Author: eagle_managewp

Artificial intelligence is moving rapidly from experimentation into enterprise strategy. Across sectors, organisations are under pressure to understand where AI can improve productivity, automate workflows, and support faster decision-making.

But as AI adoption increases, many organisations are also facing a more difficult question, how do we make AI useful, trusted, and grounded in the real-world context our decisions depend on?

For Eagle Technology, this is where trusted geospatial AI has a significant role to play.

People, assets, services, risks, infrastructure, resources, and events all exist somewhere. That ‘where’ shapes decisions every day, from where services are needed, to where infrastructure is exposed, to where communities are affected, to where investment should be prioritised.

Yet many enterprise AI systems are not inherently spatial. They are not designed to understand geography, spatial relationships, proximity, networks, boundaries, terrain, exposure, or the context that location provides.

That creates a significant gap, AI may be able to process information quickly, but without trusted spatial context, it can miss where risk is concentrated, how assets and communities are connected, what is exposed, and where action will have the greatest impact.

AI can generate answers, summarise information, and automate tasks. However, without trusted location intelligence, it can miss the spatial context behind real-world decisions.

This is where Eagle has a clear role to play. Eagle enables organisations to bring trusted geospatial intelligence into AI-enabled workflows, so decisions are not only faster, but better informed, more contextual, and grounded in the realities of place, assets, risk, infrastructure, and communities.

The market needs clarity, not more AI noise

The AI market is moving rapidly. New tools, agents, assistants, models, and platforms are emerging at pace. For many organisations, this creates both opportunity and uncertainty.

Eagle sees this every day through our work with customers across New Zealand. Organisations are not simply asking what AI is; they are asking what it can do for the problems they are trying to solve. They want to know how AI can reduce manual effort, make trusted data easier to access, support better operational decisions, and extend the value of the systems and platforms they already rely on.

There is pressure to act, but also pressure to act responsibly. Customers need clarity on what AI can do today, what is still emerging, what is safe to pursue, and where investment will create measurable value. They also need a practical path for connecting AI to existing governance, data, security, and technology environments.

This is particularly important in the geospatial space, where many organisations already hold significant value in ArcGIS environments, spatial datasets, imagery, LiDAR, asset information, field data, operational layers, and location-based workflows.

The opportunity Eagle has identified is not simply for organisations to adopt new AI tools.

It is to make trusted capability more accessible, more useful, and more connected to the wider enterprise AI strategies organisations are now developing.

This is where Eagle’s experience matters. Through our work across ArcGIS, spatial data, enterprise systems, and customer delivery, we understand both the value locked inside environments and the practical challenges organisations face when trying to apply AI safely and effectively.

The opportunity is to bring those worlds together, connecting trusted spatial data, workflows, and location intelligence into AI-enabled environments in a way that is practical, governed, secure, supportable, and focused on real customer outcomes.

There are two connected but distinct paradigms when thinking about Geospatial AI.

The first is AI in GIS: the development of AI tools, assistants, automation, analytics, and capability within ArcGIS to support GIS users and enhance existing workflows.

The second is bringing GIS to AI. This is where Eagle recognises the opportunity to support customers in bringing trusted spatial data, ArcGIS services, workflows, and location intelligence into wider enterprise AI environments.

Both stories matter, and they complement one another. Together, they show how AI can enhance the GIS environments organisations already rely on, while also enabling trusted spatial data, services, and workflows to support wider enterprise AI strategies.

Esri is continuing to develop native AI capability within ArcGIS. Eagle builds on that platform direction by translating emerging capability into practical, customer-ready solutions for New Zealand organisations.

At the same time, we enable customers to bring trusted GIS capability into wider AI workflows, agents, enterprise systems, and business processes, so spatial data and location intelligence are not left outside the enterprise AI conversation.

This distinction matters because customers are moving beyond the question of what AI can do inside a GIS platform. They are asking how trusted spatial data, ArcGIS services, and workflows can connect into the systems, processes, and operational decisions that already shape their organisation.

For Eagle, this is critical, bringing location intelligence into enterprise AI so spatial data is not treated as a separate technical layer, but as a key part of how organisations make better decisions.

From AI potential to trusted geospatial value

Eagle’s position is clear, AI-enabled capability should be practical, trusted, business-focused, and grounded in measurable value.

