City of Munich Innovation Competition

How can we involve students in Munich in the creation and updating the city's geospatial data in an active and educational way, while at the same time strengthening their connection to their neighbourhood?

The GeodataService Munich manages the geospatial data that is available across the city, including the locations of trees and other objects in public spaces. Established surveying methods are used to capture and update these urban data sets.

We are looking for an innovative and mobile application for crowdsourced data collection with an educational value to improve verification and updates of the geospatial data. This tool should enable students to actively participate in mapping process during lessons and in their free time. In this way, data errors can be corrected and additional properties can be recorded. To improve the quality of the data, an AI integration could be implemented, for example, to identify the tree species. 

However, the solution is not only aiming at improving existing urban geospatial data. It should also increase students’ understanding of the smart city approach, provide an educational benefit, and foster a sense of connection with their own neighbourhood. Thus, a virtual rewarding or ranking system could be another component of the submitted solution.

To ensure optimal integration into the city's geospatial data infrastructure, OGC-compliant data models and open, standardised exchange formats or data interfaces are preferred.

Contact persons for topic-related questions:

Department of Communal Services 
GeodataService Munich
Korbinian Kringer
phone: +49 89 233 724151
digitaler.zwilling@muenchen.de

How can we make the city’s waste and rainwater disposal services visible and tangible for Munich citizens and other interested parties?

The Munich Wastewater Mangement (MSE) significantly contributes to the quality of life in Munich by ensuring secure drainage of sewage and rainwater. Yet the services mostly take place in the background and generally go unnoticed by citizens. To make the importance of this city infrastructure visible and tangible we are looking for innovative solutions using currently available technologies.

The goal is to create transparency and awareness and to inform citizens about what goes on underground and how their taxes are spent. A further goal is to contribute to educating and raising awareness, for example to explain what belongs in the wastewater pipes and what does not or the effects of different options of handling rainwater.

Innovative solutions could for example include:

• Use of immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR)
• 360-degree views and virtual experiences that offer insights into underground structures.
• Mobile access e.g. via QR codes that allow users to access information on their smartphone or tablet.
• Virtual information boxes with links to video sequences and other content.
• Inclusion of third party media to explore the topic in more depth.
• Interactive digital platforms that provide information about the city’s infrastructure.

Other creative and innovative ideas are very welcome.

The solution should effectively supplement existing offers such as sewer canal tours or information campaigns, without any time constraints.

During the co-creation phase, the MSE invites candidates to implement and test their solution in a street section as a real-life test field, supported by the MSE.

A solution could also serve as blueprint for other underground infrastructures in the future.

Contact persons for topic-related questions:

Munich Wastewater Management
Steffen Graf
phone: +49 89 233 61892
kommunikation.mse@muenchen.de

How can the Munich Wastewater Management electronically read the data of 9,000 scanned paper plans to increase the maturity level of the sewer canal network’s digital twin?

The scans contain important structural information such as the wall strength of the canals or diameter of a shaft. This data is crucial for our digital twin. Essentially the task is to read data from PDFs and transfer them into the company system.

This use case is not only relevant for the Munich Wastewater Management (MSE) but is also important for all technical municipal companies. In addition there are many disposal companies worldwide that manage a similar data volume in PDF form. Numerous utility companies such as municipal utilities (electricity, water, gas) or international pipeline operators (gas, crude oil, chemicals) or electrical grids could benefit from this solution. The biggest benefit would be for companies that have, for historical reasons, installed very different network objects (shafts, valves, access structures).

For this use case the MSE will provide a comprehensive test bed allowing access to the GIS and the city’s AI, MUCGPT. If the application is successful we could also integrate the solution in other municipal companies.

The sewer canal network is a critical infrastructure of the City of Munich and this should be taken into account when working with the data.

Our specific use case:

The MSE operates 37,000 canal shafts and 4,000 special structures that allow canal operators to safely enter the underground sewer canal network of the City of Munich. Renovation, repairs and cleaning take place there. The most important key facts on the canal shafts are known: They are mostly found in the centre of roads. On busy roads they are located on the side to allow safe entry. All canals are travelled by robots in fixed time intervals. The approximate coordinates and the shaft depths are recorded in the geo information system (NIS) of the MSE.

Other detailed information is hidden away in 9,000 drawings that have now all been scanned. These drawings can be opened via a link in NIS.

The most important shafts were always built as the same standard shaft and exactly 16,057 times. There are also other shaft types that vary from common to rare. These are documented in these 9,000 drawings.

You can see an example of such a drawing here.

Required information:

1. Inner diameter of the shafts
2. Wall strength of the shaft
3. Other relevant characteristics such as material or existing special structures (e.g. valves, overflow thresholds, pumps) 

This information would provide us with the following benefits, for example:

1. More precise location of a building’s outgoing pipes.
2. More precise measurement of a shaft’s backwater volume.
3. Better use of underground space.
4. More extensive analysis of canal data.

We look forward to your innovative ideas!

Contact persons for topic-related questions:

Munich Wastewater Management (MSE)
Bernhard Agerer
Head of Canal Geoinformationssystem NIS
phone: +49 89 233 62336
mobile: +49 152 5794 6688
bernhard.agerer@muenchen.de

How can we use autonomous agents (Agentic AI) to tap into the scattered knowledge of the city administration so that case workers in the Department of Public Order and District Administration are able to make complex case decisions faster, more legally secure and independent of individual knowledge holders?

