CLEARANCE
Designing an End-to-End Platform for Vegetation Clearance Management
Background
Untangling The Threads
When I joined the Clearance project, our internal analytics team was grappling with a highly disjointed and complex process for managing vegetation compliance. Critical data, including LiDAR scans and GIS files was dispersed across emails, spreadsheets, and disparate folder links. While the stakes were incredibly high, directly impacting bushfire risk mitigation and network reliability, this workflow led to significant operational inefficiencies, data inconsistencies, and hindered our ability to scale.
As the Lead UX Designer, my task was to bring strategic structure and a user-focussed approach to this complex and highly technical domain. Over the past 6 months, I've led the end-to-end design of the Clearance platform. This involved collaborating with GIS analysts, software engineers, and project managers to create a unified system where users can seamlessly upload spatial data, meticulously track project progress, and generate high-quality vegetation clearance reports and all within an integrated environment.
The Challenge
When Data Was Everywhere, But Insight Was Nowhere: Our Starting Point
Vegetation clearance is the critical process of trimming trees and foliage to maintain a safe, regulated distance from high-voltage power lines. This work ensures compliance with strict safety standards to prevent fires and protect public safety.
The core problem we faced on the Clearance project was simple: our internal analytics team was swamped. Even though this work is super critical and complex, the whole process of managing vegetation clearance was disorganised. We had critical data like LiDAR scans and GIS files scattered across emails, spreadsheets, and random folders. The analysis itself, which needed a smart mix of machine learning and human review, was being done across outdated systems and manual steps.
There was a lot of data, but getting real, useful insights from it was nearly impossible. All that information, without one clear place to put it, made every day a struggle.
To really dig into why things were so complicated, I started by just talking to people. These screenshots show some of those early chats on Teams. I ran workshops and had lots of one-on-one conversations with our GIS analysts, the offshore review teams, and the project managers. We talked about how they currently worked, what processes did not work, and what they really needed. These sessions were key to understanding the day-to-day way of working and getting everyone on the same page about what we needed to fix.
The engineers walked me through their systems. Users had to navigate between different interfaces, log in repeatedly, and information was stuck in its own little silos. These pictures show a peek at that old way of doing things: data often had to be maintained between multiple systems and important details were always hiding somewhere else. This process really slowed things down, impacted information visibility, and led to subtle frustrations.
The conversations helped pinpoint the biggest problems. I laid out the main challenges we faced, to help us turn those issues into clear opportunities, for designing something much better.
Our Users
One platform, many hats
Solving the big problems we just talked about wasn't simple, because we weren't just designing for one person. We needed to build a single platform that could serve a few very different kinds of users, all wearing distinct "hats" and needing different things.
Our Offshore GIS Analysts - This team is tasked with meticulously reviewing polygons and checking vegetation clearance details after the machine learning has done its first pass. They're the hands-on reviewers, ensuring accuracy.
Our Internal GIS Analysts - They're the quality controllers, refining any clearance detections from the ML and doing the final audit on all the offshore work before anything goes out. They need precision and oversight.
Our Project Managers - This is the team keeping everything on track – from timelines and budgets to making sure all the outputs are ready. They needed a bird's-eye view, not more spreadsheets.
And looking ahead, our Utility Customers - The big vision is for them to eventually jump on the platform and handle their clearance projects, directly. Right now, the tool is too technical for them.
On one side, we had our current users, the expert internal teams who needed deep control and complete transparency over highly technical processes. On the other, our long-term goal was to eventually hand this platform over to our utility customers, and they wouldn't have GIS tools on hand or any LiDAR experience. That meant anything we built for "now" also had to lay the groundwork for a much simpler "future."
Some of our challenges included:
"How might we simplify the upload of various datasets and files for the user?"
"How might we present complex clearance data in a way that’s clear, meaningful, and easy to interpret for all our users and eventually, our customers?"
"How might we design review tools that are simple, fast and spatially accurate for technical users?"
"How might we build dashboards that work for internal teams now but can scale and adapt for future customer needs?"
To help the team figure out what to tackle first, especially with so many moving parts, I created this circular Risk–Complexity Map. It's a visual way to show which issues were urgent but relatively easy to fix (our 'Quick Wins' in the middle), and which were bigger, more complex projects we'd need to plan for later (the 'Important/Complex' ones further out). The circle is split into four zones based on key areas like data uploads, map review tools, project management, and future customer features. This visual made it incredibly easy for everyone to see where our 'Quick Wins' were hiding and helped us all agree on what to prioritize now versus what to save for future phases.
End-To-End Flow
From Complexity to Clarity
With the core issues identified and our high-level priorities clear, my next step was to really dig into how work actually can be done with the new system. Before jumping to solution-mode, I wanted to understand the full journey, from when data first came in all the way to when a final report was delivered. This meant looking closely at how different roles, tools, and handoffs played out.
The user sessions continued, and I spent time shadowing our internal teams, observing their complex workflows firsthand, and trying to make sense of all the invisible connections that held everything together
It was clear that the process relied on individual smarts and tons of manual coordination. As one analyst put it, "if something breaks in the middle, we then have to chat via Teams." The teams were dependent on informal communication.
Working on the end-to-end flow, the full picture started to unravel. For instance, the handoffs between the automated machine learning outputs and the manual GIS reviews presented opportunities for improvement. Another interesting point someone from the GIS team shared, "We don't know the ML's finished until someone emails me."
Visualising this entire journey was very useful. It helped us move past some assumptions and gave the whole team a shared, clear understanding of the process. With an end-to-end view, the pain-points and challenges became easy to identify and now it was easier to figure out how to fix them.
