arXiv:2607. 04882v1 Announce Type: cross Abstract: Clandestine tunneling beneath oil and gas pipelines enables fuel theft, smuggling, and sabotage, yet conventional monitoring detects damage only after a pipeline has been compromised.
By Muhammad Junaid, Shoab A. Khan, Nisar Ahmed
The paper introduces a depth‑aware pothole detection framework that fuses RGB‑D sensor data and evaluates five architectures—YOLOv8n, YOLOv8nSeg, YOLOv9t, RTDETRL, and RTDETRX—on the PothRGBD dataset. YOLOv8nSeg achieves the highest detection performance (mAP@50 = 0.9556, mAP@50_95 = 0.6758) and the most accurate depth estimate (2.96 cm), while YOLOv8n offers the fastest inference (3.6 ms) and RTDETRX delivers the highest detection confidence (92.70 %). The study also shows that even after RANSAC orthorectification, bounding‑box models overestimate pothole depth by 0.16–0.21 cm, indicating a structural bias rather than a calibration error.
By Md Monjurul Ahsan Prodhan, Md Nour Hossain
The paper demonstrates a lightweight method to adapt general-purpose vision‑language models (VLMs) for multispectral and synthetic aperture radar (SAR) image understanding. By rendering each observation as five optical views and one SAR view, naming them in the prompt, and applying LoRA to the language network and selected visual transformer blocks, the authors enable VLMs to process band composites, spectral indices, and radar backscatter without retraining a new foundation model. On a balanced six‑class land‑cover benchmark from BigEarthNet‑v2, the adapted Qwen3‑VL achieves a micro F1 score of 0.8275, and the same protocol improves four other VLMs and transfers to flood verification and captioning tasks.
The paper presents a lightweight method to adapt general‑purpose vision‑language models (VLMs) for multispectral and synthetic aperture radar (SAR) image understanding. By rendering each observation as five optical views and one SAR view, naming them in the prompt, and applying LoRA to the language network and selected visual transformer blocks, the authors enable VLMs to process band composites, spectral indices, and radar backscatter without retraining a new foundation model. On a balanced six‑class land‑cover benchmark from BigEarthNet‑v2, the adapted Qwen3‑VL achieves a micro F1 of 0.8275, and the same protocol improves four other VLMs and transfers to flood verification and captioning tasks.
"whyItMatters":"The study shows that existing VLMs can be repurposed for multispectral and SAR tasks through simple input rendering and compact LoRA adaptation, avoiding the need for dedicated encoders and domain pretraining."
By Shanji Liu, Kelu Yao, Junxiao Xue, Chenghui Lv, Xiangyang Miao, Yekai Huang, Yaying Chen, Chao Li
arXiv:2608.24594v1 Announce Type: new
Abstract: Submerged kelp forests are vital coastal ecosystems that support marine biodiversity and ecosystem dynamics, yet accurate underwater kelp segmentation...
By Sundarabalan Balasubramanian, C\'esar Borja, Ana C. Murillo, Lexi N. Wilkes, Meredith L. McPherson, Kira A. Krumhansl, Jennifer A. Dijkstra, Jarrett E. K. Byrnes
The paper presents a three‑stage, parameter‑efficient approach to improve automatic target recognition (ATR) with synthetic aperture sonar (SAS) data by adapting DINOv3 Vision Transformers. Stage 1 applies Low‑Rank Adaptation (LoRA) while freezing the backbone, which significantly boosts the area under the precision‑recall curve from 0.300 to 0.679. Subsequent hard‑negative mining and supervised contrastive learning stages show negligible impact, indicating that a single LoRA adaptation is sufficient for effective underwater ATR.
By Dan Zimmerman, Frank E. Bobe III, Amelia L. McCormack, Matthew Cook, Gregory D. Vetaw