Specialization Areas: 

Geographic Information Science and Technology, Land Use

Biography:

Gervais Tabopda is interested in the interaction between people and the environment; land use/land cover dynamics; local and rural environmental practices and perceptions; conservation policies; protected-area management; sustainable development in developing countries; remote sensing and GIS analysis; spatial analysis; mapping; and urban planning. He obtained his Ph.D. in Geography and Urban Planning with a specialization in environmental conservation policies from University of Orleans in France.

Teaching Interest:

My teaching interests focus on GIS, Remote Sensing, and Geospatial Data Science. I prioritize strong fundamentals—such as spatial analysis, cartography, database design, and image interpretation—because these skills never go out of style. From there, I build toward advanced applications such as GeoAI, geospatial automation, and environmental change modeling.

Key areas I’m committed to teaching:

Introductory & Advanced GIS

Remote Sensing & Image Analysis

Python/FME Automation

Spatial Modeling for Environment & Urban Systems

Geospatial Data Science & Machine Learning

My teaching philosophy is straightforward:

Teach the basics well.

Use real data, real problems, and clear expectations.

Push students to think critically and question easy answers.

Blend traditional spatial reasoning with modern tools to ensure they remain competent and employable.

I aim to develop students who can solve problems independently, communicate spatial results clearly, and understand the responsibility that comes with producing geospatial information.

Research Interest:

My research focuses on Land Use and Land Cover Change (LULCC), environmental policy analysis, and the application of GeoAI in Tropical Africa and other rapidly transforming regions. I study how human pressures, climate variability, and governance shape landscapes—and how spatial evidence can guide better decision-making.

Core research areas:

LULCC modeling and environmental change

Remote sensing for deforestation, urban growth, and ecosystem monitoring

GeoAI, machine learning, and advanced classification methods

Geospatial policy evaluation and sustainability metrics

Spatial data science for climate resilience and development planning

My work blends traditional spatial analysis with emerging AI methods—while staying skeptical of flashy trends. I focus on developing reproducible workflows, implementing long-term monitoring, and generating evidence-driven insights that policymakers can effectively utilize. Ultimately, my goal is to connect advanced geospatial analytics with sustainable development, especially in regions where data gaps and environmental pressures collide.

List of Recent Scholarly Work:

Not Applicable

Degrees with Year of Award:

Ph.D. in Geography and Planning, University of Orleans, France - 2008