Roberto Interdonato

Roberto Interdonato
  • PhD
  • Researcher at French Agricultural Research Centre for International Development

About

113
Publications
17,689
Reads
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1,737
Citations
Introduction
I am a Research Scientist at Cirad, UMR TETIS, Montpellier, France. My research interests include the design of data science techniques applied to the analysis of complex networks and the extraction of information from remote sensing data. My thematic interests include the characterization of tropical agricultural landscapes, the production of spatial information for food security and the analysis of the transnational land trade market.
Current institution
French Agricultural Research Centre for International Development
Current position
  • Researcher
Additional affiliations
February 2018 - present
French Agricultural Research Centre for International Development
Position
  • Researcher
September 2017 - January 2018
La Rochelle Université
Position
  • PostDoc Position
January 2017 - April 2017
Uppsala University
Position
  • Researcher

Publications

Publications (113)
Article
Full-text available
Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social interactions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagg...
Conference Paper
The massive presence of silent members in online communities, the so-called lurkers, has long attracted the attention of researchers in social science, cognitive psychology, and computer-human interaction. However, the study of lurking phenomena represents an unexplored opportunity of research in data mining, information retrieval and related field...
Conference Paper
Full-text available
An emerging trend in research on recommender systems is the design of methods capable of recommending packages instead of single items. The problem is challenging due to a variety of critical aspects, including context-based and user-provided constraints for the items constituting a package, but also the high sparsity and limited accessibility of t...
Preprint
Full-text available
The growing number of Earth observation satellites has led to increasingly diverse remote sensing data, with varying spatial, spectral, and temporal configurations. Most existing models rely on fixed input formats and modality-specific encoders, which require retraining when new configurations are introduced, limiting their ability to generalize ac...
Article
Due to its highly contagious nature, Avian Influenza (AI) is considered an animal health emergency affecting commercial sector and wild bird populations. Several genome sequencing databases have been created to help researchers understand how AI viruses evolve, spread, and cause disease. However, for a global epidemic monitoring approach, they need...
Article
Full-text available
Multi-sensor data has become a foundation of Earth Observation (EO) research, offering models with enhanced accuracy via optimal fusion strategies. However, the unavailability of sensor data at the regional or country scale during inference can significantly undermine model performance. The literature explores diverse approaches to increasing model...
Preprint
Full-text available
The Land Matrix initiative (https://landmatrix.org) and its global observatory aim to provide reliable data on large-scale land acquisitions to inform debates and actions in sectors such as agriculture, extraction, or energy in low- and middle-income countries. Although these data are recognized in the academic world, they remain underutilized in p...
Preprint
Full-text available
To address the current crises (climatic, social, economic), the self-sufficiency -- a set of practices that combine energy sobriety, self-production of food and energy, and self-construction - arouses an increasing interest. The CNRS STAY project (Savoirs Techniques pour l'Auto-suffisance, sur YouTube) explores this topic by analyzing techniques sh...
Preprint
Full-text available
Pre-trained vision-language models (VLMs), such as CLIP, demonstrate impressive zero-shot classification capabilities with free-form prompts and even show some generalization in specialized domains. However, their performance on satellite imagery is limited due to the underrepresentation of such data in their training sets, which predominantly cons...
Conference Paper
Full-text available
In an attempt to anticipate Food Security (FS) crises and overcome the limits of existing early warning systems, predictive models can forecast risk indices by combining heterogeneous data. While using different data sources (e.g., satellite imagery, agroclimatic data, food prices) allows to consider various factors that may impact food crises, the...
Article
Full-text available
With real-world network systems typically comprising a large number of interactive components and inherently dynamic, Graph Continual Learning (GCL) has gained increasing popularity in recent years. Furthermore, most applications involve multiple entities and relationships with associated attributes, which has led to widely adopting Heterogeneous I...
Conference Paper
We present EpidBioBERT, a biosurveillance epidemiological document tagger for disease surveillance over PADI-Web system. Our model is trained on PADI-Web corpus which contains news articles on Animal Diseases Outbreak extracted from the web. We train a classifier to discriminate between relevant and irrelevant documents based on their epidemiologic...
