna Gonzalez, Joel Harley, Amber Hatch, Patricia Kio, Christopher McCarty, Jasmine McNealy, David L. Reed, Aniruth Venkedesh, Amelia Winger-Bearskin, and Alina Zare. 2025-26. Reimagining the University: A Blue-Sky Visioning Framework for Artificial Intelligence Campus-Wide Integration. UF White Paper.
2025
Bewuket Tefera; Jane Southworth; Joann Mossa; Mashoukur Rahaman; Mohammad Safaei; Shankar Shankar; Di Yang, (2025). Predictive Groundwater Quality Responses to Land Cover and Lithology in the Upper Awash River Basin (Ethiopia) with Stacking Ensembles. Journal of Environmental Management, 394.
CS Muir, R Khatami, J Southworth, (2025). Large-scale land acquisitions and land cover change in Ethiopia. Ecology and Society, 30 (3).
Mashoukur Rahaman, J Southworth, Y Wen, D Keellings, (2025). Assessing Model Trade-Offs in Agricultural Remote Sensing: A Review of Machine Learning and Deep Learning Approaches Using Almond Crop Mapping. Remote Sensing 17 (15), 2670.
Mohammad Safaei, Jane Southworth, Cerian Gibbes, Hannah V. Herrero, Mashoukur Rahaman, Bewuket B. Tefera, Jason K. Blackburn, (2025). Land-cover classification in Addo Elephant National Park: Analyzing the impact of variables, classifiers, and object-based approach. Ecological Informatics, 90.
Mashoukur Rahaman, Jane Southworth, Amobichukwu Chukwudi Amanambu, Bewuket B. Tefera, Ali Alruzuq, Mohammad Safaei, Md. Muyeed Hasan, Audrey Culver Smith, (2025). Combining Deep Learning and Machine Learning Techniques to Track Air Pollution in Relation with Vegetation Cover through Remote Sensing Data. Journal of Environmental Management, 376.
2024
Southworth, J., Smith, A., Safaei, M., Rahaman, M., Alruzuq, A., Tefera, B.B., Muir, C.S., and Herrero, H.H. (2024). Machine Learning versus Deep Learning in Land System Science: A Decision-Making Framework for Effective Land Classification. Frontiers in Remote Sensing, 5, 1374862.
Southworth J, Bunting E, Herrero HV and Crews K. Editorial: Women in remote sensing. Front. Remote Sens. 5: 1369697.
Dewald, Julius; Southworth, Jane; Moise, Imelda (2024). The Role of People, Parks, and Precipitation on the Frequency and Timing of Fires in a Sub-Saharan Savanna Ecosystem. International Journal of Wildland Fire, 33, WF23020.
Dewald, J.R.; Southworth, J.; Szapocznik, J.; Lombard, J.L.; Brown, S.C. Greening the Urban Landscape: Assessing the Impact of Tree-Planting Initiatives and Climate Influences on Miami-Dade County’s Greenness. Remote Sens. (2024), 16, 157.
2023
Southworth J, & Kati Migliaccio (2023). Developing Career-Ready Graduates: The Importance of AI Literacy Across the Curriculum. Media & Learning Newsletter.
Yang, Di, Chiung-Shiuan Fu, Hannah Victoria Herrero, Jane Southworth, and Michael Binford (2023). “Linking forest management to surrounding lands: a citizen-based approach towards the regional understanding of land-use transitions.” Frontiers in Remote Sensing 4: 1197523.
ÖZDEŞ, Mehmet, and Jane Southworth (2023). Land Change Science: Understanding the complexity of monitoring protected areas in savanna ecosystems of Sub-Saharan Africa. Türk Coğrafya Dergisi (82), 63-76.
Southworth J, Ryan SJ, Herrero HV, Khatami R, Bunting EL, Hassan M, Muir CS and Waylen P (2023), Latitudes and land use: Global biome shifts in vegetation persistence across three decades. Front. Remote Sens. 4: 1063188.
Hannah V. Herrero, Jane Southworth, Reza Khatami, Stephanie Insalaco, and Carly Muir (2023). Examining the relationship between vegetation decline and precipitation in the national parks of the Greater Limpopo Transfrontier Conservation Area during the 21st century. Frontiers in Environmental Science, 11, 335.
Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI Across the Curriculum: Transforming the Higher Education Landscape via Innovation in AI Literacy. Computers & Education: Artificial Intelligence, 4: 100127.