FloodReasonBench: Benchmarking VLM Reasoning Segmentation for Embodied Flood Response at the Edge

2026-08-15

AI Summary

This study addresses the lack of flood rescue-specific benchmarks and edge resource constraints in existing VLM reasoning segmentation research by constructing the first dedicated dataset and benchmark for this domain. Leveraging lightweight visual encoding, hierarchical reasoning, and intermediate representation compression, we evaluate system performance on real-world edge devices. Our analysis reveals partition-dependent accuracy variations and elucidates multidimensional trade-offs among accuracy, latency, energy consumption, and communication overhead. By achieving joint task- and system-level characterization, this work effectively facilitates the deployment of embodied intelligence in resource-constrained environments, bridging the gap between high-level semantic reasoning and practical edge implementation for emergency response scenarios.

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. However, existing benchmarks largely focus on generic visual scenes and overlook the domain and resource constraints encountered in flood-response platforms. We present FloodReasonBench, a benchmark for VLM reasoning segmentation for embodied flood response at the edge. At its core, FloodReasonBench introduces FloodResponseSeg, a flood-specific reasoning-segmentation dataset constructed from real-world scenes and response-relevant targets. Beyond task accuracy, the benchmark characterizes reasoning-segmentation pipelines under lightweight visual encoding, hierarchical split inference, and compressed intermediate representations. We observe strong partition-dependent accuracy variation in the generic pre-adaptation setting, while the flood-adapted target-workload design space exhibits a substantially more compact accuracy range across partitions. Evaluation on an NVIDIA Jetson AGX Xavier further exposes the tradeoffs among reasoning-segmentation accuracy, edge-side latency, energy, and communication footprint, enabling quality-constrained selection of edge operating points. Together, these results provide a task- and system-level characterization of reasoning segmentation for resource-constrained embodied flood response at the edge.

Problem

• Reasoning Segmentation
• Embodied Flood Response
• Edge Computing
• Vision-Language Models
• Benchmark

Innovation

• Reasoning Segmentation
• Edge Computing
• Embodied AI
• Flood Response
• Benchmark