1,046 research outputs found
Knowledge representation of remote sensing quantitative retrieval models
A large number of quantitative retrieval models have been proposed in recent years, and there is continuous momentum in proposing new ones. Building a model, from design through to implementation stages, involves a process of knowledge collection, organization and transmission. In this paper we introduce the SECI model to manage the conversion of qualitative remote sensing knowledge and propose a mode of knowledge representation on the basis of the ontology for geospatial modeling. We develop a platform based on the above research and demonstrate the efficiency of the knowledge representation mode using this platform
Indoor Mapping and Reconstruction with Mobile Augmented Reality Sensor Systems
Augmented Reality (AR) ermöglicht es, virtuelle, dreidimensionale Inhalte direkt
innerhalb der realen Umgebung darzustellen. Anstatt jedoch beliebige virtuelle
Objekte an einem willkĂĽrlichen Ort anzuzeigen, kann AR Technologie auch genutzt
werden, um Geodaten in situ an jenem Ort darzustellen, auf den sich die Daten
beziehen. Damit eröffnet AR die Möglichkeit, die reale Welt durch virtuelle, ortbezogene
Informationen anzureichern. Im Rahmen der vorliegenen Arbeit wird diese
Spielart von AR als "Fused Reality" definiert und eingehend diskutiert.
Der praktische Mehrwert, den dieses Konzept der Fused Reality bietet, lässt sich
gut am Beispiel seiner Anwendung im Zusammenhang mit digitalen Gebäudemodellen
demonstrieren, wo sich gebäudespezifische Informationen - beispielsweise der
Verlauf von Leitungen und Kabeln innerhalb der Wände - lagegerecht am realen
Objekt darstellen lassen. Um das skizzierte Konzept einer Indoor Fused Reality
Anwendung realisieren zu können, müssen einige grundlegende Bedingungen erfüllt
sein. So kann ein bestimmtes Gebäude nur dann mit ortsbezogenen Informationen
augmentiert werden, wenn von diesem Gebäude ein digitales Modell verfügbar ist.
Zwar werden größere Bauprojekt heutzutage oft unter Zuhilfename von Building
Information Modelling (BIM) geplant und durchgefĂĽhrt, sodass ein digitales Modell
direkt zusammen mit dem realen Gebäude ensteht, jedoch sind im Falle älterer
Bestandsgebäude digitale Modelle meist nicht verfügbar. Ein digitales Modell eines
bestehenden Gebäudes manuell zu erstellen, ist zwar möglich, jedoch mit großem
Aufwand verbunden. Ist ein passendes Gebäudemodell vorhanden, muss ein AR
Gerät außerdem in der Lage sein, die eigene Position und Orientierung im Gebäude
relativ zu diesem Modell bestimmen zu können, um Augmentierungen lagegerecht
anzeigen zu können.
Im Rahmen dieser Arbeit werden diverse Aspekte der angesprochenen Problematik
untersucht und diskutiert. Dabei werden zunächst verschiedene Möglichkeiten
diskutiert, Indoor-Gebäudegeometrie mittels Sensorsystemen zu erfassen. Anschließend
wird eine Untersuchung präsentiert, inwiefern moderne AR Geräte, die
in der Regel ebenfalls ĂĽber eine Vielzahl an Sensoren verfĂĽgen, ebenfalls geeignet
sind, als Indoor-Mapping-Systeme eingesetzt zu werden. Die resultierenden Indoor
Mapping Datensätze können daraufhin genutzt werden, um automatisiert
Gebäudemodelle zu rekonstruieren. Zu diesem Zweck wird ein automatisiertes,
voxel-basiertes Indoor-Rekonstruktionsverfahren vorgestellt. Dieses wird auĂźerdem
auf der Grundlage vierer zu diesem Zweck erfasster Datensätze mit zugehörigen
Referenzdaten quantitativ evaluiert. Desweiteren werden verschiedene
Möglichkeiten diskutiert, mobile AR Geräte innerhalb eines Gebäudes und des zugehörigen
Gebäudemodells zu lokalisieren. In diesem Kontext wird außerdem auch
die Evaluierung einer Marker-basierten Indoor-Lokalisierungsmethode präsentiert.
Abschließend wird zudem ein neuer Ansatz, Indoor-Mapping Datensätze an den
Achsen des Koordinatensystems auszurichten, vorgestellt
An insight in cloud computing solutions for intensive processing of remote sensing data
The investigation of Earth's surface deformation phenomena provides critical insights into several processes of great interest for science and society, especially from the perspective of further understanding the Earth System and the impact of the human activities. Indeed, the study of ground deformation
phenomena can be helpful for the comprehension of the geophysical dynamics dominating natural
hazards such as earthquakes, volcanoes and landslide.
