WinLeak: A Thermal + RGB Image Dataset for Window Air Leakage Detection
BuildSys ’25 · Dataset Short Paper
Anveshika Kamble, Naman Gupta, Nishanth Pullabhotla, Daniel Mossé, Stephen Lee, Benjamin Rottman, Dilip Teja
Overview
WinLeak is a publicly available thermal + RGB image dataset designed for automated window air-leakage detection in residential buildings.
It addresses the lack of scalable, low-cost thermal data for building-energy diagnostics by combining high-end (FLIR E50) and consumer-grade (FLIR ONE Edge Pro) thermal imagery captured under controlled indoor–outdoor temperature differentials (≥ 18 °F / 10 °C).
Traditional blower-door or manual thermography tests are expensive and not scalable.
WinLeak provides an annotated, multi-modal resource enabling machine-learning models to detect window leaks efficiently and reproducibly.
Abstract (Summary)
Window air leakage can contribute up to 40 % of residential heating-and-cooling inefficiencies. WinLeak offers 1,266 paired thermal and RGB images from 7 buildings and 27 windows across multiple configurations.
Each pair includes expert-verified segmentation masks of leaky regions, generated through a semi-automated process that blends temperature-threshold sweeps with majority-vote human selection.
To demonstrate dataset utility, a dual-encoder U-Net model leveraging both modalities was trained for pixel-wise segmentation of leakage zones.
Dataset Highlights
| Category | Details |
|---|---|
| Total Images | 1,266 thermal + 1,266 RGB |
| Buildings / Windows | 7 buildings / 27 windows |
| Window Types | 7 (sliding, single-hung, double-hung, picture etc.) |
| Classes | OPEN (431) · LEAK (562) · NO_LEAK (273) |
| Date Range | 2024-09-09 → 2025-03-22 |
| Indoor Temp | 64 – 77 °F |
| Outdoor Temp | 11 – 66 °F |
| Gap Settings (cm) | 0 · 0.5 · 1 · 2 |
| Camera Heights (m) | 1 · 1.6 |
| Angles (°) | −30 · 0 · +30 |
| Thermal Contrast Rule | ≥ 18 °F (10 °C) indoor–outdoor differential |
Imaging Hardware
| Specification | FLIR E50 | FLIR ONE Edge Pro |
|---|---|---|
| Thermal Resolution | 240 × 180 | 160 × 120 |
| RGB Resolution | 2048 × 1536 | 640 × 480 |
| Thermal Sensitivity (NETD) | 50 mK | 70 mK |
| Emissivity Used | 0.97 (both) | |
| Accuracy | ± 2 % | ± 5 % |
| Thermal FOV | 25° × 19° | 54° × 42° |
| RGB FOV | 53° × 41° | ≈ 72° × 56° (estimated) |
Both cameras capture synchronized thermal and visual frames. Emissivity was fixed at 0.97 for materials such as glass, vinyl, paint, and wood.
Data-Collection Methodology
- Locations & Scope: Seven residential sites across the U.S.; each window photographed in four closure states (0, 0.5, 1.0, 2.0 cm gap).
- Angles & Distances: Captured from multiple heights and oblique/frontal views to mimic realistic inspector variability.
- Environmental Control: Indoor–outdoor ΔT ≥ 10 °C ensured visible IR contrast.
- Alignment: Minor spatial offsets between RGB and IR sensors corrected via post-processing alignment.
- Leak Simulation: Mechanical props ensured repeatable small-gap openings.
- Annotation: Semi-automated threshold-sweep masks → expert majority-vote refinement.
Comparison with Existing Datasets
| Dataset | Focus | Modalities | Air-Leakage Masks |
|---|---|---|---|
| WinSet (2021) | Window state | Thermal + RGB + LiDAR | No |
| IR Survey | Window / door detection | Thermal + RGB (1 camera) | No |
| CIDIS | Cross-spectral registration | Thermal + RGB | No |
| Street Scene | Window detection (urban) | RGB | No |
| WinLeak (ours) | Air leakage detection | Thermal + RGB (2 cameras) | ✅ Yes |
WinLeak is the first dataset to pair high-end and low-cost thermal imaging for controlled air-leakage segmentation at the residential-window level.
Dataset Access
The WinLeak dataset is publicly available on Kaggle:
Download: https://www.kaggle.com/datasets/namangupta99/winleak/data
Baseline Results by Authors
A dual-encoder U-Net combining RGB and thermal inputs was trained for pixel-wise segmentation by the research team.
| Metric (Level) | Score |
|---|---|
| Pixel Accuracy | 0.98 |
| Precision | 0.53 |
| Recall | 0.47 |
| F1 / Dice (Pixel) | 0.50 |
| IoU (Pixel) | 0.33 |
| IoU (Region) | 0.76 |
| Dice (Region) | 0.85 |
Region-level metrics show the model reliably captures contiguous leak patterns even when pixel-level precision is moderate.
License and Citation
License: Creative Commons Attribution 4.0 (CC BY 4.0)
https://creativecommons.org/licenses/by/4.0/
Citation
@inproceedings{winleak_buildsys25,
author = {Anveshika Kamble and Naman Gupta and Nishanth Pullabhotla and
Daniel Moss{\'e} and Stephen Lee and Benjamin Rottman and Dilip Teja},
title = {WinLeak: A Thermal and RGB Image Dataset for Window Air Leakage Detection in Residential Buildings},
booktitle = {Proceedings of the 12th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’25)},
year = {2025},
address = {Golden, CO, USA},
doi = {10.1145/3736425.3770112},
isbn = {979-8-4007-1945-5/2025/11}
}
Acknowledgments
This work used resources from the University of Pittsburgh Center for Research Computing (H2P cluster) supported by NSF OAC-2117681 and partially by NSF #2324873.
We thank participating homeowners and research collaborators for enabling data collection and validation.
Contact
Anveshika Kamble
📍 University of Pittsburgh
📧 LinkedIn Profile
Naman Gupta
📍 University of Pittsburgh
📧 LinkedIn Profile
Nishanth Pullabhotla
📍 University of Pittsburgh
📧 LinkedIn Profile
Dilip Teja
📍 University of Pittsburgh
📧 LinkedIn Profile
This site mirrors the accepted BuildSys 2025 dataset short paper and will evolve with future releases of metadata, annotations, and benchmark updates.