Michael Haberl

How much does the graph help? An information-parity evaluation of graph neural networks for urban air-quality estimation

Graph neural networks (GNNs) are increasingly used to estimate air pollution at unmonitored urban locations, but reported gains often conflate what a model is with what it sees. We present a heterogeneous, wind-aware graph attention network that embeds regulatory monitoring stations and traffic counters as distinct nod...