Geoinformatics student at AGH University of Science and Technology focused on spatial data science, GIS, remote sensing and Earth Observation.
My projects combine geospatial analysis with statistical modeling, machine learning and scientific computing. I work with spatial and satellite data using Python, R, MATLAB and GIS environments, with experience ranging from spatial statistics and point-pattern analysis to multispectral image classification, change detection, LiDAR, time-series modeling and environmental analysis.
I am particularly interested in applying data-driven methods to urban, environmental and Earth Observation problems, and in the intersection of geospatial technologies, data science and machine learning.
Nine applied Earth Observation case studies covering Sentinel, Landsat, SPOT 6, Pleiades and LiDAR data, including land-cover classification, multitemporal change detection, wildfire severity, agricultural drought monitoring, urban heat-island analysis and spatial planning applications.
Python-based spatial data science exercises covering vector and raster GIS, OpenStreetMap data acquisition, point-process analysis, kernel density estimation, spatial statistical testing, clustering and spatial autocorrelation.
Spatial-temporal modeling of Antarctic sea-ice extent using historical observations, regression, Fourier analysis, autoregressive modeling and Antarctic polar visualization.
Geomorphological landslide investigation combining terrain analysis, UAV-derived orthophotography, field verification, GIS interpretation and formal landslide documentation.
Scientific-computing exercises covering numerical PDE solutions, diffusion, gravity modeling, wave propagation, Monte Carlo simulation, GPU computing and Bayesian MCMC analysis.
- Spatial Data Science & Analytics
- GIS & Spatial Statistics
- Remote Sensing & Earth Observation
- Urban & Environmental Analytics
- Machine Learning
- LiDAR & Point-Cloud Analysis
- Natural Language Processing
