Paola Carrara

This paper reviews recent literature on Spatial Data Infrastructure (SDI) for remote sensing, aiming to provide a comprehensive analysis of the state-of-the-art practices, challenges, and future directions. It also tries to find out if current projects and initiatives involving the exploitation of satellite remote sensing are still framed in the SDI concepts.

The current practice of users searching for different types of geo-resources in a geographic area and wishing to identify the most convenient routes for visiting the most relevant ones, requires the iterative formulation of several queries: first to identify the more interesting resources and then to select the best route to visit them. In order to simplify this process, in this paper a novel functionality for a geographic information retrieval (GIR) system is proposed, which retrieves and ranks several routes for visiting a number of relevant georeferenced resources as a result of a single query, named flexible trip-planning query. An original retrieval model is defined to identify the relevant resources and to rank the most convenient routes by taking into account personal user preferences. To this end, a graph-based algorithm is defined, exploiting prioritized aggregation to optimize the routes’ identification and ranking. The proposed algorithm is applied in the proof-of-concept of a Smart cOmmunity-based Geographic infoRmation rEtrievAl SysTem (SO-GREAT) designed to strengthen local communities: it collects and manages open data from regional authorities describing categories of authoritative territorial resources and services, such as schools, hospitals, etc., and from volunteered geographic services (VGSs) created by citizens to offer services in their neighbourhood.

Distinct, alternative forms of geosemantics, whose classification is often ill-defined, emerge in the management of geospatial information. This paper proposes a workflow to identify patterns in the different practices and methods dealing with geoinformation. From a meta-review of the state of the art in geosemantics, this paper first pinpoints “keywords” representing key concepts, challenges, methods, and technologies. Then, we illustrate several case studies, following the categorization into implicit, formal, and powerful (i.e., soft) semantics depending on the kind of their input. Finally, we associate the case studies with the previously identified keywords and compute their similarities in order to ascertain if distinguishing methodologies, techniques, and challenges can be related to the three distinct forms of semantics. The outcomes of the analysis sheds some light on the diverse methods and technologies that are more suited to model and deal with specific forms of geosemantics.

We present an efficient tool for managing, visualizing, analysing, and integrating with other data sources, Earth Observation (EO) data for the analysis of surface deformation phenomena. In particular, we focused on specific EO data that are those obtained by an advanced-processing of Synthetic Aperture Radar (SAR) data for monitoring wide areas of the Earth's surface. More specifically, we refer to the SAR technique called advanced differential interferometric synthetic aperture radar (DInSAR) that have demonstrated its capabilities to detect, to map and to analyse the on-going surface displacement phenomena, both spatially and temporally, with centimetre to millimetre accuracy thanks to the generation of deformation maps and time-series. Currently, the DInSAR scenario is characterized by a huge availability of SAR data acquired during the last 25 years, now with a massive and ever-increasing data flow supplied by the C-band Sentinel-1 (S1) constellation of the European Copernicus program. Considering this big picture, the Spatial Data Infrastructures (SDI) becomes a fundamental tool to implement a framework to handle the informative content of geographic data. Indeed, an SDI represents a collection of technologies, policies, standards, human resources, and related activities permitting the acquisition, processing, distribution, use, maintenance, and preservation of spatial data. We implemented an SDI, extending the functionalities of GeoNode, which is a web-based platform, providing an open-source framework based on the Open Geospatial Consortium (OGC) standards. OGC makes easier interoperability functionalities, that represent an extremely important aspect because allow the data producers to share geospatial information for all types of cooperative processes, avoiding duplication of efforts and costs. Our implemented GeoNode-Based Platform extends a Geographic Information System (GIS) to a web-accessible resource and adapts the SDI tools to DInSAR-related requirements. Our efforts have been dedicated to enabling the GeoNode platform to effectively analyze and visualize the spatial/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. We demonstrate the high performance of implemented GeoNode-Based Platform, showing DInSAR results relevant to the acquisitions of the Sentinel-1 constellation, collected during 2015-2018 over Italy. This work is supported by the 2019-2021 IREA CNR and Italian Civil Protection Department agreement; the H2020 EPOS-SP project (GA 871121); the I-AMICA (PONa3_00363) project; and the IREA-CNR/DGSUNMIG agreement.    

This work presents the development of an efficient tool for managing, visualizing, analysing, and integrating with other data sources, the deformation time-series obtained by applying the advanced differential interferometric synthetic aperture radar (DInSAR) techniques. To implement such a tool we extend the functionalities of GeoNode, which is a web-based platform providing an open source framework based on the Open Geospatial Consortium (OGC) standards, that allows development of Geospatial Information Systems (GIS) and Spatial Data Infrastructures (SDI). In particular, our efforts have been dedicated to enable the GeoNode platform to effectively analyze and visualize the spatio/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. The examples we present, based on Sentinel-1 DInSAR results relevant to Italy, demonstrate the effectiveness of the extended version of the GeoNode platform.

This paper describes an experiment carried out within the project “URBAN GEOmatics for Bulk data Generation, Data Assessment and Technology Awareness (URBAN GEO BIG DATA)” on novel technological solutions for encouraging the use of time series of big geodata by scientists, public administrators, and the citizens of urban areas. In this experiment, we focus on fostering the use of ground deformation time series derived through the Differential Synthetic Aperture Radar Interferometry (DInSAR) measurements in urban areas (i.e., Naples, Milan, and Turin) through visualization. Two visualization applications are developed within the project. First, mean deformation velocity maps are visualized; second, the cumulative deformation time series is animated to enable the comprehension of deformation of eighteen years at a glance. The experiment allows experts and non-experts to discover spatio-temporal patterns of deformation. The paper focuses on the characteristics and implementation details of this experiment. Both visualizations are overlaid on a three-dimensional map, i.e., a virtual globe. We use open standards and free and open source software (FOSS) for Web to enable interoperability, replicability, and reusability.

The adoption of Sensor Web Enablement (SWE) practices by sensor maintainers is hampered by the inherent complexity of the Sensor Model Language (SensorML), its high expressiveness, and the scarce availability of editing tools. To overcome these issues, the Earth Observation (EO) community often recurs to SensorML profiles narrowing the range of admitted metadata structures and value ranges. Unfortunately, profiles frequently fall short of providing usable editing tools and comprehensive validation criteria, particularly for the difficulty of checking value ranges in the multi-tenanted domain of the Web of Data. In this paper, we provide an updated review of current practices, techniques, and tools for editing SensorML in the perspective of profile support and introduce our solution for effective profile definition. Beside allowing for formalization of a broad range of constraints that concur in defining a metadata profile, our proposal closes the gap between profile definition and actual editing of the corresponding metadata by allowing for ex-ante validation of the metadata that is produced. On this basis, we suggest the notion of Semantic Web SensorML profiles, characterized by a new family of constraints involving Semantic Web sources. We also discuss implementation of SensorML profiles with our tool and pinpoint the benefits with respect to the existing ex-post validation facilities provided by schema definition languages.