Conference Paper
Textual documents such as online news articles have become a key source in epidemiological surveillance such as being used in the detection of new and re-emerging diseases. However, such sources suffer redundancies with the need to automate the process of identifying novel information. In this paper, we propose a framework for learning novel themat...
Preprint
Full-text available
Language models now constitute essential tools for improving efficiency for many professional tasks such as writing, coding, or learning. For this reason, it is imperative to identify inherent biases. In the field of Natural Language Processing, five sources of bias are well-identified: data, annotation, representation, models, and research design....
Article
Event based disease surveillance (EBS) systems are biosurveillance systems that have the ability to detect and alert on (re)-emerging infectious diseases by monitoring acute public or animal health event patterns from sources such as blogs, online news reports and curated expert accounts. These information rich sources, however, are largely unstruc...
Article
Full-text available
Food Security (FS) is a major concern in West Africa, particularly in Burkina Faso, which has been the epicenter of a humanitarian crisis since the beginning of this century. Early warning systems for FS and famines rely mainly on numerical data for their analyses, whereas textual data, which are more complex to process, are rarely used. However, t...
Article
Land is central to addressing global development challenges, and land governance is at the heart of paradigms of democracy, justice, sustainability, and resilience. Despite the thoroughly recognized importance of land as a basis for all human-nature relations, the land sector is challenged by mechanisms of commodification and land control. The wide...
Preprint
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. Each EBS system has its own surveillance definitions and priorities, therefore this makes the task of select...
Article
Full-text available
Background The timely and accurate identification of food insecurity situations represents a challenging issue. Household surveys are routinely used in low-income countries and are an essential tool for obtaining key food security indicators that are used by decision makers to determine the targets of food security interventions. Methodology This...
Article
Full-text available
This paper presents an annotated dataset used in the MOOD Antimicrobial Resistance (AMR) hackathon, hosted in Montpellier, June 2022. The collected data concerns unstructured data from news items, scientific publications and national or international reports, collected from four event-based surveillance (EBS) Systems, i.e. ProMED, PADI-web, HealthM...
Article
Full-text available
The GEOGLAM Crop Monitor for Early Warning is based on the integration of the crop conditions assessments produced by regional systems. Discrepancies between these assessments can occur and are generally attributed to the interpretation of the vegetation and climate data. The premise of this paper is that other sources of discrepancy related to the...
Article
Full-text available
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. Each EBS system has its own surveillance definitions and priorities, therefore this makes the task of select...
Article
Full-text available
Large-scale national and transnational commercial land transactions, or Large-Scale Land Acquisitions (LSLAs), have been gaining a lot of academic attention since the late 2000s and since the reported rush for land, resulting in turn from an increase in demand for arable land. If many data exist to characterize land deals, the analysis of investmen...
Article
Full-text available
Huge amount of data are nowadays produced by a large and disparate family of sensors, which typically measure multiple variables over time. Such rich information can be profitably organized as multivariate time-series. Collect enough labelled samples to set up supervised analysis for such kind of data is challenging while a reasonable assumption is...
Chapter
Food security is a major concern in West Africa, particularly in Burkina Faso, which has been the epicenter of a humanitarian crisis since the beginning of this century. Early warning systems for food insecurity and famines rely mainly on numerical data for their analyses, whereas textual data, which are more complex to process, are rarely used. To...
Article
The Senegalese delta, like many other agricultural territories in the Global South, is experiencing changes in agricultural trajectory. These changes are related to the promotion of competitive and performance-based forms of agriculture. In a context of tense relations between farmers and herders, the quest for equitable access to land, which is a...
Article
Full-text available
A variety of remote sensing applications call for automatic optical classification of satellite images. Recently, satellite missions, such as Sentinel-2, allow us to capture images in real-time of the Earth’s scenario. The classification of this large amount of data requires increasingly precise and fast methods, which must take into account not on...
