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Proposal — Urban Lens

Summary

Hidden moisture inside building envelopes is a costly and underestimated problem. While stains, leaks, or visible surface damage are easy to identify, moisture trapped inside walls and roofs can accumulate silently for months or years before becoming visible. By the time property managers notice the issue, the damage is often already severe, leading to expensive repairs, operational disruption, liability, and avoidable material waste.

Our project addresses this gap by developing a drone-based inspection system that helps detect hidden moisture earlier and with greater confidence. Unlike conventional inspection workflows that mainly identify suspicious anomalies, our approach combines thermal imaging with mmWave and microwave radar to better prioritize where moisture is most likely present and where intervention should happen first.

In simple terms, others can tell you where to look. We aim to tell you where hidden moisture is most likely to be.

The Problem

Any property manager can see a stain on a ceiling.

What is much harder to detect, and much more dangerous, is the hidden moisture trapped inside the building envelope.

This moisture can build up silently over time due to common issues such as:

excessive humidity
condensation
water infiltration
roofing issues
maintenance failures
HVAC-related problems

Because the damage develops out of sight, it is often only discovered once it has already affected insulation, structural materials, membranes, or interior finishes. At that stage, repairs are far more expensive, disruptive, and difficult to scope precisely.

This is not a rare edge case. According to the EPA’s BASE study, in office buildings:

85% had evidence of past water damage
45% had current leaks

In many cases, hidden moisture is not just a maintenance issue. It becomes an asset risk, a financial risk, and sometimes even an insurance or legal risk. Once the problem is visible, repair costs can easily reach hundreds of thousands of dollars and, for larger buildings, can rise into the millions.

The core issue is simple: building owners and property managers often lack a reliable way to detect and prioritize hidden moisture early enough to act efficiently.

The Customer

Our customer is the person responsible for a building and accountable for its condition.

This can include:

property managers
building owners
condo managers
asset managers
facility managers
inspection and maintenance decision-makers

These people are under pressure from residents, owners, investors, boards, and insurers to make the right call at the right time.

They face a difficult tradeoff:

If they act too late, damage grows and repair costs become much larger.
If they act too early without strong evidence, they may waste money opening the wrong areas or commissioning unnecessary work.

Their real need is not just more inspection data. Their need is better decision confidence.

They need to know:

where the risk is highest
where to inspect first
where to intervene first
how to justify those decisions to stakeholders

That is the gap current solutions do not solve well enough.

Current Solutions and Their Limits

Today, building envelope inspection commonly relies on tools such as:

RGB imaging
thermal imaging
LiDAR
manual follow-up inspection

These tools can be very useful for identifying anomalies or suspicious areas. However, they generally do not provide strong confidence that moisture is truly present behind the surface.

In practice, the output often looks like this:

“Here are several suspicious spots. You should spend more time and money investigating which ones are real.”

That means the customer is still left with uncertainty. They still need to decide where to open, where to test, and how to allocate maintenance resources.

Thermal inspection became the dominant workflow because it was already a good-enough standard. It is familiar, accepted, and supported by recognized industry practices such as ASTM C1153. Because of that, the industry has had limited pressure to move toward more advanced workflows.

That creates a market gap: current solutions are good at spotting anomalies, but not strong enough at identifying hidden moisture with higher confidence.

Our Solution

We are developing a drone-based building inspection system designed to detect hidden moisture before it turns into visible damage or major repair work.

Our approach combines:

traditional thermal imaging
mmWave radar
microwave radar

This combination allows us to go beyond simple anomaly detection and move toward higher-confidence moisture identification and prioritization.

What makes our solution different

Most competitors help customers find suspicious areas.

We aim to help customers find hidden moisture zones with stronger confidence.

Instead of saying:

“There are 12 suspicious areas. Go check them.”

Our system is designed to say something closer to:

“Here are 3 high-priority hidden-moisture zones, 4 medium-priority zones, and a clearer basis for where to open first.”

