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In Sub-Saharan Africa, smallholder farms play a key role in agriculture, occupying most of the agricultural land. Design policies for increasing smallholder productivity remains a safe way to establish sustainable food systems and boost local economies. However, efforts are still needed in order to achieve accurate and timely monitoring in smallholder farming systems. With the advent of modern Earth Observation programmes such as the Sentinel satellites, which provide quasi-synchronous and high-resolution multi-source information over any area of the continental surfaces, new opportunities are opened up to accurately map crop yields in smallholder farming systems. This study intends to estimate and forecast millet yields in central Senegal, making the use of multi-source (synthetic-aperture radar (SAR) and optical) image time series and state-of-the-art machine learning models. A Random Forest (RF) model explained up to 50% of the millet yield variability, while deep learning models such as Convolutional Neural Network (CNN) showed promise results but performed lower. We also found that the concatenation of SAR polarizations and vegetation indices improved our crop yield modelling, but such improvement was tightly related to the modelling approach, namely RF and CNN. Using RF to forecast millet yields, we achieved stable and satisfactory accuracy 2 weeks before the harvest period.

International audience; This letter proposes a deep learning model to deal with the spatial transfer challenge for the mapping of irrigated areas through the analysis of Sentinel-1 data. First, a convolutional neural network (CNN) model called "Teacher Model" is trained on a source geographical area characterized by a huge volume of samples. Then, this model is transferred from the source area to a target area characterized by a limited number of samples. The transfer learning framework is based on a distill and refine strategy in which the teacher model is firstly distilled into a student model and, successively, refined by data samples coming from the target geographical area. The proposed strategy is compared to different approaches including a random forest (RF) classifier trained on the target dataset, a CNN trained on the source dataset and directly applied on the target area as well as several CNN classifiers trained on the target dataset. The evaluation of the performed transfer strategy shows that the "distill and refine" framework obtains the best performance compared to other competing approaches. The obtained findings represent a first step towards the understanding of the spatial transferability of deep learning models in the Earth Observation domain.

International audience; Nowadays, huge amount of data are being produced by a large and diverse family of sensors (e.g., remote sensors, biochemical sensors, wearable devices). These sensors 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 information is collected often makes the data labeling a difficult task. This results in a low-data regime scenario where only a small set of labeled samples is available and standard supervised learning algorithms cannot be employed. To cope with the task of multi-variate time series classification in low-data regime scenarios, here, we propose a framework that combines convolutional neural networks (CNNs) with self-training (pseudo labeling) in a transductive setting (test data are already available at training time). Our framework, named ResNetIPL, wraps a CNN based classifier into an iterative procedure that, at each step, enlarges the training set with new samples and their associated pseudo labels. An experimental evaluation on several benchmarks, coming from different domains, has demonstrated the value of the proposed approach and, more generally, the ability of the deep learning approaches to effectively deal with scenarios characterized by low-data regimes.

International audience; Nowadays, huge volume of satellite images, via the different Earth Observation missions, are constantly acquired and they constitute a valuable source of information for the analysis of spatio-temporal phenomena. However, it can be challenging to obtain reference data associated to such images to deal with land use or land cover changes as often the nature of the phenomena under study is not known a priori. With the aim to deal with satellite image analysis, considering a real-world scenario where reference data cannot be available, in this paper, we present a novel end-to-end unsupervised approach for change detection and clustering for satellite image time series (SITS). In the proposed framework, we firstly create bi-temporal change masks for every couple of consecutive images using neural network autoencoders. Then, we associate the extracted changes to different spatial objects. The objects sharing the same geographical location are combined in spatio-temporal evolution graphs that are finally clustered accordingly to the type of change process with gated recurrent unit (GRU) autoencoder-based model. The proposed approach was assessed on two real-world SITS data supplying promising results.

The need for reliable and exhaustive data on land use is a major issue in planning policies and monitoring of land take. In this work, we evaluate urban building classification resulting from deep learning based approaches using as input SPOT 6/7 satellite images (at a spatial resolution of 1.5m) and national databases. In addition to assessing the classifier behaviour on urban land cover, the objective here is to compare the deep learning results at both pixel-based and object-based scale to qualify the overall results. Standard evaluation metrics (such as F-score) have shown better scores at the object-based assessment level (median F-score = 0.78) than the pixel ones (median F-score = 0.68). Identifying the impact of object characteristics using the object-based level of analysis has also revealed that beyond a surface area of 100 m2, objects are much better detected (median F-score > 0.91). It is the same with a high urban density (median F-score > 0.97). The accuracy of the intersections evaluated from the Intersection over Union also follows these trends as the entities area increases.

Multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth’s surface. Nonetheless, nonidealities and estimation imperfections between records and investigation models can limit its information extraction ability. Ensemble learning could be used to tackle these issues. Combining the information acquired by multiple weak classifiers can prevent the analysis of large scale heterogeneous datasets from being affected by overfitting and biasing. In this paper, we introduce an enhanced ensemble learning scheme where the information acquired by the weak classifiers is combined to optimize the maximum information extraction for the given system at a decision level. Using an asymptotic information theory-based approach, we define the capacity index associated with the maximum accuracy that can be achieved under optimal conditions for multimodal analysis. By selecting the decisions delivered by the different classifiers according to the capacity optimization, the performance of the ensemble learning scheme will be maximized.

