• 🔭 I'm currently researching Geospatial AI, hydrologic and environmental modeling, and physics-guided machine learning
• 🧭 I translate elevation data, satellite imagery, and hydrologic simulation into reproducible, high-performance geospatial pipelines for environmental decision-support
• 📚 PhD Researcher in Geographic Information Systems and Environmental Modeling, Southern Illinois University Carbondale — M.S. Geography & Environmental Resources (Spec. Geospatial AI), 2024 — B.S. Geoinformatics and Surveying, University of Uyo, Nigeria, 2019
• 🌎 I'm open to collaborations across GIScience, GeoAI, hydrology, and environmental sustainability
• 💬 Ask me about Geospatial AI, physics-guided ML, LiDAR/DEM analysis, or drainage & hydrography detection
• ✨ Vision: "Turning elevation data and hydrologic signal into decisions that protect the ground and water beneath us — where physics, geospatial AI, and environmental stewardship meet."
• 📄 View my CV
• 📫 Reach me at: edidemichael@gmail.com

Deep learning–based soil and land-surface segmentation using Swin-UNet architectures, applied to drought forecasting across remote-sensing drought indices. Primary developer within GeoFewLab.

• 🔗 https://github.com/mikedidem/soil

Large-scale groundwater modeling and scenario-based prediction for Nebraska, integrating MODFLOW with ML surrogate models for water-resource management decisions. Technologies: MODFLOW-2005, FloPy, Python, CNN, Transformer-based models, PINNs

End-to-end deep learning pipelines (U-Net, CM-UNet, Faster R-CNN, YOLOv5, Transformer models) for detecting drainage crossings from elevation-derived hydrographic data, supporting infrastructure monitoring and hydrographic mapping across the contiguous USA.

• 🔗 https://github.com/mikedidem/Drainage_CrossingDet

• Edidem, M., Xu, B., Li, R., Wu, D., Rekabdar, B., Wang, G. (2025). Identification of Drainage Crossings on High-Resolution Digital Elevation Models Using Explanatory Deep Learning Approaches. Frontiers in Artificial Intelligence
• Edidem, M., Li, R., Wu, D., Rekabdar, B., Wang, G. (2025). GeoAI-based Drainage Crossing Detection for Elevation-derived Hydrographic Mapping. Environmental Modelling & Software
• Nazeri, A., Godwin, D.W., Panteleaki, A.M., Anagnostopoulos, I., Edidem, M., Li, R., Shu, T. (2025). Exploration of TPU Architectures for the Optimized Transformer in Drainage Crossing Detection. Proceedings of 2024 IEEE International Conference on Big Data
• Wu, D., Li, R., Edidem, M., Wang, G. (2024). Enhancing Hydrologic LiDAR Digital Elevation Models: Bridging Hydrographic Gaps at Fine Scales. JAWRA – Journal of the American Water Resources Association
• Wu, D., Li, R., Talbert, C., Edidem, M., Rekabdar, B., Wang, G. (2023). Classification of Drainage Crossings on High-resolution Digital Elevation Models: A Deep Learning Approach. GIScience & Remote Sensing, 60:1

• 🌐 Portfolio: mikedidem.github.io/medidem
• 💻 GitHub: mikedidem
• 📧 Email: edidemichael@gmail.com

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