In this context, the microwave space-borne Earth Observation (EO) techniques represent very powerful instruments for the ground deformation estimation. In particular, Small BAseline Subset (SBAS) is regarded as one of the key techniques, for its ability to investigate surface deformation affecting large areas of the Earth with a centimeter to millimeter accuracy in different scenarios (volcanoes, tectonics, landslides, anthropogenic induced land motions). The current Remote Sensing scenario is characterized by the availability of huge archives of radar data that are going to increase with the advent of Sentinel-1 satellites. The effective exploitation of this large amount of data requires both adequate computing resources as well as advanced algorithms able to properly exploit such facilities. In this work we concentrated on the use of the P-SBAS algorithm (a parallel version of SBAS) within HPC infrastructure, to finally investigate the effectiveness of such technologies for EO applications. In particular we demonstrated that the cloud computing solutions represent a valid alternative for scientific application and a promising research scenario, indeed, from all the experiments that we have conducted and from the results obtained performing Parallel Small Baseline Subset (P-SBAS) processing, the cloud technologies and features result to be absolutely competitive in terms of performance with in-house HPC cluster solution
Coastal Eye: Monitoring Coastal Environments Using Lightweight Drones
Monitoring coastal environments is a challenging task. This is because of both the logistical demands involved with in-situ data collection and the dynamic nature of the coastal zone, where multiple processes operate over varying spatial and temporal scales. Remote sensing products derived from spaceborne and airborne platforms have proven highly useful in the monitoring of coastal ecosystems, but often they fail to capture fine scale processes and there remains a lack of cost-effective and flexible methods for coastal monitoring at these scales. Proximal sensing technology such as lightweight drones and kites has greatly improved the ability to capture fine spatial resolution data at user-dictated visit times. These approaches are democratising, allowing researchers and managers to collect data in locations and at defined times themselves. In this thesis I develop our scientific understanding of the application of proximal sensing within coastal environments. The two critical review pieces consolidate disparate information on the application of kites as a proximal sensing platform, and the often overlooked hurdles of conducting drone operations in challenging environments. The empirical work presented then tests the use of this technology in three different coastal environments spanning the land-sea interface. Firstly, I use kite aerial photography and uncertainty-assessed structure-from-motion multi-view stereo (SfM-MVS) processing to track changes in coastal dunes over time. I report that sub-decimetre changes (both erosion and accretion) can be detected with this methodology. Secondly, I used lightweight drones to capture fine spatial resolution optical data of intertidal seagrass meadows. I found that estimations of plant cover were more similar to in-situ measures in sparsely populated than densely populated meadows. Lastly, I developed a novel technique utilising lightweight drones and SfM-MVS to measure benthic structural complexity in tropical coral reefs. I found that structural complexity measures were obtainable from SfM-MVS derived point clouds, but that the technique was influenced by glint type artefacts in the image data. Collectively, this work advances the knowledge of proximal sensing in the coastal zone, identifying both the strengths and weaknesses of its application across several ecosystems.Natural Environment Research Council (NERC
A two-stage framework for designing visual analytics systems to augment organizational analytical processes
A perennially interesting research topic in the field of visual analytics is how to effectively develop systems that support organizational knowledge worker’s decision-making and reasoning processes. The primary objective of a visual analytic system is to facilitate analytical reasoning and discovery of insights through interactive visual interfaces. It also enables the transfer of capability and expertise from where it resides to where it is needed–across individuals, and organizations as necessary.
The problem is, however, most domain analytical practices generally vary from organizations to organizations. This leads to the diversified design of visual analytics systems in incorporating domain analytical processes, making it difficult to generalize the success from one domain to another. Exacerbating this problem is the dearth of general models of analytical workflows available to enable such timely and effective designs.
To alleviate these problems, this dissertation presents a two-stage framework for informing the design of a visual analytics system. This two-stage design framework builds upon and extends current practices pertaining to analytical workflow and focuses, in particular, on investigating its effect on the design of visual analytics systems for organizational environments. It aims to empower organizations with more systematic and purposeful information analyses through modeling the domain users’ reasoning processes.