Many real world data can be modeled by a graph with a set of nodes interconnected to each other by multiple relationships. Such a rich graph is called multilayer graph or network. Providing useful visualization tools to support the query process for such graphs is challenging. Although many approaches have addressed the visual query construction, few efforts have been done to provide a contextualized exploration of query results and suggestion strategies to refine the original query. This is due to several issues such as i) the size of the graphs ii) the large number of retrieved results and iii) the way they can be organized to facilitate their exploration. In this article, we present VERTIGo, a novel visual platform to query, explore and support the analysis of large multilayer graphs. VERTIGo provides coordinated views to navigate and explore the large set of retrieved results at different granularity levels. In addition, the proposed system supports the refinement of the query by visual suggestions to guide the user through the exploration process. Two examples and a user study demonstrate how VERTIGo can be used to perform visual analysis (query, exploration, and suggestion) on real world multilayer networks.

International audience; Semi-supervised learning is crucial in many applications where accessing class labels is unaffordable or costly. The most promising approaches are graph-based but they are transductive and they do not provide a generalized model working on inductive scenarios. To address this problem, we propose a generic framework, ESA☆, for inductive semi-supervised learning based on three components: an ensemble of semi-supervised autoencoders providing a new data representation that leverages the knowledge supplied by the reduced amount of available labels; a graph-based step that helps augmenting the training set with pseudo-labeled instances and, finally, a classifier trained with labeled and pseudo-labeled instances. Additionally, we also introduce two variants of our framework adopting different graph-based pseudo-labeling strategies: the first, ESALP, is based on a confidence-aware label propagation algorithm, while the second, ESAGAT, on a graph convolutional attention network. The experimental results show that our framework outperforms state-of-the-art inductive semi-supervised methods.

International audience; Semi-supervised learning is a family of classification methods conceived to reduce the amount of required labeled information in the training phase. Graph-based methods are among the most popular semi-supervised strategies: a nearest neighbor graph is built in such a way that the manifold of the data is captured and the labeled information is propagated to target samples along the structure of the manifold. Research in graph-based semi-supervised learning has mainly focused on two aspects: i) the construction of the k-nearest neighbors graph and/or ii) the propagation algorithm providing the classification. Differently from the previous literature, in this paper we focus on data representation with the aim of incorporating semi-supervision earlier in the process. To this end, we propose an algorithm that learns a new knowledge-aware data embedding via an ensemble of semi-supervised autoencoders to enhance a graph-based semi-supervised classification. The experiments carried out on different classification tasks demonstrate the benefit of our approach.

International audience; Monitoring the ecological status of natural habitats is crucial to the conservation process, as it enables the implementation of efficient conservation policies. Nowadays, it is increasingly possible to automate species identification, given the availability of very large image databases and state-of-the-art computational power which makes the training of automated machine learning-based classification models an increasingly viable tool for monitoring marine habitats. Coralligenous reefs are an underwater habitat of particular importance, found in the Mediterranean. This habitat is of a similar biocomplexity to coral reefs. They have been monitored in French waters since 2010 using manually annotated photo quadrats (RECOR monitoring network). Based on the large database of annotations accumulated therein, we have trained convolutional neural networks to automatically recognise coralligenous species using the data gathered from photo quadrats. Previous studies conducted on similar habitats performed well, but were only able to consider a limited number of classes, resulting in a very coarse description of these often-complex habitats. We therefore designed a custom network based on off-theshelf architectures which is able to discriminate between 61 classes with 72.59% accuracy. Our results showed that confusion errors were for the most part taxonomically coherent, showing accuracy performances of 84.47% when the task was simplified to 15 major categories, thereby outperforming the human accuracy previously recorded in a similar study. In light of this, we built a semi-automated tool to reject unsure results and reduce error risk, for when a higher level of accuracy is required. Finally, we used our model to assess the biodiversity and ecological status of coralligenous reefs with the Coralligenous Assemblage Index (CAI) and the Shannon Index. Our results showed that whilst the prediction of the CAI was only moderately accurate (pearson correlation between observed and predicted CAI = 0.61), the prediction of Shannon Index was more accurate (pearson correlation = 0.74). In conclusion, it will be argued that the approach outlined by this study offers a cost and time-effective tool for the analysis of coralligenous assemblages which is suitable for integration into a large-scale monitoring network of this habitat.

Modern earth observation (EO) systems produce huge volumes of images with the objective to monitor the earth surface. Due to the high revisit time of EO systems, such as Sentinel-2 constellation, satellite image time series (SITS) is continuously produced allowing to improve the monitoring of spatiotemporal phenomena. How to efficiently analyze SITS considering both spectral and spatial information is still an open question in the remote sensing field. To deal with SITS classification, in this letter, we propose a spatio-spectral classification framework that leverages the mathematical morphology to extract spatial characteristics from SITS data and combines them with the already available spectral and temporal information. Experiments carried out on two study sites characterized by different heterogeneous land covers have demonstrated the significance of our proposal and the value to combine spatial as well as spectral information in the context of SITS land cover classification.

[EN] The First International Workshop on Modeling, Learning and Mining for Cross/Multilinguality (MultiLingMine) was held in conjunction with the 2016 European Conference on Information Retrieval (ECIR), in Padua, Italy. This report presents an overview of the motivations and objectives underlying the establishment of this workshop. It also provides a summary of the contributing papers and of the main research topics and trends discussed among the participants., This work is partially funded by research project PON 2007-2013 “BA2Know - Business Analytics to Know”, funded by Italian Ministry of Instruction, University, and Research, and by the research project TIN2015-71147-C2-1-P of the Spanish Ministry of Economy and Competitiveness.