It should not be treated as a chatbot, a demonstration, or a technology experiment in search of a use case.

The value sits in the full solution, trusted spatial data, strong foundations, clear governance, secure integration, practical workflows, reliable architecture, and ongoing support.

Graeme Henderson, CEO of Eagle Technology, says, “The real value of AI in the geospatial space is not in applying new technology without a clear purpose. It is in helping organisations make better use of the trusted spatial systems, data and workflows they already rely on. Eagle’s role is to help customers apply AI and ArcGIS safely and practically, so they can improve productivity, surface new insights, and make more confident decisions in the real world.”

That is the practical opportunity.

AI can reduce manual effort, help people access information faster, identify patterns, support scenario testing, and make specialist capability easier to use across the business. But those benefits only become meaningful when AI is connected to authoritative information, aligned to governance, and designed around real operational needs.

Graeme Henderson says, “Enterprise AI will only become more valuable when it is grounded in the real-world context organisations operate in. Location is a critical part of that context. Most AI systems don’t automatically understand where something is happening, how assets, communities, networks, and risks relate to each other, or why location changes the decision. Eagle’s role is to bring trusted GIS capability into AI workflows, so customers can move beyond generic outputs and make decisions that are more relevant, more contextual, and more operationally useful.”

Where AI value starts

For many organisations, AI value does not begin with an agent, assistant, or automated workflow. It begins with readiness: trusted spatial data, clear metadata, appropriate permissions, strong governance, and the confidence that AI-enabled workflows are drawing from information that is accurate, authorised, and understood.

This is why AI-ready spatial data is a critical foundation. It reduces the risk of inaccurate outputs, disconnected analysis, or AI systems drawing on information that is incomplete, duplicated, outdated, or poorly understood.

With those foundations in place, AI-enabled workflows can create value in practical ways. They can make authoritative spatial information easier to access through natural-language interfaces, support faster analysis of imagery, LiDAR, drone, sensor, video, mobile, and field data, and enable predictive analysis by combining spatial, historical, environmental, operational, and business information.

This can help organisations detect asset changes, assess damage, monitor remote environments, understand future risk, test planning options, prioritise investment, and communicate decisions more clearly.

With the right foundations and governance in place, AI-enabled workflows can extend the value of GIS across the organisation, giving non-specialists guided access to trusted spatial insight while enabling GIS specialists to focus on higher-value analysis, advisory, and decision support.

Building capability that lasts

AI capability cannot be treated as ‘deploy and forget.’

Platforms will evolve, business needs will change, security expectations will mature, and governance requirements will become more defined. For AI-enabled capability to deliver lasting value, it needs to be designed from the outset to adapt, scale, and remain supportable beyond the first prototype or implementation.

That is why maintainability is central to Eagle’s approach. Customers need AI capability that can grow with their business, adapt as ArcGIS and AI capabilities advance, and avoid being locked into a single version, workflow, or short-term implementation approach.

Eagle’s role extends across the full AI journey: strategy and advisory, enablement, delivery, and long-term scale, operation, and maintenance.

We work with customers to identify where AI can create measurable value, build the internal confidence needed for adoption, deliver practical and governed implementations, and ensure capability remains secure, resilient, supportable, and aligned to evolving business and platform requirements.

The organisations that gain the greatest value from AI will not be the ones chasing every new feature. They will be the ones building trusted foundations now, so they can move faster, more safely, and with confidence as capability matures.

For Eagle, this is the opportunity to protect and extend the value of customers’ ArcGIS and spatial data investments, bring trusted location intelligence into enterprise AI, and support better real-world decisions through AI that is practical, secure, explainable, and commercially sound.

The future value of AI will depend on how well organisations can connect intelligence to context, systems to workflows, and technology to the decisions that matter. That is where trusted geospatial expertise becomes essential.

[vc_row columns=”1″][vc_column][vc_column_text]By Nathan Heazlewood

Introducing Eagle Technology’s New eBook

Artificial Intelligence (AI) is rapidly reshaping industries across the globe, and the geospatial sector is no exception. Yet for many Geographic Information System (GIS) professionals, AI can feel complex, opaque, and difficult to engage with meaningfully. Recognising this challenge, Eagle Technology has developed a comprehensive eBook designed to bridge the gap between AI theory and practical geospatial application.

