How I am building a community project for public monitoring of Amsterdam’s canals state with AI
Hello everyone! First, let me share the motivation. Amsterdam is widely known for its aquatic ecosystem- a network of canals (gracht in Dutch) cover the whole city. And I have to admin that these canals are generally clean and taken good care of. However, as I have personally witnessed multiple times, in some specific cases there could be challenges too. Namely, sometimes you can spot floating trash, algal blooming is typical for spring / summer, rarely some smell can happen too. But the real challenge is to monitor the situation closely along over 100 km (60+ miles) of canals. And this is where my project https://waterwatch.ai jumps in.
The idea is very simple: anyone is Amsterdam can easily send a report (photo + location). AI analyzes the report - identifies the problem and estimates its criticality. But the main part of the project is the open and publicly available map where all these problems are depicted. That’s what I believe makes this project special. Instead of sending an official report and waiting for days for response, users inform the community and the community has the whole picture and the understanding of the situation across the city.
Now, let’s get to the building. I came up with the following simple design: user sends a report via telegram bot, the backend part (django + ollama local model) processes the report and stores the result in db and 3: a regular cron job runs the report generation + pushes the report and fresh map points to a static github-hosted website. I use telegram bot as a platform for sending reports as it provides minimal friction for a user but still provides all the required functionality - no separate app or registration needed, indeed the whole process for an absolutely new user is about a minute. Django seems to be quite a common solution for a project like that and I personally feel comfortable with it. Processing the reports and generating report with local model allowed us not no share data with third-parties and keeps the maintenance costs reasonable at this stage. Once the data is processed and the report is generated (with a regular job) it is automatically pushed to the repo. Github-hosted static website is reliable but there is more about it - storing the data in the public repo reflects the philosophy of the project to keep it open and accessible (including history from older commits)
For my own tests it worked reasonably well - identifying small scattered and bulky trash in water. It also identified algal blooming well, distinguishing it from green reflection of the trees. Nevertheless, we continue testing and if you are from Amsterdam you are welcome to join!
Looking forward to your feedback and ideas, productive discussion. And, of course, to share how it goes further. Thanks!
https://waterwatch.ai