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) from all the different sensors due to: (i) atmospheric conditions and/or (ii) acquisition cost. In this context of data (or modalities) misalignment, only part of the area under consideration could be covered by the different sensors (modalities). Unfortunately, standard machine learning approaches commonly employed in operational Earth monitoring systems require consistency between training and test data (i.e., they need to match the same information schema). Such a constraint limits the use of additional fruitful information, i.e., information coming from a particular sensor that may be available at training but not at test time. Recently, a framework able to manage such information misalignment between training and test information is proposed under the name of Generalized Knowledge Distillation (GKD). With the aim to provide a proof of concept of GKD in the context of multi-source Earth Observation analysis, here we provide a Generalized Knowledge Distillation framework for land use land cover mapping involving radar (Sentinel-1) and optical (Sentinel-2) satellite image time series data (SITS). Considering that part of the optical information may not be available due to bad atmospheric conditions, we make the assumption that radar SITS are always available (at both training and test time) while optical SITS are only accessible when the model is learnt (i.e., it is considered as privileged information). Evaluations are carried out on a real-world study area in the southwest of France, namely Dordogne, considering a mapping task involving seven different land use land cover classes. Experimental results underline how the additional (privileged) information ameliorates the results of the radar based classification with a main gain on the agricultural classes.

International audience; 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), radar and optical (multi-spectral) imagery, respectively, at 10 m spatial resolution with revisit time around 5 days. Such high temporal resolution allows to collect Satellite Image Time Series (SITS) that support a plethora of Earth surface monitoring tasks. How to effectively combine the complementary information provided by such sensors remains an open problem in the remote sensing field. In this work, we propose a deep learning architecture to combine information coming from S1 and S2 time series, namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies in both types of SITS. The proposed architecture is devised to boost the land cover classification task by leveraging two levels of complementarity, i.e., the interplay between radar and optical SITS as well as the synergy between spatial and temporal dependencies. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Koumbia site in Burkina Faso and Reunion Island, a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal.

International audience; A major issue affecting optical imagery is the presence of clouds. The need of cloud-free scenes at specific date is crucial in a number of operational monitoring applications. On the other hand, the cloud-insensitive SAR sensors are a solid asset and they provide orthogonal information with respect to optical satellite, that enable the retrieval of information lost in optical images due to cloud cover. In the context of an increasing availability of both optical and SAR images, thank to the Sentinel constellation, we propose a deep learning method to reconstruct (gap-fill) optical data, polluted by cloud phenomena, exploiting multi-temporal SAR and optical images.

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-SISO [Axe_IRSTEA]TETIS-ATTOS [ADD1_IRSTEA]Dynamiques spatiales d'anthropisation; International audience; 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 type of neural network, i.e., Convolutional Neural Networks (CNNs) or Recurrent 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 \method{} (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 \textit{Gard} site in France and the \textit{Reunion Island} in the Indian Ocean), demonstrate the significance of our proposal.

International audience; The expansion of satellite technologies makes remote sensing data abundantly available. While the access to such data is no longer an issue, the analysis of this kind of data is still challenging and time consuming. In this paper, we present an object-oriented methodology designed to handle multi-annual Satellite Image Time Series (SITS). This method has the objective to automatically analyse a SITS to depict and characterize the dynamic of the areas (the way that the land cover of the areas evolve over time). First, it identifies the spatio-temporal entities (reference objects) to be tracked. Second, the evolution of such entities is described by means of a graph structure and finally it groups together spatio-temporal entities that evolve similarly. The analysis were performed on three study areas to highlight inter (among the study areas) and intra (inside a study area) similarity by following the evolution of the underlying phenomena. The analysis demonstrate the benefits of our methodology. Moreover, we also stress how an expert can exploit the extracted knowledge to pinpoint relevant landscape evolutions in the multi-annual time series and how to make connections among different study areas.

International audience; In most real world scenarios, experts dispose of limited background knowledge that they can exploit for guiding the analysis process. In this context, semi-supervised clustering can be employed to leverage such knowledge and enable the discovery of clusters that meet the analysts’ expectations. To this end, we propose a semi-supervised deep embedding clustering algorithm that exploits triplet constraints as background knowledge within the whole learning process. The latter consists in a two-stage approach where, initially, a low-dimensional data embedding is computed and, successively, cluster assignment is refined via the introduction of an auxiliary target distribution. Our algorithm is evaluated on real-world benchmarks in comparison with state-of-the-art unsupervised and semi-supervised clustering methods. Experimental results highlight the quality of the proposed framework as well as the added value of the new learnt data representation.

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 information provided by radar and optical SITS is still an open challenge. In this context, we propose a new neural architecture for the combination of Sentinel-1 (S1) and Sentinel-2 (S2) imagery at object level, applied to a real-world land cover classification task. Experiments carried out on the Reunion Island, a overseas department of France in the Indian Ocean, demonstrate the significance of our proposal.

[Notes_IRSTEA]19996 [Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-SISO [ADD1_IRSTEA]Dynamique et fonctionnement des écosystèmes; International audience; Describing how communities change over space and time is crucial to better understand and predict the functioning of ecosystems. We propose a new methodological framework, based on network theory and modularity concept, to determine which type of mechanisms (i.e. deterministic versus stochastic processes) has the strongest influence on structuring communities. This framework is based on the computation and comparison of two networks: the co-occurrence (based on species abundances) and the functional networks (based on the species traits values). In this way we can assess whether the species belonging to a given functional group also belong to the same co-occurrence group. We adapted the Dg index of Gauzens et al. (2015) to analyze congruence between both networks. This offers the opportunity to identify which assembly rule(s) play(s) the major role in structuring the community. We illustrate our framework with two datasets corresponding to different faunal groups and ecosystems, and characterized by different scales (spatial and temporal scales). By considering both species abundance and multiple functional traits, our framework improves significantly the ability to discriminate the main assembly rules structuring the communities. This point is critical not only to understand community structuring but also its response to global changes and other disturbances.

