This supplementary document follows the Datasheets for Datasets template of (8) to document the Global Flood Forecasting (GFF) dataset and its creation. Further resources are provided: in the accompanying publication https://arxiv.org/abs/2409.18591 in the GitHub repository https://github.com/Multihuntr/GFF

To ensure quality, all charts and questions are handpicked, curated, and verified by human experts.

Inspired by the above motivation, we collect real cunning texts as our raw data from a famous Chinese online forum, the "Ruozhiba" (retard forum)

While successful, these methods are mainly designed for sampling from unconstrained domains.

We decreased the confidence threshold to 0.1 to increase article and headline The following specifications were used: { resolution: 256, learning rate: 2e-3 }. This limit is binding for common words, e.g., "the". The recognizer is trained using the Supervised Contrastive ("SupCon") loss function [7], a gener-45 In particular, we work with the "outside" SupCon loss formulation We use a MobileNetV3 (Small) encoder pre-trained on ImageNet1k sourced from the timm [19] We use 0.1 as the temperature for Center Cropping, to avoid destroying too much information. C (Small) model that is developed in [2] for character recognition. If multiple article bounding boxes satisfy these rules for a given headline, then we take the highest.