2,973 research outputs found
EarthâObservation Data Access: A Knowledge Discovery Concept for Payload Ground Segments
In recent years the ability to store large quantities of Earth Observation (EO) satellite images has greatly surpassed the ability to access and meaningfully extract information from it. The state-of-the-art of operational systems for Remote Sensing data access (in particular for images) allows queries by geographical location, time of acquisition or type of sensor. Nevertheless, this information is often less relevant than the content of the scene (e.g. specific scattering properties, structures, objects, etc.). Moreover, the continuous increase in the size of the archives and in the variety and complexity of EO sensors require new methodologies and tools - based on a shared knowledge - for information mining and management, in support of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In addition, the current Payload Ground Segments (PGS) are mainly designed for Long Term Data Preservation (LTDP), in this article we propose an alternative solution for enhancing the access to the data content. Our solution presents a knowledge discovery concept, whose intention is to implement a communication channel between the PGS (EO data sources) and the end-user who receives the content of the data sources coded in an understandable format associated with semantics and ready for the exploitation. The first implemented concepts were presented in Knowledge driven content based Image Information Mining (KIM) and Geospatial Information Retrieval and Indexing (GeoIRIS) system as examples of data mining systems. Our new concept is developed in a modular system composed of the following components 1) the data model generation implementing methods for extracting relevant descriptors (low-level features) of the sources (EO images), analyzing their metadata in order to complement the information, and combining with vector data sources coming from Geographical Information Systems. 2) A database management system, where the database structure supports the knowledge management, feature computation, and visualization tools because of the modules for analysis, indexing, training and retrieval are resolved into the database. 3) Data mining and knowledge discovery tools allowing the end-user to perform advanced queries and to assign semantic annotations to the image content. The low-level features are complemented with semantic annotations giving meaning to the image information. The semantic description is based on semi-supervised learning methods for spatio-temporal and contextual pattern discovery. 4) Scene understanding counting on annotation tools for helping the user to create scenarios using EO images as for example change detection analysis, etc. 5) Visual data mining providing Human-Machine Interfaces for navigating and browsing the archive using 2D or 3D representation. The visualization techniques perform an interactive loop in order to optimize the visual interaction with huge volumes of data of heterogeneous nature and the end-user
Very-High-Resolution SAR Images and Linked Open Data Analytics Based on Ontologies
In this paper, we deal with the integration of multiple sources of information such as Earth observation (EO) synthetic aperture radar (SAR) images and their metadata, semantic descriptors of the image content, as well as other publicly available geospatial data sources expressed as linked open data for posing complex queries in order to support geospatial data analytics. Our approach lays the foundations for the development of richer tools and applications that focus on EO image analytics using ontologies and linked open data. We introduce a system architecture where a common satellite image product is transformed from its initial format into to actionable intelligence information, which includes image descriptors, metadata, image tiles, and semantic labels resulting in an EO-data model. We also create a SAR image ontology based on our EO-data model and a two-level taxonomy classification scheme of the image content. We demonstrate our approach by linking high-resolution TerraSAR-X images with information from CORINE Land Cover (CLC), Urban Atlas (UA), GeoNames, and OpenStreetMap (OSM), which are represented in the standard triple model of the resource description frameworks (RDFs)
Accelerated Probabilistic Learning Concept for Mining Heterogeneous Earth Observation Images
We present an accelerated probabilistic learning concept and its prototype implementation for mining heterogeneous Earth observation images, e.g., multispectral images, synthetic aperture radar (SAR) images, image time series, or geographical information systems (GIS) maps. The system prototype combines, at pixel level, the unsupervised clustering results of different features, extracted from heterogeneous satellite images and geographical information resources, with user-defined semantic annotations in order to calculate the posterior probabilities that allow the final probabilistic searches. The system is able to learn different semantic labels based on a newly developed Bayesian networks algorithm and allows different probabilistic retrieval methods of all semantically related images with only a few user interactions. The new algorithm reduces the computational cost, overperforming existing conventional systems, under certain conditions, by several orders of magnitude. The achieved speed-up allows the introduction of new feature models improving the learning capabilities of knowledge-driven image information mining systems and opening them to Big Data environment
MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum
In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application
Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images
Scientists in the marine domain process satellite images in order to extract information
that can be used for monitoring, understanding, and forecasting of marine phenomena, such as
turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information
has motivated the adoption of semantically aware strategies on satellite images with different spatiotemporal and spectral characteristics. A big issue of these approaches is the lack of coincidence
between the information that can be extracted from the visual data and the interpretation that the
same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting
the quantitative elements of the Earth Observation satellite images with the qualitative information,
modelling this knowledge in a marine phenomena ontology and developing a question answering
mechanism based on natural language that enables the retrieval of the most appropriate data for each
userâs needs. The main objective of the presented methodology is to realize the content-based search
of Earth Observation images related to the marine application domain on an application-specific
basis that can answer queries such as âFind oil spills that occurred this year in the Adriatic Seaâ
GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data
abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. âBig but valuelessâ has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNNâs model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNNâs model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201
Understanding Heterogeneous EO Datasets: A Framework for Semantic Representations
Earth observation (EO) has become a valuable source of comprehensive, reliable, and persistent
information for a wide number of applications. However, dealing with the complexity of land cover is
sometimes difficult, as the variety of EO sensors reflects in the multitude of details recorded in several types
of image data. Their properties dictate the category and nature of the perceptible land structures. The data
heterogeneity hampers proper understanding, preventing the definition of universal procedures for content
exploitation. The main shortcomings are due to the different human and sensor perception on objects, as well
as to the lack of coincidence between visual elements and similarities obtained by computation. In order to
bridge these sensory and semantic gaps, the paper presents a compound framework for EO image information
extraction. The proposed approach acts like a common ground between the user's understanding, who is
visually shortsighted to the visible domain, and the machines numerical interpretation of a much wider
information. A hierarchical data representation is considered. At first, basic elements are automatically
computed. Then, users can enforce their judgement on the data processing results until semantic structures
are revealed. This procedure completes a user-machine knowledge transfer. The interaction is formalized as
a dialogue, where communication is determined by a set of parameters guiding the computational process
at each level of representation. The purpose is to maintain the data-driven observable connected to the level
of semantics and to human awareness. The proposed concept offers flexibility and interoperability to users,
allowing them to generate those results that best fit their application scenario. The experiments performed on
different satellite images demonstrate the ability to increase the performances in case of semantic annotation
by adjusting a set of parameters to the particularities of the analyzed data
- âŠ