International audience; Modern Earth Observation systems provide remote sensing data at different temporal and spatial resolutions. Among all the available spatial mission, today the Sentinel-2 program supplies high temporal (every five days) and high spatial resolution (HSR) (10 m) images that can be useful to monitor land cover dynamics. On the other hand, very HSR (VHSR) imagery is still essential to figure out land cover mapping characterized by fine spatial patterns. Understanding how to jointly leverage these complementary sources in an efficient way when dealing with land cover mapping is a current challenge in remote sensing. With the aim of providing land cover mapping through the fusion of multitemporal HSR and VHSR satellite images, we propose a suitable end-to-end deep learning framework, namely M3Fusion , which is able to simultaneously leverage the temporal knowledge contained in time series data as well as the fine spatial information available in VHSR images. Experiments carried out on the Reunion Island study area confirm the quality of our proposal considering both quantitative and qualitative aspects.

International audience; Many real world data can be represented by a network with a set of nodes linked each other by multiple relations. Such a rich graph is called multilayer graph. In this demo, we present a tool for Visual Querying of Large Multilayer Graphs that allows to visually draw the query, retrieve result patterns and finally navigate and browse the results considering the original multilayer graph database. Our approach does not only provide a graphical user interface for the graph engine but the query processing is fully integrated.

International audience; In most real world clustering scenarios, experts generally dispose of limited background information, but such knowledge is valuable and may guide the analysis process. Semi-supervised clustering can be used to drive the algorithmic process with prior knowledge and to enable the discovery of clusters that meet the analyst's expectations. Usually, in the semi-supervised clustering setting, the background knowledge is converted to some kind of constraint and, successively, metric learning or constrained clustering are adopted to obtain the final data partition. Conversely, we propose a new semi-supervised clustering algorithm that directly exploits prior knowledge, under the form of labeled examples, avoiding the necessity to derive constraints. Our algorithm employs a multiresolution strategy to generate an ensemble of semi-supervised autoencoders that fit the data together with the background knowledge. Successively, the network models are employed to supply a new embedding representation on which clustering is performed. The proposed strategy is evaluated on a set of real-world benchmarks also in comparison with well-known state-of-the-art semi-supervised clustering methods. The experimental results highlight the benefit of directly leveraging the prior knowledge and show the quality of the representation learnt by the multiresolution schema.

International audience; For more than a decade, extracting frequent patterns from single large graphs has been one of the research focuses. However, in this era of data eruption, rich and complex data is being generated at an unprecedented rate. This complex data can be represented as a multigraph structure-a generic and rich graph representation. In this paper, we propose a novel frequent subgraph mining approach MuGraM that can be applied to multigraphs. MuGraM is a generic frequent subgraph mining algorithm that discovers frequent multigraph patterns. MuGraM eciently performs the task of subgraph matching, which is crucial for support measure, and further leverages several optimization techniques for swift discovery of frequent subgraphs. Our experiments reveal two things: MuGraM discovers multigraph patterns, where other existing approaches are unable to do so; MuGraM, when applied to simple graphs, outperforms the state of the art approaches by at least one order of magnitude.

International audience; Mapping winter vegetation quality is a challenging problem in remote sensing. This is due to cloud coverage in winter periods, leading to a more intensive use of radar rather than optical images. The aim of this letter is to provide a better understanding of the capabilities of Sentinel-1 radar images for winter vegetation quality mapping through the use of deep learning techniques. Analysis is carried out on a multitemporal Sentinel-1 data over an area around Charentes-Maritimes, France. This data set was processed in order to produce an intensity radar data stack from October 2016 to February 2017. Two deep recurrent neural network (RNN)-based classifiers were employed. Our work revealed that the results of the proposed RNN models clearly outperformed classical machine learning approaches (support vector machine and random forest).

Land cover is a fundamental variable for regional planning, as well as for the study and understanding of the environment. This work propose a multi-temporal approach relying on a fusion of radar multi-sensor data and information collected by the latest sensor (Sentinel-1) with a view to obtaining better results than traditional image processing techniques. The Danube Delta is the site for this work. The spatial approach relies on new spatial analysis technologies and methodologies: Deep Learning of multi-temporal Sentinel-1. We propose a deep learning network for image classification which exploits the multi-temporal characteristic of Sentinel-1 data. The model we employ is a Gated Recurrent Unit (GRU) Network, a recurrent neural network that explicitly takes into account the time dimension via a gated mechanism to perform the final prediction. The main quality of the GRU network is its ability to consider only the important part of the information coming from the temporal data discarding the irrelevant information via a forgetting mechanism. We propose to use such network structure to classify a series of images Sentinel-1 (20 Sentinel-1 images acquired between 9.10.2014 and 01.04.2016). The results are compared with results of the classification of Random Forest.

