STURM-FloodDepth Flooded Cars

This dataset is designed for training deep learning models in urban flood depth estimation using street-level images of submerged vehicles.

The dataset consists of 3,367 images categorized into five flood levels based on water height relative to vehicle features (e.g., tires, chassis, windows). 

The images were extracted from an original dataset (https://github.com/ywszxdd/original_dataset, reference paper: Automatic detection of urban flood level with YOLOv8 using flooded vehicle dataset), cropped using original bounding boxes, with a 20% enlargement around the detected objects. Additionally, all images were resized so that the smaller dimension is at least 224 pixels, with a maximum scaling factor of 4×, using  Enhanced Deep Residual Networks for Single Image Super-Resolution  models.

The dataset is divided into five folders, each corresponding to a specific flood depth level. Additionally, three text files define the training, validation, and test splits.

Split
Level 0
Level 1
Level 2
Level 3
Level 4
Total

Train
640
564
892
356
240
2,692

Validation
80
70
111
44
30
335

Test
80
72
113
45
30
340

Total
800
706
1,116
445
300
3,367