Smart Dashboard Puts AI-Rebuilt 3D City Building Data in a 300 KB Pocket

Every building in the European Union is supposed to have a digital energy identity by 2030. That is the deadline embedded in the recast Energy Performance of Buildings Directive, which obliges Member States to digitise their national building stocks so that individual structures can be assessed, renovated and decarbonised one by one. It is an ambition that sounds tidy on paper and chaotic in the field, because the people who must actually carry it out—surveyors with tablets, energy-agency analysts with spreadsheets and patchy 3G connections—are being asked to make consequential decisions about real buildings using incomplete, noisy and enormous three-dimensional data. A new study published in Mobile Networks and Applications by Annamária Behúnová, Vladislav Vavrák, Matúš Pohorenec and Marcel Behún of the Technical University of Košice shows how that gap might be closed with a surprisingly modest piece of technology: a dashboard weighing less than a typical smartphone photograph.

The technical heart of the problem lies in LiDAR, the laser-scanning technique that fires millions of light pulses at façades, roofs and streets to capture the geometry of the built environment. Airborne or mobile LiDAR campaigns rarely see everything. Occlusions from trees, neighbouring buildings and blind angles leave point clouds riddled with holes, so researchers have turned to artificial intelligence models—geometry-aware transformers such as PoinTr and AdaPoinTr, and completion architectures like SnowflakeNet and PointAttN—to fill in the missing geometry. These models hallucinate plausible surfaces where the laser never reached, producing complete building shapes from partial captures. The trouble is that a completed point cloud is not a certified measurement. If an algorithm invents the far side of a roof, how good is that invention, and who decides when it is good enough to base renovation priorities on?

That question is where the Košice team makes its sharpest intervention. In the point-cloud completion literature, quality is typically reported as an F-score computed after normalising the data, and it is tempting to treat a threshold on that score as a physical accuracy guarantee. The authors show, through a careful unit-chain analysis, that this temptation is a trap. A nominal acceptance threshold of two centimetres, read off a normalized completion F-score, in fact certifies a median physical tolerance of 7.6 centimetres—and that tolerance drifts with the size of the building. In other words, a number that looks like a length is not a length at all, and certifying building data on that basis would be an error silently baked into policy. Instead, the team re-anchors acceptance tiers to physical point-to-mesh deviation bands drawn from scan-to-BIM accuracy specifications, including the Level of Accuracy guidance used by the building-documentation industry, so that every quality class corresponds to a real, size-independent geometric tolerance that a surveyor or regulator can understand.

With the acceptance rule fixed, the remaining challenge was delivery. Field deployment means commodity mobile hardware, intermittent connectivity and campaign budgets that cannot absorb cloud-computing costs or round trips to a distant server for every query. The researchers therefore built a mobile-edge dashboard that carries the entire decision-support apparatus to the browser. They demonstrated it on 9,998 AI-completed buildings drawn from the TU Delft Building-PCC 50k benchmark, a large-scale dataset of building point clouds designed for completion research. The primary view of the dashboard ships in a 251 KB payload—comfortably under the 300 KB target in the study’s title—which includes enough pre-computed statistical structure that the heavy lifting of quality triage happens locally, on the device, without any server-side computation.

The interaction model is deliberately simple and deliberately fast. Live sliders let an analyst move the physical accuracy bounds—the point-to-mesh deviation bands—up and down, and the dashboard recomputes the distribution of buildings across acceptance tiers in linear time, using plain JavaScript with no framework dependencies. The same sliders project the campaign-level consequences: because better-verified digital models mean fewer repeat site visits, the tool estimates the survey-travel carbon savings, amounting to 35.6 tonnes of CO₂-equivalent in the study’s mid bracket for the demonstrated building set. That figure is not a hypothetical for the whole EU stock; it is a measured projection for the benchmark buildings under way, illustrating how the choice of an acceptance threshold propagates directly into diesel burned and carbon emitted by inspection vehicles.