International audience; Nowadays, remote sensing technologies produce huge amounts of satellite images that can be helpful to monitor geographical areas over time. A satellite image time series (SITS) usually contains spatio-temporal phenomena that are complex and difficult to understand. Conceiving new data mining tools for SITS analysis is challenging since we need to simultaneously manage the spatial and the temporal dimensions at the same time. In this work, we propose a new clustering framework specifically designed for SITS data. Our method firstly detects spatio-temporal entities, then it characterizes their evolutions by mean of a graph-based representation, and finally it produces clusters of spatio-temporal entities sharing similar temporal behaviors. Unlike previous approaches, which mainly work at pixel-level, our framework exploits a purely object-based representation to perform the clustering task. Object-based analysis involves a segmentation step where segments (objects) are extracted from an image and constitute the element of analysis. We experimentally validate our method on two real world SITS datasets by comparing it with standard techniques employed in remote sensing analysis. We also use a qualitative analysis to highlight the interpretability of the results obtained.

Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to efficiently analyze such kind of information is still an open question in the remote sensing field. Recently, deep learning methods proved suitable to deal with remote sensing data mainly for scene classification (i.e. Convolutional Neural Networks - CNNs - on single images) while only very few studies exist involving temporal deep learning approaches (i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series. In this letter we evaluate the ability of Recurrent Neural Networks, in particular the Long-Short Term Memory (LSTM) model, to perform land cover classification considering multi-temporal spatial data derived from a time series of satellite images. We carried out experiments on two different datasets considering both pixel-based and object-based classification. The obtained results show that Recurrent Neural Networks are competitive compared to state-of-the-art classifiers, and may outperform classical approaches in presence of low represented and/or highly mixed classes. We also show that using the alternative feature representation generated by LSTM can improve the performances of standard classifiers.

International audience; 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, in which multiple types of connectivity might be considered. In this work, we fill this gap in the literature by introducing the first framework for local community detection in multilayer networks (ML-LCD). We formalize the ML-LCD optimization problem and provide three definitions of the associated objective function, which correspond to different ways to incorporate within-layer and across-layer topo-logical features. We also exploit our framework to generate multilayer global community structures. We conduct an extensive experimentation using seven real-world multilayer networks, which also includes comparison with state-of-the-art methods for single-layer local community detection and for multilayer global community detection. Results show the significance of our proposed methods in discovering local communities over multiple layers, and also highlight their ability in producing global community structures that are better in modularity than those produced by native global community detection approaches .

International audience; Enhancing the frequency of satellite acquisitions represents a key issue for Earth Observation community nowadays. Repeated observations are crucial for monitoring purposes, particularly when intra-annual process should be taken into account. Time series of images constitute a valuable source of information in these cases. The goal of this paper is to propose a new methodological framework to automatically detect and extract spatiotemporal information from satellite image time series (SITS).Existing methods dealing with such kind of data are usually classification-oriented and cannot provide information about evolutions and temporal behaviors. In this paper we propose a graph-based strategy that combines object-based image analysis (OBIA) with data mining techniques. Image objects computed at each individual timestamp are connected across the time series and generates a set of evolution graphs. Each evolution graph is associated to a particular area within the study site and stores information about its temporal evolution. Such information can be deeply explored at the evolution graph scale or used to compare the graphs and supply a general picture at the study site scale. We validated our framework on two study sites located in the South of France and involving different types of natural, semi-natural and agricultural areas. The results obtained from a Landsat SITS support the quality of the methodological approach and illustrate how the framework can be employed to extract and characterize spatiotemporal dynamics.

International audience; In this paper we introduce a new approach of semi-supervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationships among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model characterizing the features of the normal instances and then use a set of distance-based techniques for the discrimination between the normal and the anomalous instances. We compare our approach with the state-of-the-art methods for semi-supervised anomaly detection. We empirically show that a specifically designed technique for the management of the categorical data outperforms the general-purpose approaches. We also show that, in contrast with other approaches that are opaque because their decision cannot be easily understood, our proposal produces a discriminative model that can be easily interpreted and used for the exploration of the data.

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 for local community detection based on the multilayer network model. Our approach relies on the maximization of the ratio between the community internal connection density and the external connection density, according to multilayer similarity-based community relations. We also define a biasing scheme that allows the discovery of local communities characterized by different degrees of layer-coverage diversification. Experimental evaluation conducted on real-world multilayer networks has shown the significance of our approach., Accepted at 8th International Conference on Complex Networks (CompleNet'17)

Data produced within marine and terrestrial biodiversity research projects that evaluate and monitor Good Environmental Status, have a high potential for use by stakeholders involved in environmental management. However, environmental data, especially in ecology, are not readily accessible to various users. The specific scientific goals and the logics of project organization and information gathering lead to a decentralized data distribution. In such a heterogeneous system across different organizations and data formats, it is difficult to efficiently harmonize the outputs. Few tools are available to assist. For instance standards and specific protocols can be applied to interconnect databases. Such semantic approaches greatly increase data interoperability. This communication present the recent results and the consortium IndexMEED (Indexing for Mining Ecological and Environmental Data) activity that aims to build new approaches to investigate complex research questions, and support the emergence of new scientific hypotheses based on graph theory Auber et al. 2014). Current developments in data mining based on graphs, as well as the potential for relevant contributions to environmental research, particularly about strategic decision-making, and new ways of organizing data will be presented (David et al. 2015). In particular, the consortium makes decisions on how i) to analyze heterogeneous distributed data spread throughout different databases combining molecular and habitat characteristics data [3], ii) to create matches and incorporate some approximations, iii) to identify statistical relationships between observed data and the emergence of contextual patterns using a calculation library and distributed calculation center at the European level, iv) to encourage openness and sharing data while complying with the general principles of FAIR (Findable, Accessible, Interoperable, Re-usable and citable) in order to enhance data value and their utilization. IndexMEED participants are now exploring the ability of two scientific communities (ecology sensu lato and computer sciences) to work together, using several studies cases. The ECOSCOPE project aims to meet the need to access structured and complementary omics-datasets to better understand biodiversity state and its dynamics. Indeed, the ECOSCOPE case study targets to visualize, through the graph approach, links between datasets and databases from genetics to ecosystems. Another case study, displaying anthropology fossils and omics on the same graph, will also be presented. DEVOTES (DEVelopment Of innovative Tools for understanding marine biodiversity and assessing good Environmental Status) and CIGESMED (Coralligenous based Indicators to evaluate and monitor the "Good Environmental Status" of the MEDiterranean coastal water) European projects, conducted by IMBE, are focused on photo quadrats, cartography and omics data of the marine hard bottom in order to discover context patterns helpful to build decision support system building. Study case "65 Millions d'observateurs" French project is testing AskOmics to provide a graph-based querying interface using RDF (Resource Description Framework) and SPARQL technologies. Scientific questions can be resolved by the new data mining approaches that offer new ways to investigate heterogeneous environmental data with graph mining (Muñoz et al. 2017). The uses of data from biodiversity research demonstrate the prototype functionalities (David et al. 2016) and introduce new perspectives to analyze environmental and societal responses including decision-making at large scale, both at the information system level and the observing system level than at the observed system level.

