Flood Dataset-1

The Flood dataset -1 dataset is a large-scale, unified image collection designed to train deep learning models for the automatic detection and fine-grained quantification of urban flood levels. Addressing the disjointed annotation schemas of previous resources, this dataset provides a consistent, pixel-level re-annotation of images exported in the standard MS COCO JSON format for seamless integration with major computer vision libraries. The dataset incorporates non-flooded negative samples to minimize false positives. It employs a unique "regression-ready" 45-class schema that frames flood estimation as a fine-grained object detection task. By linearly mapping the 11 granular submersion levels (0 to 10) of four reference objects—Person (classes 1–11), Bicycle (12–22), Car (23–33), and Bus (34–44)—alongside a general "Flood" class (45), models can mathematically derive ground-truth regression targets directly from the class IDs.