Lalita

Transformer-based Deep Learning for Spatiotemporal Forecasting of Natural Resource Availability

ST-ResFormer is proposed, a spatiotemporal transformer architecture that fuses a graph-attention spatial encoder with a patch-based sparse-attention temporal encoder through a cross-attention fusion block, enabling joint multi-horizon forecasting of three coupled resource variables from heterogeneous data sources.