International audience; Many real world datasets can be represented by graphs with a set of nodes intercon- nected with each other by multiple relations (e.g., social network, RDF graph, biological data). Such a rich graph, called multigraph, is well suited to represent real world scenarios with com- plex interactions. However, performing subgraph query on multigraphs is still an open issue since, unfortunately, all the existing algorithms for subgraph query matching are not able to ad- equately leverage the multiple relationships that exist between the nodes. Motivated by the lack of approaches for sub-multigraph query and stimulated by the increasing number of datasets that can be modelled as multigraphs, in this paper we propose IMQA (Index based Multigraph Query Answering), a novel algorithm to extract all the embeddings of a sub-multigraph query from a single large multigraph. IMQA is composed of two main phases: Firstly, it implements a novel indexing schema for multiple edges, which will help to efficiently retrieve the vertices of the multigraph that match the query vertices. Secondly, it performs an efficient subgraph search to output the entire set of embeddings for the given query. Extensive experiments conducted on real datasets prove the time efficiency as well as the scalability of IMQA.; De nombreuses données réelles peuvent être représentées par un réseau avec un ensemble de nœuds interconnectés via différentes relations (i.e. les réseaux sociaux, les données biologiques, les graphes RDF). Ce type de graphe, appelé multigraphe, est tout à fait adapté à la représentation de scénarios réels contenant des interactions complexes. La recherche de sousmultigraphe dans des multigraphes est un domaine de recherche ouvert et malheureusement les algorithmes existants pour faire de la recherche de sous-graphe ne sont pas adaptés et ne peuvent pas prendre en compte les différentes relations qui peuvent exister entre les nœuds. Motivés par le manque d’approches existantes et par le nombre croissant d’applications qui peuvent être modélisées via des multigraphes, nous proposons dans cet article IMQA un nouvel algorithme pour extraire tous les sous-multigraphes inclus dans un grand multigraphe. IMQA comporte deux étapes principales. Tout d’abord il implémente une nouvelle structure d’indexation pour les relations multiples qui est utilisée pour rechercher efficacement les sommets du multigraphe qui correspondent aux sommets de la requête. Ensuite, il réalise une recherche efficace de Ingénierie des systèmes d’information – no 3/2016, 53-74 54 ISI. Volume 21 – no 3/2016 l’ensemble des sous-multigraphes correspondant à une requête donnée. Les nombreuses expérimentations menées sur des jeux de données réelles ont montré l’efficacité et le passage à l’échelle de IMQA.

International audience; Many real world datasets can be represented by a network with a set of nodes interconnected with each other by multiple relations. Such a rich graph is called a multigraph. Unfortunately, all the existing algorithms for subgraph query matching are not able to adequately leverage multiple relationships that exist between the nodes. In this paper we propose an efficient indexing schema for querying single large multi-graphs, where the indexing schema aptly captures the neighbourhood structure in the data graph. Our proposal SuMGra couples this novel indexing schema with a subgraph search algorithm to quickly traverse though the solution space to enumerate all the matchings. Extensive experiments conducted on real benchmarks prove the time efficiency as well as the scalability of SuMGra.

International audience; Many real world information can be represented by a graph with a set of nodes interconnected with each other by multiple type of relations called edge layers (e.g., social network, biological data). Edge bundling techniques have been proposed to solve cluttering issue for standard graphs while few efforts were done to deal with the similar issue for multilayer graphs. In multilayer graphs scenario, not only the clutter induced by large amount of edges is a problem but also the fact that different type of edges can overlap each other making useless the final visualization. In this paper we introduce a new multilayer graph edge bundling technique that firstly produces a preliminary edge bundling independently of the different edge layers and then deals with the specificity of multilayer graphs where more than one type of edges can be routed on the same bundle. The proposed visualization is tested on a real world case study and the outcomes point out the ability of our proposal to discover patterns present in the data.

International audience; Social media is strongly present in people's everyday life and Twitter is one example that stands out. The data within these types of services can be analyzed in order to discover useful knowledge. One interesting approach is to use data mining techniques to perceive hidden behaviours and patterns. The primary focus of this paper is the identification of patterns of retweets and to understand how information spreads over time in Twitter. The aim of this work lies in the adaptation of the GetMove tool, that is capable of extracting spatio-temporal pattern tra-jectories, and TweeProfiles, that identifies tweet profiles regarding several dimensions: spatial, temporal, social and content. We hope that the more flexible clustering strategy from TweeProfiles will enhance the results extracted by GetMove. We study the application of said mechanism to one case study and developed a visualization tool to interpret the results.

