Within the Space It Up project, my research on Local Climate Zone (LCZ) mapping focuses on developing automated and scalable classification workflows to characterize urban and peri-urban environments. The goal is to improve the spatial understanding of urban form and land cover patterns that influence local climatic conditions. I employ advanced remote sensing data and machine learning algorithms to automatically delineate LCZ classes, minimizing manual interpretation and improving consistency.

A second core component of my research deals with analyzing Surface Urban Heat Islands (SUHI) using hyperspectral and thermal remote sensing imagery. This study examines the spatial and temporal dynamics of surface temperature variations within cities, aiming to identify the physical and material factors shaping heat distribution. Hyperspectral data are used to derive detailed information about surface materials, vegetation stress, and albedo, while thermal infrared imagery captures land surface temperature patterns.

Click here to view my poster.