The correct functioning of photovoltaic (PV) cells is critical to ensuring the optimal performance of a solar plant. Anomaly detection techniques for PV cells can result in significant cost savings in operation and maintenance (O&M). Recent research has focused on deep learning techniques for automatically detecting anomalies in electroluminescence images. Automated anomaly annotations can improve current O&M methodologies and help develop decision-making systems to extend the life cycle of the PV cells and predict failures. This article addresses the lack of anomaly segmentation annotations in the literature by proposing a combination of state-of-the-art data-driven techniques to create a golden standard benchmark. The proposed method stands out for: 1) its adaptability to new PV cell types; 2) cost-efficient fine-tuning; and 3) leverage public datasets to generate advanced annotations. The methodology has been validated in the annotation of a widely used dataset, obtaining a reduction of the annotation cost by 60%.

Time series alignment methods call for highly expressive, differentiable and invertible warping functions which preserve temporal topology, i.e diffeomorphisms. Diffeomorphic warping functions can be generated from the integration of velocity fields governed by an ordinary differential equation (ODE). Gradient-based optimization frameworks containing diffeomorphic transformations require to calculate derivatives to the differential equation’s solution with respect to the model parameters, i.e. sensitivity analysis. Unfortunately, deep learning frameworks typically lack automatic-differentiation-compatible sensitivity analysis methods; and implicit functions, such as the solution of ODE, require particular care. Current solutions appeal to adjoint sensitivity methods, ad-hoc numerical solvers or ResNet’s Eulerian discretization. In this work, we present a closed-form expression for the ODE solution and its gradient under continuous piecewise-affine (CPA) velocity functions. We present a highly optimized implementation of the results on CPU and GPU. Furthermore, we conduct extensive experiments on several datasets to validate the generalization ability of our model to unseen data for time-series joint alignment. Results show significant improvements both in terms of efficiency and accuracy.

We present a strategy to create an online phenology prediction service for non-technical users based on machine learning. The presented methodology is explained into two interrelated parts. The first part describes the core of the service, the automated machine learning procedure that enables the prediction modeling of the phenology phases evolution. The second part summarizes the architecture of the pipeline of the service (from data acquisition to interactive interface) considered for the proof of concept. Finally, the olive phenology prediction service developed in the DEMETER H2020 European project is presented as a real usage example.

Using on-demand processing pipelines to generate virtual geospatial products is beneficial to optimizing resource management and decreasing processing requirements and data storage space. Additionally, pre-processed products improve data quality for data-driven analytical algorithms, such as machine learning or deep learning models. This paper proposes a method to integrate virtual products based on integrating open-source processing pipelines. In order to validate and evaluate the functioning of this approach, we have integrated it into a geo-imagery management framework based on Open Data Cube (ODC). To validate the methodology, we have performed three experiments developing on-demand processing pipelines using multi-sensor remote sensing data, for instance, Sentinel-1 and Sentinel-2. These pipelines are integrated using open-source processing frameworks.

In recent years, the data science community has pursued excellence and made significant research efforts to develop advanced analytics, focusing on solving technical problems at the expense of organizational and socio-technical challenges. According to previous surveys on the state of data science project management, there is a significant gap between technical and organizational processes. In this article we present new empirical data from a survey to 237 data science professionals on the use of project management methodologies for data science. We provide additional profiling of the survey respondents’ roles and their priorities when executing data science projects. Based on this survey study, the main findings are: (1) Agile data science lifecycle is the most widely used framework, but only 25% of the survey participants state to follow a data science project methodology. (2) The most important success factors are precisely describing stakeholders’ needs, communicating the results to end-users, and team collaboration and coordination. (3) Professionals who adhere to a project methodology place greater emphasis on the project’s potential risks and pitfalls, version control, the deployment pipeline to production, and data security and privacy.

We propose a methodology to manage and process remote sensing and geo-imagery data for non-expert users. The proposed system provides automated data ingestion and manipulation capability for analytical data-driven purposes. In this paper, we describe the technological basis of the proposed method in addition to describing the tool architecture, the inherent data flow, and its operation in a specific use case to provide statistical summaries of Sentinel-2 regions of interest corresponding to the cultivation polygonal areas located in the Basque Country (ES).