The first stage in this framework is an Observation and Designing stage,
in which a visual analytic system is designed and implemented to abstract and encapsulate general organizational analytical processes, through extensive collaboration with domain users. The second stage is the User-centric Refinement stage, which aims at interactively enriching and refining the already encapsulated domain analysis process based on understanding user’s intentions through analyzing their task behavior. To implement this framework in the process of designing a visual analytics system, this dissertation proposes four general design recommendations that, when followed, empower such systems to bring the users closer to the center of their analytical processes.
This dissertation makes three primary contributions: first, it presents a general characterization of the analytical workflow in organizational environments. This characterization fills in the blank of the current lack of such an analytical model and further represents a set of domain analytical tasks that are commonly applicable to various organizations. Secondly, this dissertation describes a two-stage framework for facilitating the domain users’ workflows through integrating their analytical models
into interactive visual analytics systems. Finally, this dissertation presents recommendations and suggestions on enriching and refining domain analysis through capturing and analyzing knowledge workers’ analysis processes.
To exemplify the generalizability of these design recommendations, this dissertation presents three visual analytics systems that are developed following the proposed recommendations, including Taste for Xerox Corporation, OpsVis for Microsoft, and IRSV for the U.S. Department of Transportation. All of these systems are deployed to domain knowledge workers and are adopted for their analytical practices. Extensive empirical evaluations are further conducted to demonstrate efficacy of these systems in facilitating domain analytical processes
Integrating case based reasoning and geographic information systems in a planing support system: Çeşme Peninsula study
Thesis (Doctoral)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2009Includes bibliographical references (leaves: 110-121)Text in English; Abstract: Turkish and Englishxii, 140 leavesUrban and regional planning is experiencing fundamental changes on the use of of computer-based models in planning practice and education. However, with this increased use, .Geographic Information Systems. (GIS) or .Computer Aided Design.(CAD) alone cannot serve all of the needs of planning. Computational approaches should be modified to deal better with the imperatives of contemporary planning by using artificial intelligence techniques in city planning process.The main aim of this study is to develop an integrated .Planning Support System. (PSS) tool for supporting the planning process. In this research, .Case Based Reasoning. (CBR) .an artificial intelligence technique- and .Geographic Information Systems. (GIS) .geographic analysis, data management and visualization techniqueare used as a major PSS tools to build a .Case Based System. (CBS) for knowledge representation on an operational study. Other targets of the research are to discuss the benefits of CBR method in city planning domain and to demonstrate the feasibility and usefulness of this technique in a PSS. .Çeşme Peninsula. case study which applied under the desired methodology is presented as an experimental and operational stage of the thesis.This dissertation tried to find out whether an integrated model which employing CBR&GIS could support human decision making in a city planning task. While the CBS model met many of predefined goals of the thesis, both advantages and limitations have been realized from findings when applied to the complex domain such as city planning
Earth Observation Open Science and Innovation
geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc
Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies
The Regional Climate Model Evaluation System (RCMES) is an enabling tool of
the National Aeronautics and Space Administration to support the United
States National Climate Assessment. As a comprehensive system for evaluating
climate models on regional and continental scales using observational
datasets from a variety of sources, RCMES is designed to yield information on
the performance of climate models and guide their improvement. Here, we
present a user-oriented document describing the latest version of RCMES, its
development process, and future plans for improvements. The main objective of
RCMES is to facilitate the climate model evaluation process at regional
scales. RCMES provides a framework for performing systematic evaluations of
climate simulations, such as those from the Coordinated Regional Climate
Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data
products. The main components of RCMES are (1)Â a database of observations
widely used for climate model evaluation, (2)Â various data loaders to import
climate models and observations on local file systems and Earth System Grid
Federation (ESGF) nodes, (3)Â a versatile processor to subset and regrid
the loaded datasets, (4)Â performance metrics designed to assess and quantify
model skill, (5)Â plotting routines to visualize the performance metrics,
(6)Â a toolkit for statistically downscaling climate model simulations, and
(7)Â two installation packages to maximize convenience of users without Python
skills. RCMES website is maintained up to date with a brief explanation of
these components. Although there are other open-source software (OSS)
toolkits that facilitate analysis and evaluation of climate models, there is
a need for climate scientists to participate in the development and
customization of OSS to study regional climate change. To establish
infrastructure and to ensure software sustainability, development of RCMES is
an open, publicly accessible process enabled by leveraging the Apache
Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The
OCW software that powers RCMES includes a Python OSS library for common
climate model evaluation tasks as well as a set of user-friendly interfaces
for quickly configuring a model evaluation task. OCW also allows users to
build their own climate data analysis tools, such as the statistical
downscaling toolkit provided as a part of RCMES.</p
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