用深度学习提取无人机灾后影像关键信息，减少传输数据量。
Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning
- 在机载端运行语义提取器，筛选任务相关数据
- 在两个数据集上保持高精度，传输数据量显著减少
- 适合资源受限的灾后无人机实时决策场景
无人飞行器（UAV）辅助的灾后救援任务因可靠性与灵活性受到重视。尽管机载机器学习算法可实现实时处理与高效决策，但受限于带宽和间歇性连接，将结果传回地面站仍具挑战。本文提出一种新型语义提取器，可集成于任意下游机器学习任务中，用于识别决策所需的关键数据。该提取器可在机载端执行，显著降低需上传至地面站的数据量。我们在 FloodNet 与 RescueNet 两个公开数据集上，针对视觉问答与灾害损毁等级分类两项任务测试了该架构。实验结果表明，该方法在不同下游任务中均保持高准确率，同时大幅减少传输数据量，验证了其在捕捉任务特定显著信息方面的有效性。
原文摘要 · Abstract (English)
Unmanned aerial vehicle-assisted disaster recovery missions have been promoted recently due to their reliability and flexibility. Machine learning algorithms running onboard significantly enhance the utility of UAVs by enabling real-time data processing and efficient decision-making, despite being in a resource-constrained environment. However, the limited bandwidth and intermittent connectivity make transmitting the outputs to ground stations challenging. This paper proposes a novel semantic extractor that can be adopted into any machine learning downstream task for identifying the critical data required for decision-making. The semantic extractor can be executed onboard which results in a reduction of data that needs to be transmitted to ground stations. We test the proposed architecture together with the semantic extractor on two publicly available datasets, FloodNet and RescueNet, for two downstream tasks: visual question answering and disaster damage level classification. Our experimental results demonstrate the proposed method maintains high accuracy across different downstream tasks while significantly reducing the volume of transmitted data, highlighting the effectiveness of our semantic extractor in capturing task-specific salient information.
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