Distributed Acoustic Sensing: Your City’s Quiet AI Sensor

A lot of smart city programs still try to “see” everything with cameras. It’s expensive, politically sensitive, and often unnecessary. Meanwhile, cities already own something that can listen to what’s happening underground and along critical corridors: fiber-optic cable.

That’s the practical promise behind distributed acoustic sensing (DAS)—the topic discussed in a SmartCitiesWorld podcast conversation with Fotech’s leadership. DAS turns standard fiber into a dense, continuous sensor that detects vibrations. Paired with AI analytics, it becomes a real-time signal for infrastructure health, safety, and service performance.

For our series “Mākslīgais intelekts publiskajā sektorā un viedajās pilsētās”, DAS is a near-perfect example of how public-sector AI shouldn’t start with flashy dashboards. It should start with better data from the assets we already have, then translate that data into decisions: where to inspect, what to fix first, and how to prevent outages before residents feel them.

Distributed acoustic sensing, explained in one sentence

Distributed acoustic sensing turns a fiber-optic cable into thousands of virtual vibration sensors, producing continuous data that AI can classify into actionable events.

Traditional sensing often means installing devices point-by-point: microphones, accelerometers, flow meters, or CCTV. DAS flips the model. The fiber itself becomes the sensing element, with measurements taken at short intervals along the cable.

Here’s why public agencies care:

• Coverage: one fiber route can monitor long distances instead of one location.
• Passive infrastructure: no power or electronics in the ground along the route.
• Multi-use: the same sensing layer can support multiple operational goals (transport, water, security, civil works).

What DAS actually “hears”

DAS doesn’t record conversations. It detects vibration patterns—things like digging, vehicle movement, footsteps, or mechanical oscillations from a failing component.

That distinction matters for procurement and governance. The data is more like a continuous “seismograph for a corridor” than audio surveillance.

Why DAS is suddenly relevant again in 2025

Cities are under pressure to do more maintenance with less disruption—and AI needs better signals than citizen complaints.

By late 2025, most municipalities I speak with are juggling the same constraints:

• Aging water and district heating networks
• Rising construction activity and accidental utility strikes
• Higher expectations for service uptime
• Tighter budgets and difficulty recruiting field technicians
• Increasing scrutiny around privacy and surveillance

DAS fits this moment because it gives public works and utilities a way to detect the start of a problem, not just the consequences.

A practical example: if your only trigger for a water main leak is low pressure calls or visible flooding, you’re already late. With DAS, abnormal vibration signatures along a pipeline corridor can trigger an investigation earlier—especially when paired with AI classification and context (time of day, known construction permits, weather, etc.).

The myth to drop: “More sensors means more complexity”

Most teams assume adding sensing increases operational load. The reality is the opposite when AI is designed well:

DAS without good analytics is noise. DAS with AI is triage.

Your operations center doesn’t need “more data.” It needs fewer, better alerts—ranked by likelihood and impact.

Where DAS delivers the most value in a smart city

DAS is best when you need wide-area monitoring along linear assets: pipelines, rail, roads, perimeters, and critical corridors.

In the SmartCitiesWorld discussion with Fotech, the focus is on insight: using acoustic signatures to understand what’s happening across a city system. Let’s translate that into concrete smart city use cases that map to public-sector outcomes.

1) Utility corridor protection (water, gas, district heating)

DAS can detect vibrations consistent with excavation or drilling near buried utilities. With AI classification, agencies can distinguish:

• permitted construction activity
• suspicious digging outside permit hours
• “strikes” or sudden impact events

Operational payoff:

• fewer third-party damage incidents
• faster dispatch to verify risky activity
• better evidence trail for liability and claims

2) Rail and metro monitoring

Rail networks are inherently linear and sensitive to safety and uptime. DAS can support:

• trackside intrusion detection
• abnormal vibration patterns that suggest asset degradation
• monitoring near stations or depots

The AI layer matters because rail environments are “busy.” You need models that can tell routine train movement from anomalies worth attention.

3) Traffic flow and incident detection (without cameras everywhere)

DAS along key road corridors can infer:

• congestion signatures (stop-and-go vibration patterns)
• heavy vehicle passage
• collisions or abrupt impact events (with careful validation)

This doesn’t replace traffic cameras. It complements them, especially where privacy sensitivity is high or lighting/weather conditions are challenging.

4) Perimeter and critical infrastructure security

For sensitive assets—water treatment plants, substations, data centers—DAS can monitor fence lines and approach routes. The advantage is broad coverage with minimal visible hardware.

Public-sector requirement: security use cases must have clear governance, strict access controls, and transparent policies.

