Commons:Free media resources/Photography/Crowdsource app
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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.
How to import
[edit]- If you have a Google account, go to https://crowdsource.google.com/images.
- In the search bar, type something like locomotive/street/sign/device/etc or a country/city/brand name.
- In the search results, choose any picture that:
- Has encyclopedic value.
- Embeds enough context in the picture itself. For instance, if it is unclear in what country a nature picture has been taken, then it is not useful.
- Complies with all Commons policies. For instance, remember that many African countries do not have freedom of panorama.
- 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.
- In addition to other relevant categories, add this category to the picture: Contributed via Google Crowdsource app
- 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
[edit]- Mostly from Africa and other underrepresented regions. These are the regions from where Commons and the Wikipedias need pictures the most.
- All pictures are recent.
- About a third of the pictures are of good photographic quality.
- A good 5% of the pictures show something that has a Wikipedia article but no Commons picture yet.
Warnings
[edit]- 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.