George Garbis

Geographica: A Benchmark for Geospatial RDF Stores (Long Version)

Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently been defined and corresponding geospatial RDF stores have been implemented. However, there is no widely used benchmark for evaluating geospatial RDF stores which takes into account recent advances to the state of the art in this area. In this paper, we develop a benchmark, called Geographica, which uses both real-world and synthetic data to test the offered functionality and the performance of some prominent geospatial RDF stores.

Wildfire monitoring using satellite images, ontologies and linked geospatial data

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation data archives have been constantly increasing in size in the last few years, and have become a valuable source of data for many scientific and application domains. When Earth Observation data is coupled with other data sources many pioneering applications can be developed. In this paper we show how Earth Observation data, ontologies, and linked geospatial data can be combined for the development of a wildfire monitoring service that goes beyond applications currently deployed in various Earth Observation data centers. The service has been developed in the context of European project TELEIOS that faces the challenges of extracting knowledge from Earth Observation data head-on, capturing this knowledge by semantic annotation encoded using Earth Observation ontologies, and combining these annotations with linked geospatial data to allow the development of interesting applications.

Building Virtual Earth Observatories Using Ontologies, Linked Geospatial Data and Knowledge Discovery Algorithms

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, satellite image archives have been constantly increasing in size in the last few years (now reaching petabyte sizes), and have become a valuable source of information for many science and application domains (environment, oceanography, geology, archaeology, security, etc.). TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery of knowledge that can be used in applications. To achieve this, TELEIOS builds on scientific databases, linked geospatial data, ontologies and techniques for discovering knowledge from satellite images and auxiliary data sets. In this paper we outline the vision of TELEIOS (now in its second year), and give details of its original contributions on knowledge discovery from satellite images and auxiliary datasets, ontologies, and linked geospatial data.

Building virtual earth observatories using ontologies and linked geospatial data

Advances in remote sensing technologies have enabled public and commercial organizations to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation (EO) data has been constantly increasing in volume in the last few years, and is currently reaching petabytes in many satellite archives. For example, the multi-mission data archive of the TELEIOS partner German Aerospace Center (DLR) is expected to reach 2PB next year, while ESA estimates that it will be archiving 20PB of data before the year 2020. As the volume of data in satellite archives has been increasing, so have the scientific and commercial applications of EO data. Nevertheless, it is estimated that up to 95% of the data present in existing archives has never been accessed, so the potential for increasing exploitation is very big.

Managing Big, Linked, and Open Earth-Observation Data: Using the TELEIOS\/LEO software stack

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service.

Big, Linked and Open Data: Applications in the German Aerospace Center

Earth Observation satellites acquire huge volumes of high resolution images continuously increasing the size of the archives and the variety of EO products. However, only a small part of this data is exploited. In this paper, we present how we take advantage of the TerraSAR-X images of the German Aerospace Center in order to build applications on top of EO data.

The spatiotemporal RDF store strabon

Strabon is a very scalable and efficient RDF store for storing and querying geospatial data that changes over time. We present the geospatial and temporal features of Strabon and we demonstrate their utilization in the fire monitoring and the burn scar mapping applications of the National Observatory of Athens.

Improving Knowledge Discovery from Synthetic Aperture Radar Images Using the Linked Open Data Cloud and Sextant

