Measuring street cleanliness with cameras on sweepers

What was done

In an interview published by Digital360 on September 3, 2025, Bourg-en-Bresse's Cleanup department explained that it lacked an overall vision, could not justify certain actions and did not have reliable data for reporting required by eco-organizations. He discovered Cortexia's solution at an event of the Association of Cities for Urban Cleanliness. Cameras installed on sweepers, including the one operating in the commercial and tourist hypercentre, capture images of roads, sidewalks and canves. Cortexia analyses them in SaaS mode to categorize, quantify and geolocalize waste, including butts, and returns them to an interface. The data are used to map cleanliness, adjust the resources allocated to each neighbourhood, evaluate the effectiveness of the actions and inform the inhabitants. The installation was carried out by the municipal garage in two half days. The subscription of EUR 60 000 per year for three schemes was covered by the financing of extended producer responsibility provided by Citeo and Alcome.

Before you start

Tool cited by the source: Cortexia. Fill in the bracketed items: [tableau]. Strip confidential data that is not needed and check your tool’s access terms. Features and plans can change.

The method, in three steps.

1. 01

   Gather the context

   Spot the bracketed items in the prompt ([tableau]) and prepare what they ask for.

2. 02

   Adapt the prompt

   Copy the prompt below, replace the bracketed items, then paste it into the AI assistant you use. The cited tool (Cortexia) is not necessarily available to the public..

3. 03

   Reread before using

   Reread the result, correct it if needed and keep the final decision. Caveats are detailed below.

Your starting point.

Replace the bracketed items, then adapt the proposal to your situation. This prompt is written by the iadujour desk It is not a quotation from the source.

Here is an export of cleanliness records per street per week (street, neighborhood, waste type, quantity): [tableau].

1. Classifies neighborhoods at least dirty and shows evolution over the period.
2. Finds the streets where a type of waste dominates (butts, dumps, deposits).
3. Proposes a reallocation of cleaning passages and targeted awareness-raising.
4. Indicates the limitations of these data before drawing conclusions.

What deserves your attention