FloodVLM-Pix is a new language‑guided pixel‑level flood understanding model introduced in the arXiv paper "FloodVLM-Pix: Language‑Guided Pixel‑Level Flood Understanding." The authors created the first large‑scale remote sensing flood multi‑modal benchmark, comprising 131,200 image patches and 443,100 textual descriptions of flood severity and evolution. FloodVLM‑Pix incorporates a flood prior module to mitigate attentional bias and a multi‑temporal flood pixel decoder with a general‑specific feature fusion mechanism to improve flood segmentation accuracy. "whyItMatters":"The paper presents a benchmark and model that enhance both flood description quality and pixel‑level segmentation, addressing shortcomings of existing vision‑language models on flood‑related tasks."
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.
arXiv:2608. 15410v1 Announce Type: cross Abstract: Reasoning segmentation enables vision-language models (VLMs) to translate mission-relevant language requests into pixel-level visual grounding, offering a natural perception interface for embodied agents.
arXiv:2505.15147v3 Announce Type: replace Abstract: Remote sensing images (RSIs) capture both natural and human-induced changes on the Earth's surface. Semantic segmentation (SS) of RSIs enables the...
arXiv:2608. 07562v1 Announce Type: cross Abstract: Urban flooding poses an escalating threat to transportation infrastructure, yet no operational system provides real-time, street-level flood-depth estimates at centimeter resolution.
arXiv:2606. 18661v1 Announce Type: cross Abstract: Intelligent landslide hazard interpretation is critical for disaster prevention, yet current paradigms struggle to simultaneously extract visual features and high-level geoscientific semantics, while general-purpose vision-language models (VLMs) suffer from perceptual limitations and domain hallucinations in complex geological scenarios.
The paper introduces FloodVision, a knowledge-guided vision‑language framework that estimates urban flood depth from a single RGB image. It combines a general‑purpose vision‑language model with FloodKG, a domain knowledge base that encodes canonical object dimensions and component landmarks to promote component‑level reasoning. On 654 crowdsourced New York flood images, FloodVision cuts mean absolute error from 15.62 cm to 8.75 cm and median error from 14.35 cm to 7.75 cm, outperforming the VLM‑only baseline in 69.3 % of cases.
Intelligent landslide hazard interpretation is critical for disaster prevention, yet current paradigms struggle to simultaneously extract visual features and high-level geoscientific semantics, while general-purpose vision-language models (VLMs) suffer from perceptual limitations and domain hallucinations in complex geological scenarios. To address these challenges, we propose an instruction-driven agentic framework comprising three components.
arXiv:2606. 02092v1 Announce Type: cross Abstract: Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets.
arXiv:2608. 03822v1 Announce Type: cross Abstract: Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation.
arXiv:2607. 16050v1 Announce Type: new Abstract: Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive process-based models unsuitable for daily continental-scale use.
The paper introduces NWPU-Refer, the largest bilingual dataset for referring remote sensing image segmentation, featuring 15,003 high‑resolution images from over 30 countries and 49,745 annotated targets. It also proposes the Multi‑scale Referring Segmentation Network (MRSNet), which uses an Intra‑scale Feature Interaction Module to refine fine‑grained features and a Hierarchical Feature Interaction Module to fuse cross‑scale information while preserving spatial structure. Experiments on NWPU‑Refer and RRSIS‑D show that MRSNet achieves strong, competitive performance, especially under strict IoU thresholds, demonstrating the effectiveness of both the new dataset and the model.
DisasterInsight is a building‑centric benchmark designed to evaluate vision‑language models (VLMs) for disaster response. Built on the xBD satellite dataset, it adds OpenStreetMap‑derived functional labels to 134,108 building instances and offers 15 task types, including instance assessment, scene counting, multi‑instance reasoning, and structured report generation. Experiments show that VLMs excel at visible damage detection but struggle with building function, multi‑instance reasoning, counting, and grounded reporting, and instruction tuning only partially mitigates these gaps.