arXiv:2608. 14317v1 Announce Type: cross Abstract: This research developed a neural network-based model to extract various information from 2D floor plans.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
The paper introduces Sewer-Transformer-ML, a hierarchical vision Transformer that fuses multi‑level features for multi‑label sewer defect classification, and two lightweight variants, Sewer-MobileNet-ML and Sewer-Mobile-TransNet, tailored for resource‑constrained inspection scenarios. On the Sewer‑ML test set, Sewer‑Transformer‑ML‑Base achieved an $F2_{ ext{CIW}}$ of 65.68% and an $F1_{ ext{Normal}}$ of 92.68%, topping the public leaderboard and surpassing the next best method by 7.6 percentage points in $F2_{ ext{CIW}}$. The lightweight Sewer‑MobileNet‑ML reached a comparable $F2_{ ext{CIW}}$ of 65.73% with only 17 M parameters, a 95% reduction from the base model, while Sewer‑Mobile‑TransNet achieved 96.43% accuracy under the standard data split, and ablation studies highlighted the effectiveness of direct concatenation for Transformer features and attention‑based fusion for multiscale CNN features.
arXiv:2606. 01229v1 Announce Type: new Abstract: During green building design, computer-aided energy assessment is widely used to improve efficiency and achieve overall optimization.
arXiv:2608.03323v2 Announce Type: replace Abstract: Estimating room layouts from multi-view imagery is a core task for indoor scene understanding. Existing methods are typically limited either by poo...
arXiv:2606. 08033v1 Announce Type: cross Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages.
arXiv:2609.36064v1 Announce Type: cross Abstract: Foundation models are powerful generators, but many engineering domains require structured representations that general-purpose systems handle poorly...
arXiv:2509. 09794v5 Announce Type: replace Abstract: Computational models have emerged as powerful tools for multi-scale energy modeling research at the building and urban scale, supporting data-driven analysis across building and urban energy systems.