Spotting Insects from Satellites: Modeling the Presence of Culicoides Imicola Through Deep CNNs
By Stefano Vincenzi, Angelo Porrello, Pietro Buzzega, Annamaria Conte, Carla Ippoliti, L. Candeloro, A. D. Lorenzo, A. C. Dondona, Simone Calderara. 2019
14 citations recorded by the source corpus.
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.
Subfields: Agronomy and Crop Science, Ecology, Evolution, Behavior and Systematics, Infectious Diseases
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In Infectious Diseases
Citations in the corpus
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- The color out of space: learning self-supervised representations for Earth Observation imagery (2020, methodology): “We exploit the normalization technique described in [43], [47] computing the 2nd and 98th percentile TABLE I PERFORMANCE (MAP) ON BIGEARTHNET FOR DIFFERENT STRATEGIES TO VARY THE NUMBER OF TRAINING EXAMPLES.”