Public space reports

Municipality of Velsen

Anyone can make a report to the municipality about public space (e.g. about litter, a defective lamp post or flooding) via the Fixi app. Fixi is the municipality's notification system to resolve reports in public spaces.

Last change on 22nd of July 2025, at 9:27 (CET) | Publication Standard 1.0

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

General information

Theme

Public Order and Safety

Begin date

03-2025

Contact information

[e-mail]

Link to publication website

Melding openbare ruimte | Gemeente Velsen

Responsible use

Goal and impact

If something needs to be fixed or cleaned up in the street or in a park, it can be reported to the municipality via the Fixi app, the reporting system of the municipality of Velsen. A dangerous traffic situation, housing nuisance or nuisance from people and the hospitality industry can also be reported. The notifier himself chooses which main category fits the report best (e.g. 'nuisance' or 'street furniture'). Employees of the municipality check the submitted reports and divide them into sub-categories, so that the report reaches the right municipal handling team. Associated with each (sub-)category is a predetermined handling period.

Considerations

Photos can be added to reports, for example of the malfunction or problem. These photos must not violate residents' privacy. The algorithm aims to catch this, by blurring text, license plates and people. This way, residents will know which reports have already been submitted, without any personal data being visible.

Human intervention

Reports are assessed and distributed by a coordinator. Reports on waste dumping are forwarded directly to HVC. Reports on enforcement and nuisance go directly to team Toezicht Handhaving Openbare Ruimte. Reports received are assessed at both HVC and THOR.

Reports in the categories enforcement and nuisance are protected from public view. Residents therefore do not see these on the map.

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, Housing Act and Building Decree, APV, Wmo

Operations

Data

Field not filled in.

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:

• Gaussian convolution for image smoothing,
• Colour space transformations for preprocessing,
• 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