Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning
This work addresses data transmission bottlenecks for UAV operators in disaster scenarios, offering an incremental improvement by integrating a semantic extractor into existing machine learning pipelines.
The paper tackles the challenge of limited bandwidth and intermittent connectivity in UAV-assisted disaster recovery by proposing a novel semantic extractor that reduces data transmission volume while maintaining high accuracy in tasks like visual question answering and disaster damage classification, as tested on FloodNet and RescueNet datasets.
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.