Why AI Feels Complex

AI is not a single technology but a multidisciplinary field that draws on computer science, mathematics, and involves elements of cognitive science and ethics. At its core, AI involves creating systems that can learn from data and make decisions or predictions. However, achieving this involves a broad range of techniques and concepts—each with its own language and underlying theory.

For example, AI includes different advanced methods like:

• Neural networks: computational systems inspired by the human brain that recognise patterns in data.
• Deep learning: a subset of AI that uses multi-layered neural networks to process complex data such as images or time-series information.
• Transformers and attention mechanisms: modern techniques that allow AI models to focus on the most relevant parts of data, improving performance in tasks like language processing and increasingly, spatial analysis.
• Generative models: systems capable of creating new data, such as predicting future scenarios.

Each of these components introduces its own terminology and workflows, making AI feel overwhelming—particularly for professionals whose expertise lies in spatial analysis rather than data science.

The GIS-First Approach: Strengths and Limitations

Many GIS professionals naturally approach AI by starting with familiar tools and workflows. This “GIS-first” approach often involves applying AI through software platforms such as ArcGIS to solve practical problems, including:

• Automated feature extraction from aerial or satellite imagery
• Land cover classification
• Predictive modelling for environmental or urban planning scenarios
• Image segmentation (dividing imagery into meaningful regions)
• Spatiotemporal analysis (examining how geographic patterns change over time)

This approach has clear advantages. It delivers immediate, tangible results and allows practitioners to enhance existing workflows without needing to fully understand the underlying AI mechanisms.

However, it can also create a dependency on specific tools and limit deeper understanding. Without a solid grasp of the principles behind AI, it becomes difficult to:

• Evaluate new tools and claims critically
• Adapt to rapidly evolving AI technologies
• Design tailored solutions for unique geospatial challenges

A Concept-First Approach to AI Learning

Eagle Technology’s eBook takes an alternative approach—starting not with tools, but with foundational AI concepts. This method is designed to build a strong conceptual framework that GIS professionals can apply across technologies and use cases.

The eBook introduces key ideas such as:

• What defines a neural network, and how it processes data
• The distinction between machine learning and deep learning, and when each is appropriate
• Attention mechanisms, which help models prioritise relevant information
• Bias in AI, which can lead to unfair or inaccurate results if not managed
• Explainability, the ability to understand and justify AI-driven decisions

By clearly explaining these terms in accessible language and within a sequence of structured categories, the eBook removes much of the intimidation often associated with AI.

Connecting AI Concepts to Geospatial Applications

Once these fundamentals are established, the eBook bridges the gap to GIS by demonstrating how AI concepts directly apply to geospatial problems.

For instance:

• Convolutional Neural Networks (CNNs), a type of deep learning model, are particularly effective for analysing raster data such as satellite imagery because they can detect spatial patterns like edges, shapes, and textures.
• Reinforcement learning can be applied to optimise transport networks, dynamically improving route selection based on changing conditions.
• Predictive models can forecast environmental changes, urban growth, or infrastructure demands using historical spatial data.

This dual perspective—concept first, application second—equips readers with both understanding and practical insight. It empowers GIS professionals not just to use AI tools, but to understand why they work and how to apply them effectively.

A Resource for Professional Growth

The eBook is designed as a self-development resource tailored to the needs of geospatial professionals. It supports:

• Building confidence in AI terminology and concepts
• Enhancing technical literacy across emerging technologies
• Enabling more informed decision-making in projects and investments
• Supporting innovation in geospatial solutions

At the same time, it serves a secondary audience: AI practitioners who want to understand how their techniques can be applied in the geospatial domain. By providing context and examples grounded in GIS, the eBook fosters better collaboration between data scientists and spatial professionals.

Enabling the Future of Geospatial Intelligence

As AI continues to evolve, its integration with GIS will become increasingly central to solving complex spatial problems—from climate change modelling to smart city development. Understanding AI is no longer optional; it is a strategic capability.

Eagle Technology’s eBook offers a practical and accessible pathway into this evolving field. By focusing on foundational concepts and connecting them to real-world geospatial applications, it provides the knowledge needed to move beyond tool-based usage and towards true AI-enabled innovation.

For GIS professionals looking to stay relevant, competitive, and forward-thinking, this eBook is an essential step in that journey.