Performance numbers are reported with unusual rigour for a dashboard paper. Under throttled network emulation corresponding to a 1.6 Mbps 3G link, the cold-cache time-to-interactive was measured at 3.0 seconds; on 4G it dropped to 1.0 second. Those figures matter because the entire premise of the system is that it works where infrastructure is worst—on a rural survey route, in a basement archive, at the edge of a network cell. The engineering contract that makes this possible is pinned by schema-lock unit tests, which guarantee that the pre-computed data shipped from the edge always matches what the browser code expects, so a silent schema change cannot corrupt the triage logic in the field.

Governance receives as much attention as speed. The system is designed to be auditable against the OWASP Top 10:2021 catalogue of critical web application security risks and against WCAG 2.2, the current Web Content Accessibility Guidelines, so that security reviewers and accessibility auditors both have a defined checklist rather than a black box. It follows GDPR data minimisation by design: because the analytics run client-side on pre-computed summaries, no building-level personal or location data needs to leave the device, and there is no back-end to breach. The architecture also embodies the Industry 5.0 principle of human oversight, positioning the dashboard not as an automated gatekeeper but as a transparent instrument that keeps a human expert in command of every acceptance decision. The team frames this within a broader line of human-centric digitalisation work, arguing that sustainability triage of buildings is precisely the kind of consequential, norm-laden judgement that should not be fully delegated to a model.

The broader significance of the work goes beyond one dashboard. Cities worldwide are investing in digital twins—living digital replicas of urban infrastructure—yet the field has repeatedly stumbled on the question of data quality assurance: what does it mean for a machine-completed model to be trustworthy enough for energy simulation, renovation subsidy allocation or regulatory compliance? By tying AI completion quality to normative, physically meaningful tolerance bands, the Košice study offers a template for converting benchmark scores into regulatory language. It also demonstrates a deployment philosophy that runs counter to the current fashion for ever-heavier client applications: a quarter-megabyte of carefully pre-computed data and dependency-free JavaScript can outperform a bloated framework app where connectivity and hardware budgets are thinnest.

For the 2030 deadline itself, the implications are practical. Energy agencies do not need to wait for nationwide 5G coverage or for every municipality to procure server infrastructure; a decision tool that runs on a mid-range phone over 3G can be distributed to surveyors almost as easily as a PDF. The triage framing is equally consequential: rather than demanding perfect digital models of every building, the system sorts the stock into tolerance tiers, allowing renovation programmes to prioritise buildings whose digital representations are confidently reliable while flagging those that need re-surveying or better completion models. That kind of tiered honesty about uncertainty is rare in smart-city marketing and essential in energy policy, where a misclassified building can mean misdirected subsidies and unmet carbon targets.

The study, partially funded through Slovak research programmes including KEGA and APVV projects on digital technologies and circular construction, arrives at a moment when the gap between AI capability and regulatory usability has become the central bottleneck of urban digitalisation. Point-cloud completion transformers will keep improving, and benchmarks like Building-PCC will keep expanding. What the Košice team adds is the connective tissue: a unit-sound acceptance rule, a browser-native decision tool that fits in 251 KB, measured performance on the worst networks a surveyor is likely to meet, and an audit trail aligned with security, accessibility and privacy norms. It is a reminder that the path from a laboratory benchmark to a policy instrument is paved with unglamorous engineering—and that sometimes the most futuristic thing a smart city can deploy is a very small, very fast, very honest web page.

Subject of Research: A mobile-edge smart-city dashboard that certifies AI-completed LiDAR building point clouds against physical accuracy tolerances for EU building-stock sustainability triage.

Article Title: A Mobile-edge Smart-city Dashboard for AI-completed LiDAR Building Stocks: Normative-anchored Sustainability Triage Under a 300 KB Payload

Article References: Behúnová, A., Vavrák, V., Pohorenec, M., & Behún, M. (2026). A Mobile-edge Smart-city Dashboard for AI-completed LiDAR Building Stocks: Normative-anchored Sustainability Triage Under a 300 KB Payload. Mobile Networks and Applications. https://doi.org/10.1007/s11036-026-02534-4

Image Credits: AI Generated

DOI: 10.1007/s11036-026-02534-4

Keywords: smart city, mobile-edge computing, LiDAR, point cloud completion, building energy performance, digital twins, scan-to-BIM accuracy, Industry 5.0, GDPR data minimisation, WCAG 2.2, sustainability triage, TU Delft Building-PCC benchmark