In the last few years, thanks to projects like TELEIOS, the linked open data cloud has been rapidly populated with geospatial data some of it describing Earth Observation products (e.g., CORINE Land Cover, Urban Atlas). The abundance of this data can prove very useful to the new missions (e.g., Sentinels) as a means to increase the usability of the millions of images and EO products that are expected to be produced by these missions. In this paper, we explain the relevant opportunities by demonstrating how the process of knowledge discovery from TerraSAR-X images can be improved using linked open data and Sextant, a tool for browsing and exploration of linked geospatial data, as well as the creation of thematic maps.Dimensionality reduction for visualization is widely used in visual data mining where the data is represented by high dimensional features. However, this leads to have an unbalanced and occluded distribution of visual data in display space giving rise to difficulties in browsing images. In this paper, we propose an approach to the visualization of images in a 3D display space in such a way that: (1) images are not occluded and the provided space is used efficiently; (2) similar images are positioned close together. An immersive virtual environment is employed as a 3D display space. Experiments are performed on an optical image dataset represented by color features. A library of dimensionality reduction is employed to reduce the dimensionality to 3D. The results confirm that the proposed technique can be used in immersive visual data mining for exploring and browsing large-scale datasets.In this paper, we evaluate sample selection strategies based on optimum experimental design for SAR image classification. Traditionally, support vector machine active learning is widely used by selecting the samples close to the decision surface. Recently, new methods based on optimum experimental design have been developed. To gain a complete understanding of these selection strategies, a comparative study on three approaches, transductive experimental design, manifold adaptive experimental design and locally linear reconstruction, has been performed for SAR image classification using different features. Among the three approaches,we show that manifold adaptive experimental design performs best and stably in terms of both accuracy and computational complexity.Large volume of detailed features of land covers, provided by High-Resolution Earth Observation (EO) images, has attracted the interests to assess the discovery of these features by Content-Based Image Retrieval systems. In this paper, we perform Latent Dirichlet Allocation (LDA) on the Bag-of-Words (BoW) representation of collections of EO images to discover their high-level features, so-called topics. To assess the discovered topics, the images are represented based on the occurrence of different topics, we name it Bag-of-Topics (BoT). Then, the BoT model is compared to the BoW model of images based on the given human-annotations of the data. In our experiments, we compare the classification accuracy resulted by BoT and BoW representations of two different EO datasets, a Synthetic Aperture Radar (SAR) dataset and a multi-spectral satellite dataset. Moreover, we provide isualizations of feature space for better perceiving the changes in the discovered information by BoT and BoW models. Experimental results demonstrate that the dimensionality of the data can be reduced by BoT representation of images; while it either causes no significant reduction in the classification accuracy or even increase the accuracy by sufficient number of topics.In the context of Earth Observation (EO), image information retrieval systems have gained importance as a way to explore terabytes of archive data. Concurrently, evaluation of these systems becomes a topic. Evaluation has typically been conducted in the form of metrics such as Precision Recall measures, with more recent approaches attempting to include the user in the evaluation process. This paper presents a more user centered evaluation of a CBIR tool in an EO context. The evaluation methodology involved open ended user feedback, which was then inductively categorized, and its distribution and content were analyzed. Results are presented, with conclusions indicating certain aspects of the user experience cannot be obtained from metrics alone, but can be complementary to metrics.This paper presents SAR patch categorization based on feature descriptors within the dual tree complex wavelet transform using non-parametric features, which were estimated for each wavelet based subband, which was additionally transformed using a Fourier transform. Spectral properties of wavelet transform were characterized using thefirst and second moments, Kolmogorov Sinai entropy and coding gainwithin an oriented dual tree complex wavelet transform (2D ODTCWT). A database with 2000 images representing 20 different classes with 100 images per class was used for estimation of classification efficiency. A window size for estimation feature parameters was estimated. A supervised learning stage was implemented with support vector machine using 10 % and 20% of the test images per class. The experimental results showed that the non-parametric features achieved 94.3 % accuracy, when 20 % of database was used for supervised training.This paper presents an application of visual data mining technique to Earth-Observation images for exploring very large image archives. We present a visual data mining workstation solution and create some use cases in order to demonstrate its functionality. This tool allows interactive exploration and analysis of very large, high complexity, and non-visual data sets stored into a database by using human-machine communication. The tool relies on image processing components that transform the image content to primitive feature vectors and a graphical user interface, which allows the exploration of the entire image archive. The use cases are based on Synthetic Aperture Radar images, digital orthophotos and photos in-situ.α-trees provide a hierarchical representation of an image into partitions of regions with increasing heterogeneity. This model, inspired from the single-linkage paradigm, has recently been revisited for grayscale images and has been successfully used in the field of remote sensing. This article shows how this representation can be adapted to more complex data here hyperspectral images, according to different strategies. We know that the measure of distance between two neighbouring pixels is a key element for the quality of the underlying tree, but usual metrics are not satisfying. We show here that a relevant solution to understand hyperspectral data relies on the prior learning of the metric to be used and the exploitation of domain knowledge.The multitude of sensors used to acquire Earth Observation (EO) images have led to the creation of an extremely various collection of data. Along with appropriate methods able to work with great amount of data, informat ion retrieval process requires algorithms to cope with a range of input imagery. Even if the geometry and the manner of creating Synthetic Aperture Radar (SAR) images are totally different than multispectral data, there are attempts of finding a common ground such that optical image indexing algorithms can be applied for SAR data and vice versa. Moreover, new concepts must be defined in order to obtain satisfying results, enabling measurements and comparisons between the extracted features [4]. Regarding this idea, the goal is to develop an application capable to join feature extraction algorithms and classification algorithms . Its success will sustain the integration of a reliable EO data search engine. This paper presents a framework for feature extraction and classification aiming to support EO image annotation. Weber Local Descriptors (WLD), Gabor filter and Support Vector Machine (SVM) are combined in order to define an application to be tested on both SAR and optical data.We introduce a map algebra based on a cochain extension of the Linear Algebraic Representation (LAR), used to efficiently represent and query geometric and physical information through sparse matrix algebra. LAR, based on standard algebraic topology methods, supports all incidence structures, including enumerative (images), decompositive (meshes) and boundary (CAD) representations, is dimension-independent and not restricted to regular complexes. This algebraic representation enjoys a neat mathematical format— being based on chains, the domains of discrete integration, and cochains, the discrete prototype of differential forms, so naturally integrating the geometric shape with the supported physical properties, and provides a mechanism for strongly typed representation of all physical quantities associated with images. It is easy to show that k-cochains form a linear vector space over k-cells, which means that they can used as basic objects in a rich and virtually unlimited calculus of physical properties.In this paper, we present a knowledge-driven content-based information mining system for data fusion in Big Data. The tool combines, at pixel level, the unsupervised clustering results of different number of features, extracted from different image types, with a user given semantic concepts in order to calculate the posterior probability that allows the final search. The system is able to learn different semantic labels based on Bayesian networks and retrieve the related images with only a few user interactions, greatly optimizing the computational costs and over performing existing similar systems in various orders of magnitude.

Operational wildfire monitoring and disaster management support using state-of-the-art EO and Information Technologies

The National Observatory of Athens (NOA) has been established in Greece as a research institute offering, among others, operational services for disaster management of forest wildfires. In this paper the main activities of NOA related to fire monitoring and the Burn Scar Mapping damage assessment services are presented. The current capacities in delivering fire-related products and services are greatly enhanced by the integration of state-of-the-art Information Technologies, which provide the potential of refining the thematic accuracy of our products, allows the connection to other distributed databases for the generation of new and innovative added-value products, and suggest the establishment of an operational system that can be extended to include other applications related to natural disaster management.