BEEMind

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BEEMind

Technology for resilient buildings and cities 4.0

• BEEMind is an AI powered environment that integrates three key pillars: a semantic web technologies to make data fully interoperable, a big data architecture to manage large data volumes, and a set of intelligent tools to analyze, predict, and optimize urban environments.

• It is structured around the following tools:

  • MindCity
  • MindOpera

AI-based solutions to enhance climate resilience in buildings and urban environments

BEEMind tools

MindCity

MindOpera

MindCity

BEEMind tools : MindCity

 Ciutats 4.0 : high resilient cities in practice

This solution helps municipalities and consortia go digital and manage their data effectively on a local level.

It is a key tool for smart cities, energy planning, adapting to climate change, and making decisions about urban retrofitting

What does it do? It checks out KPIs for buildings and helps plan for climate change:

• City microclimate model

• Rates how vulnerable buildings are to climate issues

• Forecasts energy upgrades and adaptation strategies

• Sends heat wave alerts 4 days ahead

• Aids in designing urban green spaces and shelters

Description

Data integration Across Various Scales

3. Data at building level

2. Data at census code level

1. Data at postal code level

BEEMind tools : MindCity

AI powered geo models

BEEMind tools : MindCity

We develop machine learning and Graph Convolutional Neural Network (GCNN) models

To enable microclimate assessment and learning patterns from geo-structured data (buildings, streets, use, energy, etc.).

Estimation of KPIs for all buildings without data

Definition of future climate mitigation and adaptation scenarios

BEEMind tools : MindCity

• Integration, processing, and visualization of large amounts of urban data at the building level

• AI driven models to improve cities and make them more resilient against extreme weather events.

• Figuring out specific geospatial indicators that connect to climate action and the shift towards cleaner energy.

• Evaluation of vegetation and urban climatology scenarios

Funcionalities

Cities 4.0: Environmentally Smart

• Facilitates strategic decision-making in the energy, climate, and urban planning fields

• Optimizes resources and processes for large-scale city data analysis

• Improves coordination between technical departments and citizen interaction

• Highly scalable to any city in Europe

Beneffits

Unlocks powerful data visualization solutions for consultancies

BEEMind tools : MindCity

Use cases

• Optimizes resources and processes
  For large-scale analysis of city data

• Improves coordination
  Between technical departments and citizen interaction

• High scalability
  For any city in Catalonia or the rest of Spain

• Heat map
  Detect vulnerable areas with solar and thermal radiation

• Building energy retrofitting
  Identify and prioritize buildings with the greatest needs

• Vegetation index
  Define and plan green areas

Impacts

Cities 4.0: Environmentally Smart

Technology BEEMind

big data architecture

 BEEMind-ENMA

big data integration

Ingestion

Harmonization

Ingestion

Massive data integration

ENMA in action

Ingestion processes

BEEMind: Data ingestion

• Executed manually or periodically  
• Reading from websites, files, external databases, or APIs.  
• Implemented using Python scripts.

Dades socio-econòmiques

Dades urbanes obertes

Actualització continua dels datasets

MindCity: Data sources

MindCity: Data ingestion

Massive data integration

MindCity: Data sources

MindCity: Data ingestion

Massive data integration

Data harmonization

BEEMind: Data ontology

The ontology at the core of our solutions

Applied semantic web technologies:

Understanding and organizing data is as important as the algorithms themselves

Massive data integration

BIGG: l'ontologia  de dades que enllaça des de sensors a carrers

Reuse

• saref, s4blg, s4city, s4agri:
  • buildings, devices
• ssn:
  • systems, deployments
• geosp:
  • geolocation
• qudt:
  • units

https://github.com/BeeGroup-cimne/biggontology

BEEMind: Data ontology

BIGG: the common data ontology

Case studies: Aj.Barcelona

Climate Vulnerability Map of Barcelona

MindCity:  Climate-Ready BCN

Aj. Barcelona, ECOSERVEIS

Source: ICLEI Action Fund 2.0
Budget: €1M
Objective: Support citizens and public authorities in adapting to extreme climate events and reducing energy poverty
Implemented: From May 2023 to June 2025

Its goal is to provide precise and up-to-date information for decision-making in urban planning, public policy, climate management, and the protection of the most vulnerable inhabitants.