Design
Bringing Ideas to Life: Our Guiding Principles & Early Concepts
With a clear map of the problems and handoffs, we finally had the foundation we needed to start sketching out initial ideas for the new platform. This meant it was time to translate those identified opportunities into concrete, low-fidelity design concepts.
Hosting a design jam
I decided to host a design jam session with a few members of the team, namely the analysis team and the PMs.
My goal for this phase was simple: explore as many solutions as possible, as quickly as possible. We weren't trying to make things look pretty yet; we just needed to figure out how users would move through the system, how data would flow, and how our ideas would actually solve those core pain points we’d identified.
To make sure all our early ideas stayed on track and truly solved the problems we'd found, we established a few guiding principles. These weren't just buzzwords; they were our constant reminders as we started sketching together.
Following these guiding principles, and with the insights sparked during our design jam, I took all that raw input and started sketching out our first ideas for the new platform. Below are screenshots of some of the concepts and they helped us quickly test different approaches and get early reactions.
With these early concepts in hand, I ran some feedback sessions with our internal teams. The goal to get quick, gut-level reactions from our Project Managers and GIS Analysts. I wanted to see if our initial ideas were resonating and where we might have missed something. The feedback gave us a clear direction for our next steps.
Clearance Platform: Iteration & Refinement
Once we had a clearer picture of what we wanted to build, it was time to refine those early concepts. After several weeks of iterations and design reviews, we brought our refined concepts to life in high-fidelity.
Viewer: Precise and Visual Information for Analysts
The viewer is designed to be the central command centre for our analysts and PMs. All spatial data, from LiDAR outputs to ML classifications, is unified here. Intuitive layer toggles and visual grouping mean analysts can quickly customise their view, focus on critical areas, and perform precise clearance reviews faster than ever before.
Focusing on the pain-point "Difficulty navigating spatial data." Allowing map controls and hover-interactions so analysts can quickly see exactly what they need, without getting lost in the visual clutter
Data Upload
I designed this guided workflow to help users easily map their own GIS data fields to our system’s fields. By handling this step upfront, we make sure the data stays accurate and avoid headaches later on - making the process simple, reliable, and in their control.
Adapting to Delayed Feedback
During the initial discovery and lo-fi design phase, direct access to our Project Managers was limited due to their demanding field schedules and project deadlines. To maintain momentum and provide a tangible concept for future discussions, we proceeded by working closely with business stakeholders and internal subject matter experts who served as proxies for the user.
Our initial hypothesis, based on these conversations, was that PMs required a simple, high-level view of project completion. The key goal was 'at-a-glance' status tracking.
Initial Dashboard Design - (Archived)
Once the high-fidelity prototype was ready, we successfully secured a feedback session with two Project Managers.
While they appreciated the clarity of the V1 design, their feedback was clear. I heard a consistent theme: 'Knowing it's 25% complete tells me what happened, but it doesn't tell me if we're healthy. Am I on track with the schedule? Are we behind? How does this project's speed compare to the last one?'
The Pivot
My initial high-fidelity design, created based on stakeholder interviews, was a comprehensive Status Report. It focused on providing a clear, real-time snapshot of the project's state, answering the question 'What is the current status?'. With its clean hierarchy and scannable metrics, it gave a precise summary of project completion and workflow progress.
However, when we finally presented this to Project Managers, it became the catalyst for a crucial insight. They confirmed the data was accurate, but they immediately started asking the questions the dashboard couldn't answer: 'Are we on schedule? Is 45% good or bad? Where is my team getting stuck?'
It was the classic 'data vs. insight' problem. I had provided them with data. They needed a tool that gave them insight
Unified Project Dashboard
This is the new single source of truth for all projects, designed to tackle the pain points of having no visibility and no historical tracking. From here, Project Managers can instantly see the status of every current project at a glance. The "Projects Lists" section makes it easy to access and view historical data, providing the long-term insights the team never had before.
I pivoted the design to become an actionable "Diagnostic Tool." The final version provides crucial context by integrating "Planned vs. Actual" comparisons, performance timelines, and historical data, transforming the dashboard from a passive report into a strategic platform that empowers managers to understand their project's trajectory and make informed decisions.
Measuring Success
While Clearance is an ongoing journey, we're already seeing significant wins and learnings.
Increased Efficiency: Early feedback from our Internal GIS Analysts indicates a smoother data ingestion process, cutting down hours previously spent on manual data prep and reconciliation.
Enhanced Visibility: Project Managers now have unprecedented real-time insight into project progress, leading to fewer delays and more confident updates to customers.
Improved Collaboration: The unified platform has naturally fostered better communication between internal and offshore teams, reducing those 'silo' issues we identified early on.
Foundation for Growth: The architecture we've built is robust, ready to scale as we onboard more utility customers and expand our service offerings.
This project has been a powerful reminder of how important it is to balance immediate user needs with a bigger, long-term vision. Navigating the complexities of highly technical users alongside a future self-serve customer base taught me invaluable lessons about phased development and scalable design. It also reinforced the power of involving technical teams early – their input was key to making our solutions practical.
The Road Ahead
Our journey with Clearance is far from over. The foundational work we've done for our internal teams is a critical stepping stone towards our ultimate goal - a self-serve platform where utility customers can manage their own vegetation clearance programs directly. Future phases will focus on streamlining the customer-facing experience even further, exploring advanced reporting, and enhancing historical data comparison to empower our customers like never before.
