P2I-GAN Benchmark: Deep Generative Framework for Spatio-Temporal Rainfall Reconstruction from Sparse Gauges
Abstract
P2I-GAN is a deep generative benchmark for spatio-temporal rainfall reconstruction. It formulates rainfall interpolation from highly sparse and irregular rain-gauge observations as a video inpainting task. This repository provides:- Training pipelines- HDF5 and Zarr-based dataloader- Evaluation utilities- Visualization tools- Pretrained model weights The framework enables reproducible research in rainfall interpolation, spatio-temporal modeling, and nowcasting.
Fields of Study & Subjects
Precipitation Measurement and Analysis
Hydrological Forecasting Using AI
Generative Adversarial Networks and Image Synthesis
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 10 February 2026
- Language
- EN