Aj. Barcelona, ECOSERVEIS

Vulnerability map of Barcelona

• Vulnerability estimation using AI and energy simulations

• Export of a large set of socio-economic, energy, cadastral, and climate indicators (KPIs)

MindCity:  Climate-Ready BCN

• The Climate Vulnerability Map of Barcelona is a geospatial analysis tool designed to identify the city’s most vulnerable buildings in the event of extreme heat-related events.
• It includes the assessment of all buildings (61,000) against heatwaves.

Main goal

Added value

CVI

How we estimate the Climate Vulnerability Index (CVI)

MindCity:  Vulnerability map of Barcelona

Climate vulnerability is typically framed within three key dimensions defined in the IPCC's Third Assessment Report (Intergovernmental Panel on Climate Change) in 2001:

Exposure
Sensitivity
Adaptive capacity

Although most studies classify indicators using these three categories, our index introduces additional levels to provide a more nuanced analysis while remaining aligned with the traditional framework.

Climate variability and extreme events

• Track changes over time through indicators such as temperature, precipitation, and vegetation cover. Local data helps assess risks related to heatwaves, droughts, and floods.

Energy indicators

• Explore energy use patterns (gas and electricity consumption). Indoor temperature data is crucial for identifying households at risk during extreme heat events.

Building characteristics

• Assess features such as age, use, and size to understand their impact on energy use, indoor comfort, and structural vulnerability.

MindCity:  Indicators groups (KPIs)

Vulnerability map

Infrastructure indicators
Assess the availability of essential public services (schools, climate shelters, social housing) and the strength of social networks to understand community support capacity.

Health indicators
Analyze the relationship between climate and health outcomes to identify vulnerable populations and prepare healthcare systems.

Demographic indicators
Not all groups are equally vulnerable. Analyzing factors such as gender, age, income, migration status, and unemployment allows for the development of more detailed CVIs.

Socioeconomic indicators
Understand urban resilience through factors such as housing costs, energy poverty, household debt, and gaps in social protection, which worsen during economic crises.

Vulnerability map

MindCity:  Indicators groups (KPIs)

MindCity:  Building CVI

1. Data preprocessing – Select input data to calculate the indicators.

2. Framework selection – Determine which indicators positively or negatively affect the CVI.

3. Granularity definition – Select the spatial scale.

4. Normalization & weighting – Harmonize indicators and assign weights.

5. Aggregation – Combine indicators into groups to obtain a final value.

⚠️ Challenge: Each study adapts its CVI to its specific context and priorities.

Climate Vulnerability Index (CIV)

Vulnerability map

Our approach includes interactive features:

• View individual indicator layers.

• Customize weights for tailored vulnerability analysis.

• Adapt the CVI based on user goals (e.g., prioritizing climate vs. socioeconomic factors).

🎯 Outcome: Provide a practical tool for both policymakers and citizens to explore climate vulnerability and support decision-making.

MindCity:  Building CVI

Vulnerability map

1. Local microclimate model

MindCity:  Modelling

Vulnerability map

2. Thermal demand models

Simulation of cooling and heating demand based on a Resistor-Capacitor (RC) Model calibrated according to each building’s archetype. The model uses an electrical circuit to simplistically mimic the building’s thermal dynamics.

Vulnerability map

MindCity:  Modelling

MindCity:  Climate Vulnerability Map Visualization

Export and data visualization

Text

Vulnerability map

Vulnerability map

Index evolution by streets and buildings

Queries and forecasts

Vulnerability map

MindCity:  Climate Vulnerability Map Visualization

Queries and forecasts

Temperature forecast visualization by neighborhoods

Vulnerability map

MindCity:  Climate Vulnerability Map Visualization

Governance and licensing

Governance and licensing

Commitment to innovation

Public investment in innovation

• Catalan

• Spanish

• European

Economy of scale

Return on investment

• High initial implementation cost

• Low maintenance cost

• Medium- and long-term return

• No monthly license fees

• Collaborative software improvement

Self-sufficiency

• Independence from big tech

• Commitment to the local tech ecosystem

• Transparency

License EUPL-1.1

An open public administration

Governance and licensing

License EUPL-1.1

Open-source license promoted by the European Commission, specifically designed for software developed by public administrations in Europe

• Legal compatibility with the European framework

• Obligation to share improvements (copyleft)

• Promotes interoperability and reuse

A solid legal framework that promotes collaboration and continuous improvement of software in the public sector

License EUPL-1.1

Thank you

Jordi Cipriano
Director of  Innovation Unit BEEGroup

cipriano@cimne.upc.edu