Extended deadline: Call for Service Provision for the Project GRID (EUI)

Call for Service Provision for the Project “Green Routes Intelligent Districts – GRID” (EUI)

Call for service providers in Smart Tree Inventory and Digital Twin of Urban Trees

The workers’ cooperative commonspace is seeking the procurement of one (1) service in the field of Smart Tree Inventory (STI) and Digital Twin modelling, for the project “Green Routes Intelligent Districts” (GRID, project code: EUI03-236), funded by the European Urban Initiative programme.

The GRID project integrates advanced digital technologies with smart green infrastructure to mitigate urban heat risk. Urban areas in Southern Europe, such as Kallithea and Moschato-Tavros, face significant heat-related challenges, exacerbated by dense urban form, impermeable surfaces, and limited green space. Extreme heat reduces urban livability, increases energy consumption, and disproportionately affects vulnerable populations.

GRID will develop an innovative, integrated, data-driven suite of solutions to support urban heat risk mitigation. This will be achieved through the integration of participatory processes, IoT-based sensor networks, heat risk assessment and adaptation modeling, as well as augmented/virtual reality (AR/VR) tools.

Expression of Interest

Interested entities are invited to submit their expression of interest by 17/4/2026.

Expressions of interest can be submitted:

• In person at: 91 Asklipiou Street, 11472, Athens, Greece

• By email at: info@commonspace.gr

• Electronically via the contact form on the website (www.commonspace.gr), indicating the relevant call

Technical Description of the Service

The requested service concerns the development and delivery of a Smart Tree Inventory (STI) and Digital Twin platform for urban trees, supporting climate adaptation strategies and urban green infrastructure management within the GRID project.

Scope of Services

The selected service provider will be responsible for:

• Capturing and mapping urban tree assets within defined Areas of Interest (AOI)

• Developing high-resolution digital twins of trees using LiDAR and AI technologies

• Collecting, processing, and structuring geospatial and morphological tree data

• Delivering and maintaining a cloud-based platform for visualization, analysis, and management

• Supporting integration with project digital tools (WP6) and pilot implementations (WP7)

Technological Approach

The proposed solution should include:

• Mobile Laser Scanning (MLS) using high-precision LiDAR systems

• Vehicle-based scanning across the urban road network

• AI-driven detection, classification, and measurement of trees

• Generation of accurate 3D digital twins for each recorded tree

• Cloud infrastructure for data processing, storage, and user access

Deliverables

The service provider shall deliver:

• A comprehensive digital tree inventory database

• 3D representations (digital twins) of trees

• Tree metrics including (at minimum):

  • Species/genus classification

  • Diameter at Breast Height (DBH)

  • Tree height

  • Structural characteristics

  • Health and vitality indicators

• Species

• Tree benefits

  • Water managment

  • Ecosystem services

  • Valuation

• A web-based platform with:

  • Visualization tools

  • Data querying and filtering capabilities

  • User access management

• API access and/or export capabilities (e.g. GIS-compatible formats)

Deliverables shall be provided progressively via the platform.

Performance Requirements

The service must meet high-quality data standards, including:

• ≥90% accuracy in genus/species identification

• ±10% accuracy in DBH measurements

• ≤0.1% false positive rate and ≤1% false negative rate in tree detection

Duration

• Indicative duration: up to 12 months

• Optional extension for monitoring and updates (Smart Tree Monitoring phase)

Award Criteria

The selection of the service provider will be based on the following criteria:

• Relevance of experience: Proven experience in Smart Tree Inventory, digital twins, or similar projects

• Technical capacity and expertise: Demonstrated ability to deliver LiDAR-based and AI-driven solutions

• Quality of the proposed approach: Clarity, feasibility, and methodological robustness

• Experience in EU-funded or co-funded projects: Familiarity with European programme requirements

• Team composition: Qualifications and experience of the proposed team

• Cost-effectiveness: Financial offer in relation to scope and quality

commonspace – GRID project team