No abstract

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 proved to be beneficial in the context of Land Use/Land Cover (LULC) map generation, unfortunately, machine learning approaches commonly leveraged in remote sensing field fail to take advantage of spatio-temporal dependencies present in such data.Recently, new generation deep learning methods allowed to significantly advance research in this field. These approaches have generally focused on a single rent Neural Networks (RNNs), which model different but complementary information: spatial autocorrelation (CNNs) and temporal dependencies (RNNs).In this work, we propose the first deep learning architecture for the analysis of SITS data, namely DuP LO (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity. Our hypothesis is that, since CNNs and RNNs capture different aspects of the data, a combination of both models would produce a more diverse and complete representation of the information for the underlying land cover classification task. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Gard site in France and the Reunion Island in the Indian Ocean), demonstrate the significance of our proposal.Classification, Sentinel-2 toring and land management planning [1,2,3,4,5,6,7,8,9]. In the context of Land Use/Land Cover (LULC) classification, exploiting SITS can be fruitful to discriminate among classes that exhibit different temporal behaviors [10], i.e., with the respect to the results that can be obtained using a single image. In [7], the authors propose to exploit SITS data to extract homogeneous land units in terms of phenological patterns and, later, for the automatic classification of land units according to their land-cover. The effectiveness of Sentinel-2 SITS to produce land cover maps at country scale has been showed in [8], demonstrating the practical interest of such data source. In [9], the authors combine multi-source optical (Landsat-8) and radar (Sentinel-1) SITS in order to improve land cover maps on the agricultural domain. Another example is supplied in [3] where optical SITS are leveraged to characterize grassland area as proxy indicator for biodiversity, food production, and global carbon cycle.Despite the usefulness of temporal trends that can be derived from remote sensing time series, most of the strategies proposed for SITS analysis tasks [11,12,8,7], directly apply standard machine learning approaches (i.e. Random Forest, SVM) on the stacked images, thus ignoring any temporal dependencies that may be discovered in the data. Indeed, such algorithms make the assumption that the infor...

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 mediasharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagging and media sharing. The network of social interactions among users models various dynamics including follower/followee relations and users' communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can "like" and comment each piece of information on the platform. In this work we investigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous interactions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they devote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users' interactions in online environments and how collective trends emerge from individuals' topical interests.

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 fields. In this paper, we take a first step towards the formal specification and analysis of lurking in social networks. We address the new problem of lurker ranking and propose the first centrality methods specifically conceived for ranking lurkers in social networks. Our approach utilizes only the network topology without probing into text contents or user relationships related to media. Using Twitter, Flickr, FriendFeed and GooglePlus as cases in point, our methods' performance was evaluated against datadriven rankings as well as existing centrality methods, including the classic PageRank and alpha-centrality. Empirical evidence has shown the significance of our lurker ranking approach, and its uniqueness in effectively identifying and ranking lurkers in an online social network.