Article
Full-text available
This dataset is composed by spatial (e.g. location) and thematic (e.g. diseases, symptoms, virus) entities concerning avian influenza in social media (textual) data in English. It was created from three corpora: the first one includes 10 transcriptions of YouTube videos and 70 tweets manually annotated. The second corpus is composed by the same tex...
Article
Full-text available
Purpose Event Based Surveillance (EBS) systems detect and monitor diseases by analysing articles from online newspapers and reports from health organizations (e.g. FAO, OIE, etc.). However, they partially integrate data from social networks, even though these data are present in large quantities on the web. The purpose of this study is to exploit s...
Article
Full-text available
Graph Neural Networks (GNNs) are powerful tools that are nowadays reaching state of the art performances in a plethora of different tasks such as node classification, link prediction and graph classification. A challenging aspect in this context is to redefine basic deep learning operations, such as convolution, on graph-like structures, where node...
Chapter
We present a position paper about our concept for an artificial intelligence (AI) and data streaming platform for the agricultural sector. The goal of our project is to support agroecology in terms of carbon farming and biodiversity protection by providing an AI and data streaming platform called Gaia-AgStream that accelerates the adoption of AI in...
Article
After many years of decline, hunger in Africa is growing again. This represents a global societal issue that all disciplines concerned with data analysis are facing. The rapid and accurate identification of food insecurity situations is a complex challenge. Although a number of food security alert and monitoring systems exist in food insecure count...
Article
A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experime...
Article
The articles in this special section focus on the reloading of feature-rich information networks. The growing availability of multi-facetedrelational data gives rise to unprecedented opportunities for unveiling complex real-world behaviors and phenomena. This also supports the proliferation of complex network models where the expressive power of th...
Article
Full-text available
Satellite image time series (SITS) collected by modern Earth Observation (EO) systems represent a valuable source of information that supports several tasks related to the monitoring of the Earth surface dynamics over large areas. A main challenge is then to design methods able to leverage the complementarity between the temporal dynamics and the s...
Chapter
Recurrent Neural Networks (RNNs) can be seriously impacted by the initial parameters assignment, which may result in poor generalization performances on new unseen data. With the objective to tackle this crucial issue, in the context of RNN based classification, we propose a new supervised layer-wise pretraining strategy to initialize network param...
Article
Full-text available
Land is a scarce resource and its depletion is related to a combination of demographic and economic factors. Hence, the changes in dietary habits and increase in world population that upturn the food demand, are intertwined with a context of increasing oil prices and rise of green capitalism that in turn impacts the demand in biofuel. A visible ind...
Article
Full-text available
The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich image data. One of the main tasks assoc...
Article
Full-text available
European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at high spatial resolution and high revisit time, respectively, radar and optical images that support a wide range of Earth surface monitoring tasks, such as Land Use/Land Cover mapping. A long-standing challenge in the remote sensing community is about how to efficiently explo...
Article
Full-text available
Due to the proliferation of Earth Observation programmes, information at different spatial, spectral and temporal resolution is collected by means of various sensors (optical, radar, hyperspectral, LiDAR, etc.). Despite such abundance of information, it is not always possible to obtain a complete coverage of the same area (especially for large ones...
Chapter
Nowadays, great quantities of data are produced by a large and diverse family of sensors (e.g., remote sensors, biochemical sensors, wearable devices), which typically measure multiple variables over time, resulting in data streams that can be profitably organized as multivariate time-series. In practical scenarios, the speed at which such informat...
Article
Multilayer networks have been widely used to represent and analyze systems of interconnected entities where both the entities and their connections can be of different types. However, real multilayer networks can be difficult to analyze because of irrelevant information, such as layers not related to the objective of the analysis, because of their...
Preprint
Multilayer networks have been widely used to represent and analyze systems of interconnected entities where both the entities and their connections can be of different types. However, real multilayer networks can be difficult to analyze because of irrelevant information, such as layers not related to the objective of the analysis, because of their...
Preprint
Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information. The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data (series of images with high revisit time period on the same geographical area) is opening new opportunities to monitor the different aspects of the...
