Yeah, I am with @Richlv on that. Imho most of the problems come from the fact that the algorithm’s parameters are skewed against snow. Like if snow was generally a bad thing? At least this is what we can read in the announcement blog post. Just think about polar or subarctic regions of the world where in some places snow is present all year round. Sure, snow covers surfaces and if you are interested in mapping, say, road surfaces and you cannot see the road surface because of snow then this is not the fault of the image author or the image itself. It is just how nature works. The same is true about any other natural atmospheric phenomena, like rain, sand storm, or dust etc. Image quality should not measure what we can see or make out in the image but should be first and foremost a metric of photometric quality, like sharpness/blurriness, grain, illumination, amount of pure black areas etc.
However, we do not want to have photometrically perfect photos of “some piece of pavement only” or “interiors of a bag“ either. So, the algorithm should rather also factor-in just one combined street-level scenery parameter, answering the question “Is this a street-level image?”, since this is what Mapillary is all about (at least for now). Which I guess was the initial idea but I am quite sure that having only snow or any other particular atmospheric phenomenon in the equation is not the way to proceed with that. Atmospheric phenomena should not impose a penalty on the quality score of an image.
Besides, I am a bit confused why the scale is 0, 1, 2, 3, 4, and 5? What is wrong with [0.0 to 1.0]? I mean Amazon stars may work fine for product reviews but not for images.