Building wildfire intelligence at the edge: bridging the gap from development to deployment

The increased frequency, intensity, and severity of wildfire events in several regions across the world has highlighted several disaster response infrastructure hindrances that call for enhanced intelligence gathering pipelines. In this context, the interest in the use of unmanned aerial vehicles for surveillance and active fire monitoring has been growing in recent years. However, several roadblocks challenge the implementation of these solutions due to their high autonomy requirements and energy-constrained nature. For these reasons, the artificial intelligence development focus on large models hampers the development of models suitable for deployment onboard these platforms. In that sense, while artificial intelligence approaches can be an enabling technology that can effectively scale real-time monitoring services and optimize emergency response resources, the design of these systems imposes: (i) data requirements, (ii) computing constraints and (iii) communications limitations. Here, we propose a decentralized approach, reflecting upon these three vectors.

Data-driven artificial intelligence is central to both handle multimodal sensor data in real-time and to annotate large amounts of data collected, which are necessary to build robust safety-critical monitoring systems. Nevertheless, these two objectives have distinct implications computation-wise, because the first must happen on-board, whereas the second can leverage higher processing capabilities off-board. While autonomy of robotic platforms drives mission performance, being a key reason for the need for edge computing of onboard sensor data, the communications design is essential to mission endurance as relaying large amounts of data in real-time is unfeasible energy-wise. 

For these reasons, real-time processing and data annotation must be tackled in a complimentary manner, instead of the general practice of only targeting overall accuracy improvement. To build wildfire intelligence at the edge, we propose developments on two tracks of solutions: (i) data annotation and (ii) on the edge deployment. The need for considerable effort in these two avenues stems from both having very distinct development requirements and performance evaluation metrics. On the one hand, improving data annotation capacity is essential to build high quality databases that can provide better sources for machine learning. On the other hand, for on the edge deployment the development architectures need to compromise on robustness and architectural parsimony in order to be efficient for edge processing. Whereas the first objective is driven foremost by accuracy, the second goal must emphasize timeliness.

Acknowledgments
This work was supported by FCT – Fundação para a Ciência e a Tecnologia, I.P., through IDMEC, under project Eye in the Sky, PCIF/SSI/0103/2018, and through IDMEC, under LAETA, project UIDB/50022/2020. M. J. Sousa acknowledges the support from FCT, through the Ph.D. Scholarship SFRH/BD/145559/2019, co-funded by the European Social Fund (ESF).