Set Prediction for Next-Day Active Fire Forecasting

Predicting where active-fire clusters will occur over the next 24 hours is a sparse localisation problem with a variable number of targets. We introduce WISP, a query-based framework that formulates next-day active-fire forecasting as point-set prediction rather than dense raster prediction. WISP combines 48 hours of meteorological and fire-history inputs with satellite-derived vegetation, land descriptors, and weather covariates over the prediction horizon to produce a confidence-ranked set of future fire-cluster centres. The model integrates multi-source spatio-temporal encoding with a query decoder trained through bipartite Hungarian matching. We use asymmetric classification–localisation weighting in matching and loss, and investigate how this weighting and query capacity affect localisation, ranking, and redundant predictions. We construct a globally distributed benchmark spanning 2014–2024, with targets derived from the next-day union of VIIRS active-fire observations on a nominal 375 m reference grid. Under ERA5 reanalysis-conditioned evaluation, experiments reveal a trade-off between cluster recall and prediction-set compactness, with continuing fire activity easier to predict than newly observed activity. These findings support set prediction as a viable formulation for next-day active-fire cluster forecasting and highlight the importance of evaluating recall alongside false alarms and duplicate predictions.