Preprint
Full-text available
Nowadays, there is a general agreement on the need to better characterize agricultural monitoring systems in response to the global changes. Timely and accurate land use/land cover mapping can support this vision by providing useful information at fine scale. Here, a deep learning approach is proposed to deal with multi-source land cover mapping at...
Conference Paper
Due to the proliferation of Earth Observation programmes, information at different spatial, spectral and temporal resolution is collected by means of various sensors (optical, radar, hyperspectral, LiDAR, etc.). Despite such abundance of information, it is not always possible to obtain a complete coverage of the same area (especially for large ones...
Article
Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5...
Chapter
Full-text available
Large Scale Land Acquisitions (LSLAs) by private companies or states have seen a sudden increase in recent years, mainly due to combined and increasing demands for biofuel (i.e., caused by the increase in oil prices) and food (i.e., caused by the increase in world population and changes in dietary habits). These highly controversial phenomena raise...
Chapter
Identifying food insecurity situations timely and accurately is a complex challenge. To prevent food crisis and design appropriate interventions, several food security warning and monitoring systems are very active in food-insecure countries. However, the limited types of data selected and the limitations of data processing methods used make it dif...
Article
Full-text available
The workshop program of the Association for the Advancement of Artificial Intelligence’s 13th International Conference on Web and Social Media was held at the Bavarian School of Public Policy in Munich, Germany on June 11, 2019. There were five full-day workshops, one half-day workshop, and the annual evening Science Slam in the program. The procee...
Preprint
Full-text available
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. A long-standing challenge in the remote sensingcommunity is about how to efficiently exploit mu...
Preprint
Recurrent Neural Networks (RNNs) can be seriously impacted by the initial parameters assignment, which may result in poor generalization performances on new unseen data. With the objective to tackle this crucial issue, in the context of RNN based classification, we propose a new supervised layer-wise pretraining strategy to initialize network param...
Article
Full-text available
Obtaining relevant timely information during crisis events is a challenging task that can be fundamental to handle the consequences deriving from both unexpected events (e.g., terrorist attacks) and partially predictable ones (i.e., natural disasters). Even though microblogging-based online social networks (e.g., Twitter) have become an attractive...
Preprint
Full-text available
A multiplex network models different modes of interaction among same-type entities. A great deal of attention has been devoted to the community detection problem in multiplex networks, that is, revealing meaningful patterns of node groupings into communities by considering different types of interactions among them. In this article, we provide the...
Article
Full-text available
The huge amount of data currently produced by modern Earth Observation (EO) missions has allowed for the design of advanced machine learning techniques able to support complex Land Use/Land Cover (LULC) mapping tasks. The Copernicus programme developed by the European Space Agency provides, with missions such as Sentinel-1 (S1) and Sentinel-2 (S2),...
Article
Nowadays, the massive and horizontal diffusion of online social networks (OSNs) allows users to uncover different aspects about themselves through the use of multiple online platforms. In this respect, linking the accounts related to the same individual enables a more complete and thorough analysis of online behaviors. Indeed, the complexity of sce...
Article
Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10 m) with high temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the use of SITS has bee...
Article
Full-text available
Abstract The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations, supporting the proliferation of diverse network models such as Attributed Graphs, Heterogeneous Networks, Multilayer Networks, Temporal Networks, Location-aware Networks, Knowledge Networks, Probabilisti...
Preprint
Full-text available
Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring. Many studies have been conducted using one of the two sources, but how to smartly combine the complementary info...
Preprint
Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring. Many studies have been conducted using one of the two sources, but how to smartly combine the complementary info...
Chapter
In this chapter, we discuss computational approaches to identify and rank lurkers in online social networks. We begin with a formal definition of topology-driven lurking and a detailed description of a family of centrality methods specifically conceived for ranking lurkers solely based on network topology, namely LurkerRank. To better model dynamic...
Chapter
Identifying and mining lurkers finds application in a variety of OSNs other than social media platforms. In this chapter, we put evidence on the pervasiveness of the notion of lurking, utilizing collaboration networks and trust networks as two cases in point. As regards collaboration networks, we focus on a parallel between lurkers and vicarious le...
