Spotting Insects from Satellites: Modeling the Presence of Culicoides Imicola Through Deep CNNs
Abstract: Nowadays, Vector-Borne Diseases (VBDs) raise a severe threat for public health, accounting for a considerable amount of human illnesses. Recently, several surveillance plans have been put in place for limiting the spread of such diseases, typically involving on-field measurements. Such a systematic and effective plan still misses, due to the high costs and efforts required for implementing it. Ideally, any attempt in this field should consider the triangle vectors-host-pathogen, which is strictly linked to the environmental and climatic conditions. In this paper, we exploit satellite imagery from Sentinel-2 mission, as we believe they encode the environmental factors responsible for the vector's spread. Our analysis - conducted in a data-driver fashion - couples spectral images with ground-truth information on the abundance of Culicoides imicola. In this respect, we frame our task as a binary classification problem, underpinning Convolutional Neural Networks (CNNs) as being able to learn useful representation from multi-band images. Additionally, we provide a multi-instance variant, aimed at extracting temporal patterns from a short sequence of spectral images. Experiments show promising results, providing the foundations for novel supportive tools, which could depict where surveillance and prevention measures could be prioritized.
Citation:Vincenzi, Stefano; Porrello, Angelo; Buzzega, Pietro; Conte, Annamaria; Ippoliti, Carla; Candeloro, Luca; Di Lorenzo, Alessio; Capobianco Dondona, Andrea; Calderara, Simone "Spotting Insects from Satellites: Modeling the Presence of Culicoides Imicola Through Deep CNNs" 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Sorrento, Italy, Italy, pp. 159 -166 , 26-29 Nov. 2019, 2019 DOI: 10.1109/SITIS.2019.00036
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- DOI: 10.1109/SITIS.2019.00036