While SETI@home was my first crowdsourced venture and will always hold a special place in my heart (see earlier post on aliens), crowdsourcing imagery interpretation runs a very close second. My professional life up 'til I returned to school revolved around mapping remotely sensed data (aerial and satellite imagery, lidar, radar, sonar). I applied image and data analysis techniques to help understand and manage a variety of things, from navigational waterways to air quality, from agriculture to disaster response and recovery. It's a powerful tool for helping us manage our world, but it can be a very time consuming process. Automated techniques only get you so far until a person must get involved to make the final distinctions. Enter the public.
Crowdsourced image interpretation has been successful for contributing to humanity's collective understanding of our landscape and natural resources. Data quality management is more significant for a crowdsourced project than one with trained staff, but can be well worth the time. For example, volunteers have contributed to our understanding of the rain forest canopy https://news.mongabay.com/2017/11/crowdsourcing-the-forest-for-the-trees/. The COVID-19 shutdown even gave a volunteer time to make a significant archaeological find, https://www.theguardian.com/education/2020/apr/28/lost-henge-digging-archaeology-online-during-lockdown.
Speaking of disasters, crowdsourced disaster mapping has been around since the Haiti earthquake in 2010 and has been helpful since. The merger of mapping and social media tools makes on-the-ground, as-it-happens crisis mapping a powerful tool for events from pandemics to protests.
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