Public services in Germany are facing an enormous turning point: Approximately 25% of employees will enter retirement in the next 10 years. This risks an unprecedented loss of implicit specialist knowledge and procedural experience. Employees, especially new colleagues, are already spending disproportionate amounts of time looking for information in fragmented data structures (server networks, wikis, email chains, old reports).

The current “pain” in everyday work: A case worker often has to wait days or weeks for an answer to a specialised detailed question because the only person who “knows where it is” is too busy or not at work. This delays procedures for citizens such as gastronomy permits and endangers decision-making quality.

The aim:

We are looking for an AI solution that goes beyond the level of a simple chatbot. The aim is an autonomous agent that

1. Actively connects: Independently combines information from documents, scans, wikis and specialised procedures.
2. Understands context: Recognizes whether a report (e.g. 20 years old) is still based on current legal regulations.
3. Prepares decisions: Delivers precise, source-based answers that can flow directly into processing a case.

The solution is to be initially developed for the area of commercial issues and consumer protection of the district inspection in the Department of Public Order and District Administration. For example, we imagine in this case that when looking for information regarding a restaurant licence, an AI-supported system would indicate that while a report it has found covers the issue being looked for, said report is already 20 years old and legal regulations have potentially changed. The AI could replace the memory of employees that are otherwise being constantly asked.

We are looking for creative approaches that meet the following criteria.

• Simple accessibility: The system must be easily and at all times accessible for all employees in the relevant specialised departments.
• Interactive user interface: A user-friendly interface that facilitates simple interaction with the system is crucial.
• Fast location of information: The system should offer an intuitive search function that enables employees to easily and quickly find the required data.
• Integration of an AI chat interface
• Continual updates: The system should be able to continuously update and filter information in order to ensure that the data is always up to date.
• Error-free and data protection-compliant data processing: All processes must be compliant with data protection laws and ensure high data integrity.
• Transparency: The source of the generated answers must be verifiable at all times.
• Integration in the city IT landscape: Integration into the city’s internal AI chat solution MucGPT is desirable but not strictly necessary (Open Source)
  • Integration via the MCP protocol and the develop of an own assistant within MucGPT would be desirable.
  • Documentation on MCP
  • The IT department is currently in the process of acquiring an AI host for confidential data. This is to be used for processing confidential and personal data.
  • Available models

We are looking for startups with basic experience in AI technology to work closely with employees of the district inspection and the AI Development & Services team in the IT Department during the co-creation phase. The project scope will be decided jointly and regular consultation and milestone dates are planned. The aim is to develop and evaluate a functioning prototype that is ideally scalable in all areas of the municipal administration which require specialist knowledge.

Tap into your innovation power and shape the future of the Munich City Administration!

Contact persons for topic-related questions:

Department of Public Order and District Administration
Trade and Industry, Consumer Protection
Lena Jocham
phone: +49 89 233 45789
lena.jocham@muenchen.de

District Inspections
Petra Weber
phone: +49 89 233 45757
p.weber@muenchen.de

How can the City of Munich use existing data from the ÖKOPROFIT programme to assist participating companies with AI-supported analyses and recommendations to assess and improve their environmental record?

The environmental consultation programme ÖKOPROFIT  has been helping companies improve their environmental performance and reduce their carbon footprint since 1998. The programme offers, for example, workshops and on-site consultation for which various work materials are used. The existing Microsoft Excel-based work materials covering various environmental indicators and sustainability aspects have already been used across Germany by around 4,500 companies.

The City of Munich is now looking for innovative solutions to build on these ÖKOPROFIT work materials. We would like to develop an AI-based solution that makes it possible to efficiently analyse collected data and, based on this, offer participating companies a benchmark analysis and tailored recommendations to improve their environmental record. This means participating companies would be offered a real added value and ÖKOPROFIT would be designed in a futureproof way in order to increase the appeal of this city offer and secure a permanent demand.

We are looking for creative and innovation-driven startups or founders with experience in using AI for data analyses and have basic knowledge in the area of sustainability reporting. The solution must be user-friendly and ensure data protection in that it exclusively uses anonymised data.

As part of a co-creation project the companies of the current ÖKOPROFIT Club 2025/2026 should serve as a test bed for collecting initial data and testing the AI solution in practice. The goal is to achieve a significant improvement in data processing and use within the ÖKOPROFIT working materials that can also serve in the long-term as a model for other municipalities as part of the ÖKOPROFIT network in Germany.

Contact persons for topic-related questions:

Department of Labor and Economic Development
Sustainable Business & Mobility Team
nachhaltigkeit.raw@muenchen.de
Andrzej Michalski
phone: +49 89 233 520508
Christian Rothe
phone: +49 89 233 25516

The following teams won the awards in 2023:

Topic: „Healthy senior citizens: "Digitalize a service and care network“, winner: Helferportal GmbH & Co. KG

Topic: „Expand places in Munich with digital innovations", winner: Team Puppet

Topic: „Digital monitoring of Munich's Diesel-free zone", winner: Transcality AG

Topic: „Text and the City - text mining in city development “, winner:  Legal-Pythia LLP

More information (in German).

The following teams won the award in 2022:

• Winner of the challenge “Raising awareness of climate change”: Cityscaper GmbH
• Winner of the challenge “Development of a cemetery app”: CI Cloud Item GmbH and proband15 GmbH
• Here you can find more information on the projects and the all the award winners from 2022.

The following teams won the award in 2019:

Topic: Waste avoidance in Munich's schools, winner: upride (formerly  bike|solutions )

Topic: Weekly digital market – Penetrating new target groups, winner Digitaler Wochenmarkt München

Topic: Self-generating calendar of cultural events for urban districts, winner: Neighbor