Frequently asked questions

Infraspace is an AI-powered generative design platform for early-stage infrastructure planning. It helps project teams generate, evaluate, and optimize alternatives for roads, railways, power lines, pipelines, industrial sites, and access roads using geospatial data, cloud computation, and optimization algorithms.

Infraspace is built for infrastructure owners, consultants, planners, engineers, and project teams that need to evaluate several alternatives before decisions become locked.

It is especially relevant in early planning, feasibility studies, corridor studies, concept development, and optioneering.

Infraspace reduces the manual effort required to explore infrastructure alternatives. It helps teams save planning time, compare concepts consistently, reduce construction cost, understand environmental impact, and make better early-stage decisions using shared KPIs and 3D visualizations.

Infraspace can support roads, railways, networks of access roads, power transmission lines, pipelines, and industrial site location studies.

The platform is strongest when several alternatives must be considered and terrain, land take, earthworks, or environmental impact matter.

• Roads and highways
• Railways
• Power transmission lines
• Pipelines
• Wind farm access roads
• Industrial site location studies

Users define a project area, add relevant GIS and terrain data, configure infrastructure definitions, and either sketch, upload, or generate alignment proposals.

Infraspace then analyzes proposals with KPIs such as cost, CO2, earthworks, mass balance, land use, and environmental impact.

L.IN.E.A is the Infraspace AI engine for infrastructure planning. It applies generative design principles to explore thousands of potential proposals based on alignment rules, constraints, terrain, and project objectives.

This enables rapid evaluation of alternatives in early-stage planning.

Infraspace is primarily intended for early-stage planning, feasibility analysis, corridor studies, concept development, and optioneering.

It is not positioned as a replacement for detailed engineering design tools used later in the project lifecycle.

Infraspace is offered as a subscription for organizations, with packaging based on team size, project needs, and deployment requirements.

Contact us for a demo to discuss pricing, pilots, and rollout for your organization.

Yes. Infraspace offers an extension for Autodesk Forma so teams can run AI-assisted infrastructure planning for roads and other linear assets inside Forma workflows.

The integration supports interoperability with Civil 3D and other BIM tools, helping teams move from early optioneering in Forma to detailed design downstream.

Autodesk Civil 3D is primarily used to manually design and document detailed road and site models. Infraspace focuses on early-stage alternative generation, automated KPI analysis, and rapid comparison of many corridor options before detailed design begins.

Teams often use Infraspace for optioneering and decision support, then export alignments and context to Civil 3D or other tools for detailed engineering.

Traditional tools are mainly used to manually design and document a limited number of alternatives. Infraspace focuses on early-stage alternative generation, automated KPI analysis, and rapid comparison of many options before the project direction becomes fixed.

BIM tools are often used to model, coordinate, and document designs. Infraspace is focused on early-stage alternative generation and evaluation before detailed BIM models are created or finalized.

Use AI in early-phase infrastructure planning by defining the project area and constraints, importing GIS and terrain data, generating or uploading alignment alternatives, and comparing them with consistent KPIs for cost, CO2, earthworks, land use, and environmental impact before corridor decisions are locked.

Early-phase planning has high leverage because route, corridor, and site choices strongly influence cost, sustainability, permitting risk, and constructability. Once options become fixed, major improvements become harder and more expensive.

A practical workflow is to run feasibility or corridor studies with AI-assisted optioneering, review assumptions and feasibility with engineers, and share maps, dashboards, and 3D visualizations with stakeholders so trade-offs are clear before detailed design begins.

Yes. Teams can upload existing alignments from conventional design tools and analyze them using the same KPI framework as AI-generated proposals.

This makes it possible to compare traditional designs and AI-generated alternatives on a shared data foundation.

Infraspace is a web-based platform where project data, proposals, 3D models, and analysis results are available to authorized users.

This supports collaboration between disciplines and stakeholders without requiring everyone to use desktop engineering software.

Generative design is a method where algorithms automatically generate infrastructure alternatives based on defined objectives and constraints.

In infrastructure planning, this can include geometric rules, terrain, environmental restrictions, cost targets, land-use requirements, and CO2 objectives.

AI-assisted optioneering is the process of using AI and geospatial analysis to generate, evaluate, and compare many infrastructure alternatives.

It allows planners to move beyond a few manually created concepts and explore a wider solution space earlier in the project.

Corridor optimization is the process of finding the most suitable route or corridor for infrastructure such as roads, railways, pipelines, or power lines.

