Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field.

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Significant efforts are currently invested in application integration, to enable business processes of different companies to interact and compose complex multi-party processes. Web service standards, based on WSDL, have been adopted as process-to-process communication paradigms. However, the conceptual modeling of applications using Web services has not yet been addressed. Interaction with Web services is often specified at the level of the source code; thus, Web service interfaces are buried within a programmatic specification.In this paper, we argue that Web services should be considered as first-class citizens in the specification of Web applications. Thus, service-enabled Web applications should benefit from the high-level modeling and automatic code generation techniques that have been long advocated for Web application design and implementation. To this purpose, we extend a declarative model for specifying data-intensive Web applications in two directions: (i) high-level modeling of Web services and their interactions with the Web application using them; (ii) modeling and specification of Web applications implementing new, complex Web services.Our approach is fully implemented within a CASE tool allowing the high-level modeling and automatic deployment of service-enabled Web applications.2

This paper addresses conceptual modeling and automatic code generation for Rich Internet Applications, a variant of Web-based systems bridging desktop and thin-client Web interfaces. We show how classical Web modeling concepts are not enough to capture the specificity of RIAs, extend an existing Web modeling language, and provide an implementation of a CASE tool for visual modeling and code generation from RIA-aware specifications. Experimentation of the proposed approach in real-world scenarios is also reported.

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