Since the BOSS competition, in 2010, most steganalysis approaches use a learning methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image representation, and use of the Ensemble Classifier (EC) for the learning step. In 2015, Qian et al. have shown that the use of a deep learning approach that jointly learns and computes the features, is very promising for the steganalysis. In this paper, we follow-up the study of Qian et al., and show that, due to intrinsic joint minimization, the results obtained from a Convolutional Neural Network (CNN) or a Fully Connected Neural Network (FNN), if well parameterized, surpass the conventional use of a RM with an EC. First, numerous experiments were conducted in order to find the best " shape " of the CNN. Second, experiments were carried out in the clairvoyant scenario in order to compare the CNN and FNN to an RM with an EC. The results show more than 16% reduction in the classification error with our CNN or FNN. Third, experiments were also performed in a cover-source mismatch setting. The results show that the CNN and FNN are naturally robust to the mismatch problem. In Addition to the experiments, we provide discussions on the internal mechanisms of a CNN, and weave links with some previously stated ideas, in order to understand the impressive results we obtained., IS&T. Media Watermarking, Security, and Forensics, Part of IS&T International Symposium on Electronic Imaging, EI'2016, Feb 2015, San Fransisco, United States

International audience; In this letter, we propose a new active transductive learning (ATL) framework for object-based classification of satellite images. The framework couples graph-based label propagation with active learning (AL) to exploit positive aspects of the two learning settings. The transductive approach considers both labelled and unlabelled image objects to perform its classification as they are all available at training time while the AL strategy smartly guides the construction of the training set employed by the learner. The proposed framework was tested in the context of a land cover classification task using RapidEye optical imagery. A reference land cover map was elaborated over the whole study area in order to get reliable information about the performance of the ATL framework. The experimental evaluation under- lines that, with a reasonable amount of training data, our framework outperforms state of the art classification methods usually employed in the field of remote sensing.

Microblogging is a modern communication paradigm in which users post bits of information, or "memes" as we call them, that are brief text updates or micromedia such as photos, video or audio clips. Once a user post a meme, it become visible to the user community. When a user finds a meme of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough the social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted by their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, using parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.

In common binary classification scenarios, the presence of both positive and negative examples in training data is needed to build an efficient classifier. Unfortunately, in many domains, this requirement is not satisfied and only one class of examples is available. To cope with this setting, classification algorithms have been introduced that learn from Positive and Unlabeled (PU) data. Originally, these approaches were exploited in the context of document classification. Only few works address the PU problem for categorical datasets. Nevertheless, the available algorithms are mainly based on Naive Bayes classifiers. In this work we present a new distance based PU learning approach for categorical data: Pulce. Our framework takes advantage of the intrinsic relationships between attribute values and exceeds the independence assumption made by Naive Bayes. Pulce, in fact, leverages on the statistical properties of the data to learn a distance metric employed during the classification task. We extensively validate our approach over real world datasets and demonstrate that our strategy obtains statistically significant improvements w.r.t. state-of-the-art competitors. HighlightsWe propose a distance learning framework for PU learning with categorical data.Differently from existing methods for categorical data, our method is not based on the independence assumption.Our distance learning approach leverages attribute context.The experiments show statistically significant improvements in terms of prediction rate.The results are stable and robust with respect to parameter variations.

The increasing availability of text information coded in many different languages poses new challenges to modern information retrieval and mining systems in order to discover and exchange knowledge at a larger world-wide scale. The 1st International Workshop on Modeling, Learning and Mining for Cross/Multilinguality (dubbed MultiLingMine 2016) provides a venue to discuss research advances in cross-/multilingual related topics, focusing on new multidisciplinary research questions that have not been deeply investigated so far (e.g., in CLEF and related events relevant to CLIR). This includes theoretical and experimental on-going works about novel representation models, learning algorithms, and knowledge-based methodologies for emerging trends and applications, such as, e.g., cross-view cross-/multilingual information retrieval and document mining, (knowledge-based) translation-independent cross-/multilingual corpora, applications in social network contexts, and more.

Addressing data quality issues in information systems remains a challenging task. Many approaches only tackle this issue at the extract, transform and load steps. Here we define a comprehensive method to gain greater insight into data quality characteristics within data warehouse. Our novel architecture was implemented for an hydroecological case study where massive French watercourse sampling data are collected. The method models and makes effective use of spatial, thematic and temporal accuracy, consistency and completeness for multidimensional data in order to offer analysts a "data quality" oriented framework. The results obtained in experiments carried out on the Saone River dataset demonstrated the relevance of our approach. HighlightsA datawarehouse with integrated data quality dimensions.An hydroecological case study: massive French watercourse sampling data.Spatial, thematic and temporal accuracy, consistency and completeness.A "data quality" oriented framework.

International audience; Recent advances in network science allows the modeling and analysis of complex inter-related entities. These entities often interact with each other in a number of different ways. Simple graphs fail to capture these multiple types of relationships requiring more sophisticated mathematical structures. One such structure is multigraph, where entities (or nodes) can be linked to each other through multiple edges. In this paper we describe a new method to manage multiple types of relationships existing in multigraphs. Our approach is based on the concept of pair of nodes (edges) and, in particular, we study how nodes on different layers interact which each other considering the edges they share. We propose a two level strategy that summarizes global/local multigraph features. The global view helps us to gain knowledge related to the characteristics of layers and how they interact while the local view provides an analysis of individual layers highlighting edge properties such as cluster structure. Our proposal is complementary to standard node-link diagram and it can be coupled with such techniques in order to intelligently explore multigraphs. The proposed visualization is tested on a real world case study and the outcomes point out the ability of our proposal to discover patterns present in the data.

