MV Likoni, uploaded via Crowdsource, and best picture of this ship we have so far.We had "Textile Mills in ..." categories for most US states, but zero picture for Pakistan, the 4th largest textile producer in the world.
The Crowdsource app markets itself as a way to fight bias in Artificial Intelligence. Thanks to the developer's strategy/campaigns, users are mostly people from digitally underrepresented countries. Users can submit pictures of their surroundings, and are asked whether they want to make their contributions CC-BY or not. About 400,000 CC-BY pictures have been uploaded so far.
Has "CC BY 4.0" written at the bottom of the webpage.
Right-click to download the picture.
Using the Upload Wizard, upload the downloaded file and information, in particular and picture URL and author name which caan be found at the bottom of the webpage.
The Upload Wizard forces you to select a precise date, so choose today's date then edit the uploaded picture's page to replace it with: {{other date|between|2016|2022}}
Alternatively, for instance if you do not want to use a Google account, pictures and metadata can be downloaded in bulk.
Why this dataset can be beneficial to Commons and Wikimedia
The pictures have no geolocation nor description. This limits us to pictures where the subject can be identified with 100% certainty, for instance helicopters/superyachts/places/etc.
Labels shown on the side of each picture are unreliable. At best they can be a starting point for investigation, for instance if a picture of a stadium is labeled "Monrovia" you may want to search for stadiums in Monrovia and see whether any match the one in the picture.
Pictures are not dated. We only know that all pictures were uploaded between 2016 and now.
All faces are blurred. This is not a good dataset if you are looking for faces of people.
The web search interface only shows the first 50 results, so be creative with search keywords, or download in bulk and write a script.