arXiv:2601. 11665v3 Announce Type: replace Abstract: Unmanned Aerial Vehicles (UAVs) are transforming infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management (AEC+FM) domain.
arXiv:2609.13013v1 Announce Type: new Abstract: Flat roofs are among the most influential components of the building envelope, governing both structural performance and thermal efficiency, and thereb...
arXiv:2606. 14963v1 Announce Type: cross Abstract: Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery.
arXiv:2606. 06536v1 Announce Type: cross Abstract: Automated defect detection in high-voltage transmission-line insulators remains challenging due to severe class imbalance, large scale variation, and the small spatial extent of defect instances in Unmanned Aerial Vehicle (UAV) imagery.
The paper introduces YOLOEZ, a no-code, GUI-based tool that streamlines the entire YOLO model workflow—data labeling, training, and inference—for automated structural defect detection. It demonstrates that YOLOEZ outperforms traditional image‑processing methods across most detection metrics while simplifying deployment for users without programming expertise. The tool aims to lower technical barriers in structural health monitoring, enabling broader adoption of AI-driven inspection for predictive maintenance and intelligent structural systems.
arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.
The paper revisits building damage assessment (BDA) from satellite imagery, emphasizing the need for a sensor‑agnostic, instance‑based task definition that can handle missing pre‑event data. It reviews existing datasets by modality, timing, and geography, critiques current single‑score evaluation practices, and proposes a more robust, ranking‑based multi‑score protocol. The authors also survey deep learning methods, highlight challenges in generalizing to new events and regions, and outline how these insights could shape future BDA challenges and data‑sharing practices.
arXiv:2608. 19866v1 Announce Type: new Abstract: This paper presents a novel data-driven approach to camera-based autonomy for micro-drones in GPS-denied, radio-challenging indoor environments.
arXiv:2610.17762v1 Announce Type: new Abstract: Utility-scale photovoltaic (PV) inspection is dominated by aerial surveys, which are effective for identifying module-level hot cells but provide limit...
arXiv:2606. 09882v1 Announce Type: cross Abstract: The paradigm of digital twin cities is shifting from coarse visual mapping toward more precise and actionable digitization of urban assets.
arXiv:2606. 06217v1 Announce Type: cross Abstract: When a disaster unfolds, responders must answer not only what is happening, but also why it is happening, what will happen next, and what to do now, often from noisy low-altitude UAV views and under tight on-site compute constraints.
arXiv:2510. 21112v3 Announce Type: replace-cross Abstract: High-definition 3D city maps enable city planning and change detection, which is essential for municipal compliance, map maintenance, and asset monitoring, including both built structures and urban greenery.
The paper presents a multi‑campaign UAV thermal image dataset for inert ordnance screening, comprising 5,855 labeled image pairs collected in Tennessee across diverse terrains and seasons. The authors trained YOLOV11l and RT‑DETR‑R50 models on 33 m and 15 m altitude data, achieving automated candidate detection, and provided practical guidelines for future humanitarian mine action surveys. The dataset and models aim to aid screening and prioritization for follow‑up technical surveys or EOD assessment, not to replace clearance operations.