International audience; Gradual patterns highlight covariations of attributes of the form " The more/less X, the more/less Y ". Their usefulness in several applications has recently stimulated the synthesis of several algorithms for their automated discovery from large datasets. However, existing techniques require all the interesting data to be in a single database relation or table. This paper extends the notion of gradual pattern to the case in which the co-variations are possibly expressed between attributes of different database relations. The interestingness measure for this class of " relational gradual patterns " is defined on the basis of both Kendall's τ and gradual supports. Moreover, this paper proposes two algorithms, named τ RGP Miner and gRGP Miner, for the discovery of relational gradual rules. Three pruning strategies to reduce the search space are proposed. The efficiency of the algorithms is empirically validated, and the usefulness of relational gradual patterns is proved on some real-world databases.

International audience; The main use of satellite imagery concerns the process of the spectral and spatial dimensions of the data. However, to extract useful information, the temporal dimension also has to be accounted for which increases the complexity of the problem. For this reason, there is a need for suitable data mining techniques for this source of data. In this work, we developed a data mining methodology to extract multidimensional sequential patterns to characterize temporal behaviors. We then used the extracted multidimensional sequences to build a classifier, and show how the patterns help to distinguish between the classes. We evaluated our technique using a real-world dataset containing information about land use in Mali (West Africa) to automatically recognize if an area is cultivated or not.

High repetitivity remote sensing could substantially improve natural habitats monitoring and mapping in the next years. However, dense time series of satellite images require new processing methodologies. In this paper we proposed an approach which combines Object Based Image Analysis (OBIA) and k-partite graphs for detecting spatiotemporal evolutions in a Mediterranean protected site composed of several types of natural and semi-natural habitats. The method was applied over a recent dataset (SPOT4 Take-5) specially conceived to simulate the acquisition frequency of the future Sentinel-2 satellites. The results indicate our method is capable to synthesize complex spatiotemporal evolutions in a semi-automatic way, therefore offering a new tool to analyze high repetitivity satellite time series.

National audience; Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution. To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream.; Pour mieux analyser et extraire de la connaissance de flots de données, des approches spécifiques ont été proposées ces dernières années. L’un des challenges auquel elles doivent faire face est la détection de changement dans les données. Alors que de plus en plus de données qualitatives sont générées, peu de travaux de recherche se sont intéressés à la détection de changement dans ce contexte et les travaux existants se sont principalement focalisés sur la qualité d’un modèle appris plutôt qu’au réel changement dans les données. Dans cet article nous proposons une nouvelle méthode de détection de changement non supervisée, appelée CDCStream (Change Detection in Categorical Data Streams), adaptée aux flux de données qualitatives.

International audience; Clustering data is challenging especially for two reasons. The dimensionality of the data is often very high which makes the cluster interpretation hard. Moreover, with high-dimensional data the classic metrics fail in identifying the real similarities between objects. The second challenge is the evolving nature of the observed phenomena which makes the datasets accumulating over time. In this paper we show how we propose to solve these problems. To tackle the high-dimensionality problem, we propose to apply a co-clustering approach on the dataset that stores the occurrence of features in the observed objects. Co-clustering computes a partition of objects and a partition of features simultaneously. The novelty of our co-clustering solution is that it arranges the clusters in a hierarchical fashion, and it consists of two hierarchies: one on the objects and one on the features. The two hierarchies are coupled because the clusters at a certain level in one hierarchy are coupled with the clusters at the same level of the other hierarchy and form the co-clusters. Each cluster of one of the two hierarchies thus provides insights on the clusters of the other hierarchy. Another novelty of the proposed solution is that the number of clusters is possibly unlimited. Nevertheless, the produced hierarchies are still compact and therefore more readable because our method allows multiple splits of a cluster at the lower level. As regards the second challenge, the accumulating nature of the data makes the datasets intractably huge over time. In this case, an incremental solution relieves the issue because it partitions the problem. In this paper we introduce an incremental version of our algorithm of hierarchical co-clustering. It starts from an intermediate solution computed on the previous version of the data and it updates the co-clustering results considering only the added block of data. This solution has the merit of speeding up the computation with respect to the original approach that would recompute the result on the overall dataset. In addition, the incremental algorithm guarantees approximately the same answer than the original version, but it saves much computational load. We validate the incremental approach on several high-dimensional datasets and perform an accurate comparison with both the original version of our algorithm and with the state of the art competitors as well. The obtained results open the way to a novel usage of the co-clustering algorithms in which it is advantageous to partition the data into several blocks and process them incrementally thus "incorporating" data gradually into an on-going co-clustering solution

Nowadays, a huge amount of high resolution satellite images are freely available. Such images allow researchers in environmental sciences to study the different natural habitats and farming practices in a remote way. However, satellite images content strongly depends on the season of the acquisition. Due to the periodicity of natural and agricultural dynamics throughout seasons, sequential patterns arise as a new opportunity to model the behaviour of these environments. In this paper, we describe some preliminary results obtained with a new framework for studying spatiotemporal evolutions over natural and agricultural areas using k-partite graphs and sequential patterns extracted from segmented Landsat images.