Industrial process control systems try to keep an output variable within a given tolerance around a target value. PID control systems have been widely used in industry to control input variables in order to reach this goal. However, this kind of Transfer Function based approach cannot be extended to complex processes where input data might be non-numeric, high dimensional, sparse, etc. In such cases, there is still a need for determining the subspace of input data that produces an output within a given range. This paper presents a non-stochastic approximation to determine input values for a mathematical function or trained regression model given an output range. The proposed method creates a synthetic training data set of input combinations with a class label that indicates whether the output is within the given target range or not. Then, a decision tree classifier is used to determine the subspace of input data of interest. This method is more general than a traditional controller as the target range for the output does not have to be centered around a reference value and it can be applied given a regression model of the output variable, which may have categorical variables as inputs and may be high dimensional, sparse... The proposed method is validated with a proof of concept on a real use case where the quality of a lamination factory is established to identify the suitable subspace of production variable values.

The burgeoning capabilities of Web browsers to exploit full-featured devices can turn the huge pool of social connected users into a powerful network of processing assets. HTML5 and JavaScript stacks support the deployment of social client-side processing infrastructure, while WebGL and WebCL fill the gap to gain full GPU and multi-CPU performance. Mobile Grid and Mobile Cloud Computing solutions leverage smart devices to relieve the processing tasks to be performed by the service infrastructure. Motivated to gain cost-efficiency, a social network service provider can outsource the video analysis to elements of a mobile grid as an infrastructure to complement an elastic cloud service. As long as users access to videos, batch image analysis tasks are dispatched from the server, executed in the background of the client-side hardware, and finally, results are consolidated by the server. This paper presents SaW (Social at Work) to provide a pure Web-based solution as a mobile grid to complement a cloud media service for image analysis on videos.

Most EO image analysis algorithms ignore existing maps for solving ill-posed scene understanding problems and developing inversion procedures, yet new EO data often represent aspects of known objects and events. To solve part of this problem, we introduce a prototype designed to integrate, process and distribute existing heterogeneous local public authority maps with the outputs of automatic reconstruction/interpretation engines using mathematical tools for geoprocessing and operating on satellite images.

The Petabyte-scale data volumes in Earth Observation (EO) archives are not efficiently manageable with serial processes running on large isolated servers. Distributed storage and processing based on `big data' cloud computing frameworks needs to be considered as a part of the solution.

Image mining consists of the procedures that allow to access, search and explore very large databases of data. Institutions like spatial agencies have to manage huge archives of Earth Observation (EO) images and need solutions to make data available to users from both the algorithmic and the infrastructural point of views. On the other side, users would need to explore the variety of images not just based on metadata, like time of acquisition or sensor parameters, but also by getting knowledge of their content. In this contribution, we investigate methodologies for content-based EO image retrieval via example-based queries. In particular, we present a procedure for the indexing of large-scale unstructured archives, built on top of a cluster analytics framework, Apache Spark. The procedure is based on a hierarchical and scalable implementation of a space partitioning algorithm and allows O(log n) response query times. Scalability analyses are conducted on polarimetric data from NASA/JPL archives, by using virtualized computing resources distributed over the Internet. In particular, the effects of the cluster size and of the hardware scale-up are demonstrated. The results also reveal the applicative potential of using on-demand cloud-based resources.

As per the 2013 EOSDIS annual metrics report1, Petabyte-scale Earth Observation (EO) raster data archive volumes are growing at rates of about ten Gigabytes per day while around 95% of their contents have never been accessed by a human observer [1]. Metadata-based search clearly needs to be complemented by semi-automatic raster catalog content mining.

Context categorization is a fundamental pre-requisite for multi-domain multimedia content analysis applications. Most feature extraction methods require prior knowledge to decide if they are suitable for a specific domain and to optimize their input parameters. In this paper, we introduce a new color image context categorization method (DITEC) based on the trace transform. The problem of dimensionality reduction of the obtained trace transform signal is addressed through statistical descriptors of its frequency representation that keep the underlying information. We also analyze the distortions produced by the parameters that determine the sampling of the discrete trace transform. Moreover, Feature Subset Selection (FSS) is applied to both, improve the classification performance and compact the final length of the descriptor that will be provided to the classifier. These extracted features offer a highly discriminant behavior for content categorization without prior knowledge requirements. The method has been experimentally validated through two different datasets.