Leveraging Collective Knowledge for Forest Fire Classification

This paper presents a novel Fire Classification Multi-Agent (FCMA) framework that utilizes peer-to-peer learning and distributed learning techniques to disseminate knowledge within the agent community. Furthermore, we define and introduce the architecture of a Deep Neural Network (DNN) agent, which can infinitely interact with other DNN agents and the external environment upon deployment. The FCMA framework is suitable for natural disaster management systems where multiple agents are required to run autonomously and foster the community’s knowledge. The FCMA provides two options for knowledge transfer, a peer-to-peer and a federated one. The experimental results display the effective knowledge transfer using both options and also compare the two options with each other in a forest fire classification setting.