Litter sources and hotspots are poorly measured at an actionable granularity
#00081
Decision-makers rarely have street-level data on where litter concentrates, what it is, and which brands it come from. Without comparable, location-specific evidence, interventions can't be targeted, producers can't be held accountable, and nothing can be proven to work.
#00098AI and machine-vision litter auditing for objective measurement
Use camera surveys (handheld, vehicle- or drone-mounted) with machine-vision models to automatically detect, count and classify litter over large areas — producing objective, repeatable density data to target action and rigorously measure whether interventions worked.
#00097Open citizen-science litter mapping with geotagged, brand-level data
Let anyone photograph and geotag litter via an app that records type, material, and brand into an open database. Crowdsourcing builds real-time, street-level maps revealing hotspots and brand-level evidence for producer accountability.