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Knowledge-Guided Floodwater Level Reasoning for Cloud-Edge Collaborative Monitoring — arXiv:2611.01970

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01970
License (as archived): CC0-1.0
Archived: 2026-11-03T18:18:19.776Z
SHA-256 of the archived text: 39ce66e1dbfdf2c5529e21fe7acba6f2d98dc091a386b9325800571eaaac6fbe
Stable URL of this page: https://metraipolis.com/documento/dc_02ee2147dd95

How to cite

Knowledge-Guided Floodwater Level Reasoning for Cloud-Edge Collaborative Monitoring — arXiv:2611.01970 — arXiv (en), https://arxiv.org/abs/2611.01970 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_02ee2147dd95 (Metraipolis archive dc_02ee2147dd95, sha256 39ce66e1dbfdf2c5529e21fe7acba6f2d98dc091a386b9325800571eaaac6fbe)

BibTeX

@misc{dc02ee2147dd95,
  title = {Knowledge-Guided Floodwater Level Reasoning for Cloud-Edge Collaborative Monitoring — arXiv:2611.01970},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01970},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_02ee2147dd95; sha256 39ce66e1dbfdf2c5529e21fe7acba6f2d98dc091a386b9325800571eaaac6fbe},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01970. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Frequent urban floodwater poses a serious threat to the safety and efficiency of intelligent transportation systems. Existing vision-based methods for floodwater level detection face two main limitations. First, the lack of explicit reasoning leads to poor detection performance and little interpretability. Second, these methods struggle to generalize to the complex and varied real-world road scenes seen from different viewpoints. To address this, we reformulate the task as an explicit visual reasoning problem and propose HydroReasoner, a floodwater level reasoning framework designed for cloud-edge collaboration. HydroReasoner builds on Qwen2.5-VL and is trained with Group Relative Policy Optimization. To address the scarcity of reasoning process annotations, we build a floodwater domain knowledge graph HydroKG and design a novel KG path reward. Through deterministic graph traversal over HydroKG, this reward supervises the model's reasoning process at a fine granularity, which improves both reasoning and generalization. For edge deployment, we apply 4-bit quantization to the model, cutting GPU memory usage by 57% and inference latency by 43% while out-of-domain accuracy drops by only 0.2 percentage points. We also propose a confidence-based cloud-edge routing mechanism, which improves system safety and eases the computational load on the cloud. In addition, we build HydroLevel, a floodwater benchmark dataset of 3,000 images, for training and evaluating the model. Experimental results show that HydroReasoner achieves detection accuracy 4.0 and 13.6 percentage points higher than conventional vision baselines in the in-domain and out-of-domain evaluations respectively, with performance dropping by only 4.4 percentage points out of domain.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01970 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T18:09:53.644Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).