Deep learning framework for Landslide Susceptibility Mapping using geospatial and environmental data. Employs DNN, 1D CNN, and LSTM with DBSCAN clustering to mitigate data sparsity. Features model evaluation, feature importance analysis, and susceptibility mapping outputs.
Sussman A.L., Gardner B., Adams E.M., Salas L., Kenow K.P., Luukkonen D.R., Monfils M.J., Mueller W.P., Williams K.A., Leduc-Lapierre M., & Zipkin E.F. 2019. A comparative analysis of common methods to identify waterbird hotspots. MEE 10(9): 1454-1468.
Contains Weekly Activites for the (ISTE 740) Geographic Information Sciences and Technologies Course @ RIT
This project analyzes urban mobility patterns in Chicago by combining satellite imagery (Sentinel-2), OpenStreetMap road data, population density, business licenses/POI data, and a large Chicago taxi dataset (~14 million trips in 2024–early 2026).
Postgraduate level project : This project focuses on detecting and forecasting crime hotspots by combining spatial and temporal analysis techniques.
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