That difference matters because it improves decision-making in the field. It helps customers:

reduce unnecessary invasive checks
prioritize repairs better
spend less on uncertainty
intervene earlier
manage building risk more systematically

Why this matters

The value is not only in detecting issues. The value is in turning unclear signals into clearer action.

Our solution aims to help customers save:

money
time
materials
operational disruption

It also creates the possibility of tracking building moisture risk over time rather than treating every inspection as a one-off event.

Why This Does Not Already Exist

A reasonable question is: if this is so valuable, why has no one already built it?

There are two main reasons.

1. Existing workflows were already “good enough”

Thermal inspection has long been an accepted and recognized workflow in the market. Industry standards such as ASTM C1153 gave buyers a method they already trusted, which reduced the pressure to develop better moisture-specific detection systems.

2. The enabling radar proof point is recent

Public research showing the potential of microwave-based moisture sensing in this context only became visible recently. For example, ORNL’s moisture-sensing results were published publicly only recently and are still positioned for eventual product licensing and development.

That means the technical opportunity is real, but the commercial product category is still open.

This is exactly the window we want to enter.

Business Model

We are not only selling a scan. We are building an ongoing risk-management workflow.

Revenue model

1. One-time scan fee

Customers pay per inspection or scan.

This gives them access to a building-specific moisture risk analysis and actionable output on where to investigate first.

2. Annual software subscription

Customers subscribe to a platform that allows them to:

store inspection history
compare scans over time
visualize moisture-risk evolution
manage building-level inspection records
support maintenance planning

This turns the product from a one-off service into an ongoing building intelligence workflow.

3. Future data opportunities

Over time, anonymized and aggregated data could create value for third parties such as:

material manufacturers
roofing system stakeholders
construction product companies
insurers
asset benchmarking partners

This would only be explored in a way that respects privacy, confidentiality, and customer trust.

Market Validation

To validate commercial interest, we directly contacted the CEO of Koverto, one of the recognized players in drone-based infrared inspection in Quebec and Ontario.

He was among the early adopters of infrared drone technology in this market and has deep practical experience in the field.

After presenting our solution, his feedback was very clear:

the commercialization potential is significant
the product addresses a real gap in the market
there is concrete interest in supporting the development of the solution

Most importantly, the interest was not abstract. A follow-up meeting is already scheduled.

That makes this more than a theoretical idea. It suggests there is real demand if the product can deliver.

Environmental Value

Our solution also has an environmental angle.

When hidden moisture is found too late, building owners often have to remove and replace far more material than necessary. Larger repair zones, more demolition, more waste, and shorter building component life all create avoidable environmental cost.

Earlier and more targeted intervention can help:

reduce unnecessary material replacement
reduce demolition waste
extend the life of building components
support more efficient maintenance decisions

In that sense, this is not only a building-risk solution. It is also a more resource-efficient maintenance approach.

Team

We are a team that started working on this through a hackathon, but quickly realized the idea had real potential beyond the competition.

What makes us credible is not that we already know everything. It is that we are committed to building, testing, learning fast, and pushing this toward a real product.

Our team brings together:

technical curiosity
strong motivation
willingness to validate directly with the market
the discipline to turn an early concept into a practical solution
Olivier Hamel — Product / technical vision / system concept
Hakeem — Problem framing / customer pain / market communication
Raph — Business model / validation / commercialization
William — Team coordination / pitch / execution

Closing Statement

Hidden moisture is expensive precisely because it stays invisible until it is too late.

Current inspection methods are good at finding anomalies, but not good enough at identifying hidden moisture with the level of confidence building decision-makers actually need.

We want to change that.

By combining thermal imaging with mmWave and microwave radar, we aim to give property managers a better way to detect, prioritize, and act on hidden moisture risk before it becomes visible damage, major repair, or avoidable waste.

This is not just about seeing more.

It is about helping customers make better decisions earlier.