Preprint
Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10m) with high temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the use of SITS has been...
Article
Full-text available
While being long researched in social science and computer–human interaction, lurking behaviors in online social networks (OSNs) have been computationally studied only in recent years. Remarkably, determining lurking behaviors has been modeled as an unsupervised, eigenvector-centrality-based ranking problem, and it has been shown that lurkers can e...
Conference Paper
Full-text available
Attributed network models have seen an increasing success in recent years, thanks to their informative power and to their ability to model complex networked relations that characterize most real-world phenomena. Their use has been attractive to communities in different disciplines such as computer science, physics, social science, as well as in int...
Preprint
Research on influence maximization has often to cope with marketing needs relating to the propagation of information towards specific users. 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 diversi...
Article
Research on influence maximization ofter has to cope with marketing needs relating to the propagation of information towards specific users. 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 divers...
Chapter
This chapter summarizes main literature and relating findings from social science and human-computer interaction research, focusing on: the different interpretations of lurking and related implications, the motivational factors underlying this kind of user behavior, and the main criteria to promote delurking of lurkers.
Chapter
This chapter ends the brief offering a summary of the main topics discussed and providing suggestions for future research.
Chapter
In this chapter, we describe the Lurker Game, i.e., a model for analyzing the transitions from a lurking to a non-lurking (i.e., active) user role, and vice versa, in terms of evolutionary game theory. A study carried out on different complex network models shows how the Lurker Game is suitable to model lurking dynamics, and how the adoption of rew...
Chapter
In this chapter, we discuss main remarks and findings raised from the experimental evaluations of lurker rank methods conducted over several real-world OSNs, such as Twitter, FriendFeed, Flickr, Instagram, and Google+. We discuss how the ranking results produced by LurkerRank are effective in identifying and characterizing users at different grades...
Chapter
Encouraging lurkers to more actively participate in the OSN life, a.k.a. delurking, is desirable in order to make lurkers’ social capital available to other users. In this chapter, we discuss in detail the delurking problem and computational approaches to solve it. We first provide an overview of works focusing on user engagement methodologies to u...
Chapter
The social boundary spanning theory explains how OSN users share and transfer their knowledge through the network. In this chapter, we consider two aspects related to the role of lurkers in boundary spanning contexts. In the first part, we concentrate on the relation between lurkers and OSN communities, discussing how the user’s capability of acros...
Conference Paper
When visiting a touristic venue, building personalized itineraries is often non-trivial, mainly because of the variety of types of points-of-interest (PoIs) that might be considered by an individual. Several online platforms exist to support the tourists by providing them with detailed PoI-related information in a certain area, such as routes, dist...
Article
Full-text available
The problem of local community detection in graphs refers to the identification of a community that is specific to a query node and relies on limited information about the network structure. Existing approaches for this problem are defined to work in dynamic network scenarios, however they are not designed to deal with complex real-world networks,...
Chapter
Lurkers are silent members of a social network (SN) who gain benefit from others’ information without significantly giving back to the community. The study of lurking behaviors in SNs is nonetheless important, since these users acquire knowledge from the community, and as such they can be social capital holders. Within this view, a major goal is to...
Article
Full-text available
The problem of node-centric, or local, community detection in information networks refers to the identification of a community for a given input node, having limited information about the network topology. Existing methods for solving this problem, however, are not conceived to work on complex networks. In this paper, we propose a novel framework f...
Conference Paper
The problem of node-centric, or local, community detection in information networks refers to the identification of a community for a given input node, having limited information about the network topology. Existing methods for solving this problem, however, are not conceived to work on complex networks. In this paper, we propose a novel framework f...
Conference Paper
Given the increasing volume and impact of online social interactions in various aspects of life, inferring how a user should be trusted becomes a matter of crucial importance, which can strongly bias any decision process. Existing trust inference algorithms are based on the propagation and aggregation of trust values. However, trust opinions are su...

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