Focus on What Really Matters in Low-Altitude Governance: A Management-Centric Multi-Modal Benchmark with Implicitly Coordinated Vision-Language Reasoning Framework

TL;DR

This paper introduces GovLA-10K, a management-focused multi-modal benchmark and GovLA-Reasoner, a vision-language reasoning framework designed for urban governance, emphasizing salient targets and spatially-aware grounding.

Contribution

It presents the first management-oriented benchmark and a novel spatially-aware adapter for integrated visual and language reasoning in low-altitude governance systems.

Findings

GovLA-Reasoner improves performance without task-specific fine-tuning.

The benchmark focuses on management-relevant targets for urban governance.

The Spatially-aware Grounding Adapter effectively integrates spatial cues into reasoning.

Abstract

Low-altitude vision systems are becoming a critical infrastructure for smart city governance. However, existing object-centric perception paradigms and loosely coupled vision-language pipelines are still difficult to support management-oriented anomaly understanding required in real-world urban governance. To bridge this gap, we introduce GovLA-10K, the first management-oriented multi-modal benchmark for low-altitude intelligence, along with GovLA-Reasoner, a unified vision-language reasoning framework tailored for governance-aware aerial perception. Unlike existing studies that aim to exhaustively annotate all visible objects, GovLA-10K is deliberately designed around functionally salient targets that directly correspond to practical management needs, and further provides actionable management suggestions grounded in these observations. To effectively coordinate the fine-grained visual grounding with high-level contextual language reasoning, GovLA-Reasoner introduces an efficient Spatially-aware Grounding Adapter (SGA) that implicitly coordinates discriminative representation sharing between the visual detector and the large language model (LLM). Different from existing adapters that primarily focus on global embedding alignment, our SGA is specifically designed to compress and aggregate multi-stream grounding-aware representations, thereby preserving fine-grained spatial cues while enabling their effective integration into the language reasoning process. Extensive experiments indicate that our GovLA-Reasoner effectively improves performance while avoiding the need of fine-tuning for any task-specific individual components. We believe our work offers a new perspective and foundation for future studies on management-aware low-altitude vision-language systems. The code and dataset will be publicly released after further organization.