View and download Eagle Technology’s eBook, Artificial Intelligence Glossary: AI Key Concepts, Terminology and Geospatial Considerations.[/vc_column_text][/vc_column][/vc_row]

For years, urban designers and procedural modelers have faced challenges with the ArcGIS CityEngine Complete Streets rule—its complexity required meticulous parameter tuning, slowing down workflows. CityEngine 2025.0 Beta introduces Street Designer, a modular lane-based system that simplifies street creation, improves customisation, and enhances export flexibility for visualisation in game engines and 3D visualisation software.

Background

The design process for streets in CityEngine got a boost in 2015 with the publication of the Complete Streets rule. Developed by David Wasserman during his internship at Esri, this rule allows creation of complex street designs with the option for adding details like vehicles, pedestrians, streetlights and other street furniture, reporting options including painting, braking sight, object counts and more. While the Complete Streets rule offered extensive customisation, its sheer number of interdependent parameters made design workflows increasingly complex—requiring significant adjustments to achieve desired results. David wrote the unofficial user guide for this rule spanning 46 pages.

After departing Esri, David continued updating his version of the rule on the Esri Community site until 2020. Esri’s official version of the Complete Streets rule is included in CityEngine example ‘Example Complete Streets’ and it is continually developed and maintained.

Present Day

A decade later, Esri revisited the design and shifted towards a simpler approach. The result is Street Designer. The Street Designer is a modular approach for simpler street design. In Street Designer a street is configured as a roadbed (centreline) and a number of lanes – road lane, bus lane, pedestrian lane, trees, lamps, furniture lane. For each lane, the user can apply individual rule and therefore the design is simplified. Instead of one large rule there is a number of smaller ones, easier to understand and modify.

As part of the transition to Street Designer ESRI.lib has new rules for different types of lanes as shown in the screenshot below.

Street Designer implements a lane-based modular system. This structured approach enables transport planners to refine lane assignments and individual rules improving procedural modelling flexibility without relying on a single monolithic rule system.

Here is an example of the street designed in Street Designer.

How to create a street using Street Designer?

The design process consists of four steps:

• Create a street using the Street Creation Tool
• Add or remove lanes from Edit Lanes window
• Assign rules individually, selecting rules from ESRI.lib for each lane enabling precise customisations
• Modify attributes for lanes based on design needs

The starting point is to use the standard Street Creation Tool and draw a street. Once the street is created, add lanes with the new Street Designer tool. This tool is easy to use, with visual cues where lane can be added. For finer control use the Edit Lanes window shown in the screenshot below.

The next step is rule assignment. The new rules created for Street Designer are included in the ESRI.lib folder in current workspace. You need to apply rules to each lane individually.

The final step is modifying the attributes for each lane. Each rule has different attributes providing fine grain design details.

If you are working on a large area with many streets there is a lot of manual work and detailing with attributes. However, Street Designer has a feature called the Street Configuration that allows for the creation of a library of standardised street designs. These standardised street deigns can be applied to any street layout across multiple projects via the Street Configuration Manager providing visual feedback when necessary.

Exporting

With Street Designer, each lane can be exported as a separate set of objects. This modular approach simplifies asset replacement workflows, enabling users to substitute default objects with meshes optimised for game engines.

Using Complete Streets rules meant you didn’t have access to street furniture, for example, as separate objects once street models are exported from CityEngine. This meant that users would need to create all details including benches, parking metres and streetlights or create placeholders and then replace them in the game engine with blueprints or higher quality assets.

With Street Designer it is now possible to handle every lane separately and when exported they are exported as separate objects. This means that it is now a simple task to modify separate objects in applications such as in Unreal Engine with a blueprint that includes high quality mesh and light source with optional use of IES profiles. If you like the assets from CityEngine you can even use static meshes in your own blueprints for simpler workflow.

This workflow improvement reduces time working in Unreal Engine significantly. Below is a street created with Street Designer, exported as a Datasmith file and then imported into Unreal Engine where the original streetlights objects were replaced with a simple blueprint containing a light source.

Street Design in ArcGIS CityEngine

Street Rendering in Unreal Engine

For visualisation of larger areas or subdivision with numbers of streets and visualisation at night I would use Unreal Engine’s Megalights but that is a subject for another blog post.

With modular street configurations, urban designers, city planners and game developers can refine workflows for maximum efficiency.

How will you apply Street Designer in your next project?