There are quite a few datasets for training and testing computer vision algorithms, such as the ImageNet database and PASCAL databases. Most of the these databases are created from images with various restrictive licenses. Are there databases (with training data, such as classifications, segmentations, etc.) that are based on open access images (ie. Creative Commons, public domain, or the like) or are there meta-projects to help create such databases?

A possible example would be a project to collect open access images that are well suited for computer vision algorithms (particularly natural everyday images, not mathematical plots, cartoons, etc). I'm mainly interested in data for tasks that humans can perform, such as object detection, classification, object segmentation, etc.

mldata.org is perhaps the closest thing that I've found. They give the licenses of the datasets, although they don't provide a way to filter by license or type of license and it doesn't seem like they are always accurate.

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I don't know if this is exactly what you're looking for, but you might want to look into scientific feature catalogs and their underlying data.

For instance:

  • Solar Physics. Catalogs are available from HELIO and event listings from HEK. Input imagery is available from VSO, and some more easily used (but compressed) products available from Helioviewer. You can find info about the various detection routines from the SDO FFT website.

  • Lunar & planetary craters. Input images available from PDS. You can find catalogs from Astropedia (just search for 'catalog' ... you'll also find volcanic vents, etc).

I would assume that there'd be similar stuff for earth sciences ... but it's not a field that I'm as familiar with.

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