It considers terrain, geometry, environmental constraints, land use, cost, CO2, and constructability.

AI optimizes road alignments by combining terrain models, GIS data, geometric constraints, cost assumptions, and environmental criteria.

The system generates and compares alternatives to identify alignments that can reduce cost, improve mass balance, reduce land take, or lower environmental impact.

Geospatial AI combines spatial data, terrain analysis, GIS layers, and optimization algorithms to support infrastructure decisions.

It helps evaluate how proposed roads, railways, power lines, or sites interact with terrain, land use, environmental constraints, and existing assets.

AI can reduce planning time by automating repetitive analysis, proposal generation, KPI calculations, and comparison workflows.

Instead of manually producing and assessing each alternative, teams can evaluate many proposals in minutes or hours and focus expert time on decision-making.

AI improves decision support by giving teams consistent KPIs for many alternatives.

Cost, CO2, mass balance, land use, terrain impact, and environmental indicators can be evaluated on the same foundation, which makes trade-offs easier to explain and defend.

Infraspace presents alternatives with 3D visualizations, maps, dashboards, and KPIs that can be accessed through a web application.

This makes technical and environmental trade-offs easier to understand for stakeholders who are not specialists in engineering software.

Consultants can use Infraspace to generate and evaluate more alternatives in less time.

This allows teams to broaden the solution space, identify previously overlooked concepts, and provide clients with more transparent decision support.

AI can reduce environmental impact by allowing teams to evaluate many alternatives before corridors are fixed.

This makes it possible to compare land take, biodiversity impact, GHG estimates, terrain disturbance, viewshed, and other indicators alongside cost and technical feasibility.

Automated environmental impact assessments use algorithms, geospatial data, and digital models to assess environmental factors across multiple alternatives.

They reduce manual work and help teams include sustainability in early decision-making rather than after the main concept has already been selected.

Infraspace can calculate CO2 and greenhouse gas-related indicators based on quantities, structures, earthworks, land use, and project-specific assumptions.

These estimates can be displayed with proposals, tables, dashboards, and 3D visualizations to improve understanding across the project team.

Mass balance optimization aims to reduce the surplus or deficit of excavated materials by improving the relationship between cut and fill.

Better mass balance can reduce transport, disposal, imported materials, cost, and CO2 emissions.

Infraspace can calculate KPIs such as construction cost, CO2, earthworks volumes, mass balance, area usage, land take, viewshed, environmental metrics, affected buildings, biodiversity considerations, and required structures such as bridges and tunnels.

Yes. Infraspace is a browser-based cloud application hosted on Google Cloud Platform.

Cloud computation allows heavier analyses to run outside the user device while project data and results remain accessible to authorized users.

Infraspace stores all customer data in the EU, specifically in Belgium. Data is encrypted in transit and at rest, and tenant separation is handled through access rights.

No. Customer data is not used to train machine learning models available for other customers.

Each customer’s project data remains isolated for their authorized users only.

Infraspace supports common infrastructure and GIS formats including DXF, IFC, GLTF, OBJ, SHP, GML, SOSI, LandXML, and TIT/NYL.

Export options include 3D models, alignments, GIS data, terrain models, and KPI data.

Yes. GIS data is central to Infraspace workflows.

Users can upload or sketch areas, lines, and points, and the platform uses these layers to support generation, evaluation, and environmental analysis.

API access is available upon request for teams that want to integrate Infraspace with existing workflows.

Contact us to discuss authentication, use cases, and integration requirements for your organization.

Infraspace has demonstrated measurable outcomes such as reduced planning time, reduced estimated construction cost, and faster KPI generation.

In one case, estimated access-road construction cost was reduced from 7 MNOK to 3.5 MNOK using Infraspace.

Yes. Infraspace has been recognized as winner of Best Product in the CASSINI Challenge by EUSPA, a Top 50 Contech Startup by CEMEX Ventures, and a finalist in innovation programs from DB Mindbox and Elia Group.

AI-generated proposals are decision-support outputs, not final engineering approvals.

Engineers must review feasibility, assumptions, design standards, local context, risk, constructability, and regulatory requirements before using any alternative as a basis for further design.

The infrastructure intelligence layer is the AI-enabled decision layer that connects data, design alternatives, analysis, and recommendations.

Instead of only storing models and drawings, it helps teams predict outcomes, optimize decisions, and understand risks earlier.