Our dataset comprises $23.468$ non-labelled and $356$ labelled samples where each sample is $512 \times 512 \times 1$ dimensional IR image collected with the thermographic measurement specifications. Some samples contain scars, shadows, salt \& pepper noises and contrast burst regions, demonstrating that realistic laminar-turbulent flow observation scenarios are subject to high noise. Besides, a laminar flow area may occur brighter or darker as compared to the regions in a turbulent flow. Due to some effect (e.g. shadowing the sun) it is even possible that, in one part of the image, the laminar flow area appears darker, and in another part, it appears brighter than the turbulent flow area.

As you can see in the cover sample: Thermographic measurement examples from wind tunnel and flight test experiments: i. top and bottom row: wind tunnel ii. center row: vertical stabilizer from AFLoNext Project. Note that the red flow-separation lines were semi-automatically drawn as ground-truths by an internal software of our institution. In principle, the software took some pixel samples selected by human experts for each flow region as input, and it accordingly drew laminar flow boundary after statistical analysis on the selected pixels. Finally, if mislocalisation happened in the separation lines, human experts corrected them in an iterative way.