Ricardo Barros Lourenço

arXiv2023

Detangling the role of climate in vegetation productivity with an explainable convolutional neural network

Forests of the Earth are a vital carbon sink while providing an essential habitat for biodiversity. Vegetation productivity (VP) is a critical indicator of carbon uptake in the atmosphere. The leaf area index is a crucial vegetation index used in VP estimation. This work proposes to predict the leaf area index (LAI) using climate variables to better understand future productivity dynamics; our approach leverages the capacities of the V-Net architecture for spatiotemporal LAI prediction. Preliminary results are well-aligned with established quality standards of LAI products estimated from Earth observation data. We hope that this work serves as a robust foundation for subsequent research endeavours, particularly for the incorporation of prediction attribution methodologies, which hold promise for elucidating the underlying climate change drivers of global vegetation productivity.

physics.ao-ph↗

arXiv2017

Assessing verticalization effects on urban safety perception

We describe an experiment with the modeling of urban verticalization effects on perceived safety scores as obtained with computer vision on Google Streetview data for New York City. Preliminary results suggests that for smaller buildings (between one and seven floors), perceived safety increases with building height, but that for high-rise buildings, perceived safety decreases with increased height. We also determined that while height contributing for this relation, other zonal aspects also influences the perceived safety scores, suggesting spatial structuring also influences such scores.

stat.AP↗