The paper evaluates 16 computer‑vision models for mapping electrical substation components, training them on a manually labeled dataset of U.S. substation images. The best performer, RF‑DETR‑large, achieved mAP@50 of 0.881 and mAP@50:95 of 0.632, with alternate energy systems detected most accurately and transformers/reactors posing the greatest challenge. Applying this model nationwide produced about 22,591 component detections across 11,083 substations, with Florida and the MISO region showing the highest counts.
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
The paper reviews domain generalization (DG) for object detection, highlighting its scarcity and unique challenges such as localization and multi-scale representation. It examines synthetic data from three angles: as an enabler that diversifies and aligns training data, as a probe that allows controlled experiments to uncover failure modes, and as a source of a synthetic‑to‑real gap that hampers deployment on real imagery. The authors conclude that future DG research must develop representation‑aware methods that explicitly address both localization and classification under domain shift.
arXiv:2608. 11053v1 Announce Type: cross Abstract: The application of computer vision in agriculture has shown significant potential for improving crop monitoring and precision farming.
arXiv:2608.30727v1 Announce Type: cross Abstract: Small-object detection under long-tailed data distributions is a fundamental yet challenging problem in multimedia. Railway Foreign Object Detection...
arXiv:2609.09482v1 Announce Type: new Abstract: Critical infrastructure location data is often incomplete and unevenly distributed globally, especially in developing regions. Earth observation founda...
arXiv:2607. 00270v1 Announce Type: cross Abstract: Algorithm development for radioisotope identification in mobile urban search scenarios face significant challenges from non-uniform backgrounds, momentary source encounters, and severe class imbalance between rare threat signatures and background measurements.
The paper introduces a hybrid two‑stage machine learning pipeline for fault detection and classification in high‑voltage transmission networks. Stage 1 uses an Isolation Forest anomaly detector combined with an optional supervised binary detector, while Stage 2 applies a Random Forest multiclass classifier only to samples flagged by Stage 1. Feature engineering maps six raw channels to eighteen features, including zero‑sequence symmetrical components, achieving end‑to‑end accuracies of 95.8 % on the TLFaultDataset and 97.25 % on an independent single‑point dataset, surpassing federated benchmarks without GPU or federated infrastructure.
arXiv:2508. 03736v2 Announce Type: replace-cross Abstract: In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations.
Annual Earth‑observation embeddings, specifically Tessera, can encode wildfire disturbance signals well enough to map burned areas without needing curated fire‑specific imagery or dense time‑series analysis. In tests, linear models using a single Tessera embedding matched or outperformed paired pre‑ and post‑fire HLS imagery and post‑fire imagery alone, achieving high F1 scores for burn‑scar delineation and regional mapping. The approach successfully mapped all same‑year fires in benchmark scenes, recovered 97% of California burned area without California training data, transferred to European fires with high accuracy, and even estimated ignition timing within a 13‑day error margin. "whyItMatters":"The study demonstrates that pre‑trained annual embeddings can simplify and scale burned‑area mapping, reducing reliance on dense time‑series data and enabling more efficient wildfire monitoring."
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. 27018v1 Announce Type: cross Abstract: Remote Sensing Foundation Models (RSFMs) have emerged as a powerful alternative to supervised models for Earth Observation, allowing satellites to autonomously trigger high-resolution captures or adjust tasking parameters upon detecting an anomaly, thereby maximizing the utility of the mission's limited power and computational resources.
arXiv:2609.36215v1 Announce Type: new Abstract: Power-grid digital twins must combine data-driven prediction with physically meaningful state evolution while preserving the provenance of environmenta...