Towards Governance-Oriented Low-Altitude Intelligence: A Management-Centric Multi-Modal Benchmark With Implicitly Coordinated Vision-Language Reasoning Framework
This addresses the need for practical urban governance tools by providing a new benchmark and framework, though it is incremental as it builds on existing vision-language methods.
The paper tackles the problem of supporting management-oriented anomaly understanding in low-altitude vision systems for smart city governance by introducing GovLA-10K, a management-centric multi-modal benchmark, and GovLA-Reasoner, a vision-language reasoning framework, which significantly improves performance without fine-tuning task-specific components.
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 feature adapter that implicitly coordinates discriminative representation sharing between the visual detector and the large language model (LLM). Extensive experiments show that our method significantly 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.