The AI part: how you turn “vibrations” into decisions

The value of distributed acoustic sensing comes from classification, correlation, and prioritization—exactly where applied AI performs well.

DAS produces high-volume, continuous signals. If you show raw traces to operators, they’ll ignore them. The workflow needs to look like this:

1. Signal capture along fiber (continuous)
2. Feature extraction (frequency, amplitude, pattern over distance/time)
3. Event detection (is something notable happening?)
4. Classification (excavation vs vehicle vs rain vs mechanical issue)
5. Context fusion (permits, asset maps, schedules, historical incidents)
6. Action recommendation (dispatch, monitor, escalate)

Practical model types used with DAS

You don’t need “mystery AI.” You need fit-for-purpose approaches:

• Supervised classifiers trained on labeled events (e.g., digging, drilling, traffic)
• Anomaly detection for rare signatures (e.g., unusual oscillation on a pipe section)
• Spatiotemporal clustering to group repeated events along the same segment
• Rules + ML hybrids (often best in public operations): rules for safety thresholds, ML for pattern recognition

“Answer first” alerts operators will actually trust

If you want adoption, alert design matters as much as model accuracy:

• What happened (event type + confidence)
• Where (map location + distance markers)
• So what (potential impact: safety, service disruption, cost)
• Now what (recommended action and SLA)

A snippet-worthy truth: If an alert doesn’t tell a dispatcher what to do next, it’s not an alert—it’s a notification.

Implementation in the public sector: what to plan for upfront

DAS projects succeed when cities treat them like an operational change program, not a sensor pilot.

Plenty of “smart city pilots” fail because they don’t fit day-to-day workflows. Here’s what I’ve found works when deploying AI-driven infrastructure monitoring.

Data and governance checklist (before procurement)

• Asset ownership clarity: who owns the fiber route—city IT, utility, telecom partner?
• Data retention policy: how long is raw vs processed data stored?
• Privacy position: clear statement that DAS measures vibration patterns, not conversations
• Security model: role-based access, audit logs, and separation between ops and security uses
• Interoperability: can alerts feed your existing SCADA, work order system, or city operations platform?

Operational design: who gets the alert?

A common mistake is routing all alerts to a central “smart city” team. The better approach is routing by responsibility:

• utility corridor events → utility control room / on-call supervisor
• road corridor anomalies → traffic management center
• security perimeter events → security operations

Then create shared situational awareness at the city level (a digest view), rather than making one team the bottleneck.

Procurement: specify outcomes, not just technology

Instead of buying “DAS,” buy measurable outcomes:

• detection of excavation near protected corridors within X minutes
• reduction in unplanned outages by X% in monitored zones
• dispatch accuracy improvements (fewer false truck rolls)
• time-to-locate incidents reduced from hours to minutes

Even if you don’t commit to exact percentages on day one, framing the tender around outcomes forces vendors to propose realistic deployment and tuning plans.

A realistic 90-day path to value

You can prove value quickly if you narrow scope and define what “good” looks like.

Here’s a public-sector friendly plan that doesn’t depend on perfect data maturity.

Days 1–30: choose one corridor, one goal

Pick a corridor with:

• frequent excavation activity or
• high consequence of failure (hospital zone, city center, main trunk line)

Define one primary goal: detect digging, detect leaks, or monitor rail intrusion. Not all at once.

Days 31–60: label events and tune models

You’ll need ground truth. Coordinate with:

• permit office (who is digging, when)
• field crews (what was found)
• traffic team (planned closures)

This is the unglamorous work that makes AI reliable.

Days 61–90: integrate into dispatch and measure

Connect alerts to the system crews already use. Measure:

• false positives per week
• average time from event to verification
• incidents detected before customer impact

If you can show fewer unnecessary dispatches and earlier detection, budget owners will listen.

What this means for the “AI in public sector” conversation

DAS is a reminder that AI outcomes depend on sensing and operations, not slogans. Cities that get ahead in 2026 won’t be the ones with the most dashboards. They’ll be the ones that:

• monitor assets continuously
• prioritize interventions based on risk
• document decisions with data
• respect privacy while improving service reliability

For the “Mākslīgais intelekts publiskajā sektorā un viedajās pilsētās” series, I’d argue DAS belongs in the same toolkit as traffic analytics and e-pārvaldes automation: it’s another way to make government services feel more dependable without adding friction for residents.

If you’re considering distributed acoustic sensing for a smart city, start small: pick one corridor, one operational problem, and one team that will actually use the alerts. Then scale what works.

Where in your city would “listening” deliver the fastest public value—utility corridors, transport, or critical infrastructure?