De Olho na Mata: monitoring Atlantic Forests with drones and few-shot learning - A. A. Pedro; F. Dadrass Javan; S. Georgievska; E. H. P. Barreto; O. Ku; F. de Oliveira; P. D. P. Oliveira; C. Gevaert (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)
The expansion of invasive species is a global challenge that leads to the loss of biodiversity habitat, and there are few tools to control it. In São Paulo, identification of invasive species is done through field inspections, in parts of Conservation Units and parks, making it difficult to map all tree individuals for adequate management and coping strategies. This manuscript presents a workflow that combines Unmanned Aerial Vehicles (UAVs), or drones, with Artificial Intelligence (AI) to accurately map invasive species in the Atlantic Forest. It describes best practices on how to conduct drone flights to map the forests, exponentially expanding the range of identification and efficiency in invasive tree species management. It also presents an AI workflow that uses few-shot learning and Explainable AI techniques (to guarantee transparency and understanding of the decisions made by the algorithms). Preliminary results indicate that the method obtains acceptable results in the range of 70 percent accuracy for Archontophoenix cunninghamiana (popular name: Seafórtia), an invasive Australian palm.