Fixi - Public space notifications

Municipality of Dantumadiel

Anyone can make a report about public spaces to the municipality via the Fixi app or website. This could be about rubbish, a defective lamp post or water nuisance, for example. A dangerous traffic situation or nuisance can also be reported. Fixi is the municipality's notification system for resolving reports in public spaces.

Last change on 4th of May 2026, at 10:19 (CET) | Publication Standard 1.0

• Publication category: Impactful algorithms
• Impact assessment: Field not filled in.
• Status: In use

General information

Theme

Organisation and business operations

Begin date

2024-04

Contact information

[e-mail]

Link to publication website

https://www.dantumadiel.frl/contact

Responsible use

Goal and impact

If something needs to be fixed or cleaned up on the street or in a park, it can be reported to the municipality via the Fixi app. A dangerous traffic situation, housing nuisance or nuisance from people and the hospitality industry can also be reported. The reporter himself chooses which main category fits the report best (e.g. 'nuisance' or 'street furniture'). Municipality employees check the reports submitted and divide them into groups. If a report goes to the wrong person, it is quickly transferred to the right handler. A predetermined processing time is linked to each category.

Considerations

The reporter also has the option to add photos of the situation via the app or the website. By adding a photo to the report, the municipality can more quickly see what the report involves. The photos may show persons or license plates. By automatically making persons and license plates unrecognisable, the privacy rights of the persons are safeguarded.

Human intervention

Each report is still reviewed internally and assessed by the handler. Notifications that have arrived at the wrong group are converted manually. The notification then arrives at the correct treatment group.

Risk management

The algorithm has no impact on a decision, but is designed to make any personal data unrecognisable in photos. Municipal employees see the original photos and dates. Residents see the censored photos. The algorithm may not properly recognise a personal data, this will be acted upon on a situation-by-situation basis by the coordinator.

Legal basis

Municipal Act, Roads Act, Environmental Management Act, APV

Operations

Data

The algorithm in the Fixi app processes data on reports in public spaces. This data is mainly about the type of report (such as broken lights or litter), the location and when the report was made. The information is used to automatically categorise reports and forward them to the right department or employee.

In addition, the submitter can choose to also provide personal data, such as name, address (not mandatory), phone number and/or e-mail address. These data are only used to contact you about the report if necessary.

It is up to the submitter whether these personal data are included: a notification can also be filed without sharing these data. Once a report is submitted, all personal data are automatically anonymised before the algorithm processes the report further. A staff member checks this and performs additional anonymisation if necessary.

Technical design

Number plate recognition is performed with an ALPR module that combines OpenCV's edge detection techniques with Pytesseract's LSTM-based OCR engine for accurate character extraction.

Face detection is performed with the RetinaFace library, which uses a deep learning-based single-shot detection algorithm optimised for localising facial features.

For text detection, we use EasyOCR, which uses the CRAFT algorithm for identifying text regions and CRNN for recognising text content.

To ensure privacy, sensitive areas such as faces, number plates and specific text elements are blurred:

1. Gaussian convolution for image smoothing,
2. Colour space transformations for preprocessing,
3. and bitmap rendering for annotations such as rectangles and labels.

For text masking, we use a Named Entity Recognition (NER) module with the BERT algorithm, which provides contextual understanding for identifying sensitive textual entities. These entities are then masked using regular expression-based pattern matching algorithms.

External provider

Decos