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Computer algos do a good job at asteroid id'ing - they discover a few every day - but most experts I've spoken with (including people running large sky surveys

by typpo 13y ago

Computer algos do a good job at asteroid id'ing - they discover a few every day - but most experts I've spoken with (including people running large sky surveys like PanSTARRS and CSS) agree that crowdsourcing is an approach worth trying. I've updated my post with a few of the reasons why, which generally relate to the unreliable quality of sky survey images. From personal experience, the algorithms and data used by sky surveys are not readily available to the public. This inhibits innovation. Without a PhD, there is little opportunity for me to build on state of the art and improve them directly. I agree that with the images I've collected, there may be some interesting algorithm/ML opportunities. Part of the reason why I started with crowdsourcing is to get a decent training set. I appreciate the feedback!

danso 13y ago
Thanks for the reply and update! Also, I just realized you have a link to the source repo, which would answer a few of my questions. If there are certain conditions in which false positives arise frequently from image analysis, perhaps a refinement of the parameters (if photo_taken_during_hazy_night && by satellite_in_position(x,y)_relative_to_viewport, then increase fuzziness_factor) might also cut the noise. From my layman's perspective, the problem seems to be "easy" because of the relative simpleness of the shapes (whitish dots on a dark area) and expected movement patterns...And so the fact that it isn't means that there's other problems (data quality) or a major flaw in (my) assumptions...which is even more interesting. Conversely, it'd be interesting to set up a measurement-framework to test which conditions are users most likely to report false positives. Anyway, these are all details that may also have been overlooked by those in the field and that even if they realized it, they don't have a strategy (or see the need) to perform such meta-analysis. Another way that an outsider can bring freshness to a field.

by typpo 13y ago

danso 13y ago

Thanks for the reply and update! Also, I just realized you have a link to the source repo, which would answer a few of my questions. If there are certain conditions in which false positives arise frequently from image analysis, perhaps a refinement of the parameters (if photo_taken_during_hazy_night && by satellite_in_position(x,y)_relative_to_viewport, then increase fuzziness_factor) might also cut the noise. From my layman's perspective, the problem seems to be "easy" because of the relative simpleness of the shapes (whitish dots on a dark area) and expected movement patterns...And so the fact that it isn't means that there's other problems (data quality) or a major flaw in (my) assumptions...which is even more interesting. Conversely, it'd be interesting to set up a measurement-framework to test which conditions are users most likely to report false positives. Anyway, these are all details that may also have been overlooked by those in the field and that even if they realized it, they don't have a strategy (or see the need) to perform such meta-analysis. Another way that an outsider can bring freshness to a field.