no code implementations • LREC 2022 • Edmond Menya, Mathieu Roche, Roberto Interdonato, Dickson Owuor
We find these thematic features rich enough to improve epidemiological document classification over a smaller data set than initially used in PADI-Web classifier.
1 code implementation • 30 Mar 2023 • Nejat Arinik, Roberto Interdonato, Mathieu Roche, Maguelonne Teisseire
In the context of Epidemic Intelligence, many Event-Based Surveillance (EBS) systems have been proposed in the literature to promote the early identification and characterization of potential health threats from online sources of any nature.
no code implementations • 30 Apr 2020 • Dino Ienco, Yawogan Jean Eudes Gbodjo, Roberto Interdonato, Raffaele Gaetano
Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information.
1 code implementation • 4 Apr 2020 • Yawogan Jean Eudes Gbodjo, Dino Ienco, Louise Leroux, Roberto Interdonato, Raffaelle Gaetano
Nowadays, there is a general agreement on the need to better characterize agricultural monitoring systems in response to the global changes.
1 code implementation • 20 Nov 2019 • Yawogan Jean Eudes Gbodjo, Dino Ienco, Louise Leroux, Roberto Interdonato, Raffaele Gaetano, Babacar Ndao, Stephane Dupuy
European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at highspatial resolution and high revisit time, respectively, radar and optical imagesthat support a wide range of Earth surface monitoring tasks such as LandUse/Land Cover mapping.
no code implementations • 4 Nov 2019 • Dino Ienco, Roberto Interdonato, Raffaele Gaetano
To the best of our knowledge, despite the great interest in RNN-based classification, this is the first data-aware strategy dealing with the initialization of such models.
no code implementations • 20 Sep 2018 • Roberto Interdonato, Dino Ienco, Raffaele Gaetano, Kenji Ose
In this work, we propose the first deep learning architecture for the analysis of SITS data, namely \method{} (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity.
no code implementations • 20 Apr 2018 • Antonio Caliò, Roberto Interdonato, Chiara Pulice, Andrea Tagarelli
However, little attention has been paid to the fact that the success of an information diffusion campaign might depend not only on the number of the initial influencers to be detected but also on their diversity w. r. t.
Social and Information Networks Physics and Society