Earth observing system. Data and information system. Volume 2A: Report of the EOS Data Panel

The purpose of this report is to provide NASA with a rationale and recommendations for planning, implementing, and operating an Earth Observing System data and information system that can evolve to meet the Earth Observing System's needs in the 1990s. The Earth Observing System (Eos), defined by the Eos Science and Mission Requirements Working Group, consists of a suite of instruments in low Earth orbit acquiring measurements of the Earth's atmosphere, surface, and interior; an information system to support scientific research; and a vigorous program of scientific research, stressing study of global-scale processes that shape and influence the Earth as a system. The Eos data and information system is conceived as a complete research information system that would transcend the traditional mission data system, and include additional capabilties such as maintaining long-term, time-series data bases and providing access by Eos researchers to relevant non-Eos data. The Working Group recommends that the Eos data and information system be initiated now, with existing data, and that the system evolve into one that can meet the intensive research and data needs that will exist when Eos spacecraft are returning data in the 1990s

CID Survey Report Satellite Imagery and Associated Services used by the JRC. Current Status and Future Needs

The Agriculture and Fisheries Unit (IPSC) together with the Informatics, Networks and Library Unit (ISD) has performed this inventory called the Community Image Data portal Survey (the CID Survey); 20 Actions from 4 different Institutes (ISD, IPSC, IES, and IHCP) were interviewed. The objectives of the survey were to make an inventory of existing satellite data and future requirements; to obtain an overview of how data is acquired, used and stored; to quantify human and financial resources engaged in this process; to quantify storage needs and to query the staff involved in image acquisition and management on their needs and ideas for improvements in view of defining a single JRC portal through which imaging requests could be addressed. Within the JRC there are (including 2006) more than 700 000 low resolution (LR) and 50 000 medium resolution (MR) images, with time series as far back as 1981 for the LR data. There are more than 10 000 high resolution (HR) images and over 500 000 km2 of very high resolution (VHR) images. For the LR and MR data, cyclic global or continental coverage dominates, while the majority of HR and VHR data is acquired over Europe. The expected data purchase in the future (2007, 2008) known which enables good planning. Most purchases of VHR and HR data are made using the established FCs with common licensing terms. Otherwise multiple types of licensing govern data usage which emphasizes the need for CID to establish adequate means of data access. The total amount of image data stored (2006 inclusive) is 55 TB, with an expected increase of 80% in 2 years. Most of the image data is stored on internal network storage inside the corporate network which implies that the data is accessible from JRC, but difficulties arise when access is to be made by external users via Internet. In principle current storage capacity in the JRC could be enough, but available space is fragmented between Actions which therefore implies that a deficit in storage could arise. One solution to this issue is the sharing of a central storage service. Data reception is dominated by FTP data transfer which therefore requires reliable and fast Internet transfer bandwidth. High total volume for backup requires thorough definition of backup strategy. The user groups at JRC are heterogeneous which places requirements on CID to provide flexible authentication mechanisms. There is a requirement for a detailed analysis of all metadata standards needed for reference in a catalogue. There is a priority interest for such Catalogue Service and also for a centralized storage. The services to implement for data hosted on central storage should be WCS, WMS, file system access. During the analysis of the results mentioned above, some major areas could be identified as a base for common services to be provided to interested Actions, such as: provision of a centralized data storage facility with file serving functionality including authentication service, image catalogue services, data visualization and dissemination services. Specialized data services that require highly customized functionality with respect to certain properties of the different image types will usually remain the sole responsibility of the individual Actions. An orthorectification service for semi-automated orthorectification of HR and VHR data will be provided to certain Actions. At the end of the report some priorities and an implementation schedule for the Community Image Data portal (CID) are given.JRC.G.3-Agricultur

Earth Observing System, volume IIa: Data and Information System, Report of the EOS Data Panel

The purpose of this report is to provide NASA with a rationale and recommendations for planning, implementing, and operating an Earth Observing System data and information system that can evolve to meet the Earth Observing System\u27s needs in the 1990s. The Earth Observing System (EOS), defined by the EOS Science and Mission Requirements Working Group, consists of a suite of instruments in low Earth orbit acquiring measurements of the Earth\u27s atmosphere, surface, and interior; an information system to support scientific research; and a vigorous program of scientific research, stressing study of global-scale processes that shape and influence the Earth as a system. The EOS data and information system is conceived as a complete research information system that would transcend the traditional mission data system, and include additional capabilities such as maintaining long-term, time-series data bases and providing access by EOS researchers to relevant non-EOS data. The Working Group recommends that the EOS data and information system be initiated now, with existing data, and that the system evolve into one that can meet the intensive research and data needs that will exist when EOS spacecraft are returning data in the 1990s

Agglomerative Clustering with Threshold Optimization via Extreme Value Theory

Clustering is a critical part of many tasks and, in most applications, the number of clusters in the data are unknown and must be estimated. This paper presents an Extreme Value Theory-based approach to threshold selection for clustering, proving that the “correct” linkage distances must follow a Weibull distribution for smooth feature spaces. Deep networks and their associated deep features have transformed many aspects of learning, and this paper shows they are consistent with our extreme-linkage theory and provide Unreasonable Clusterability. We show how our novel threshold selection can be applied to both classic agglomerative clustering and the more recent FINCH (First Integer Neighbor Clustering Hierarchy) algorithm. Our evaluation utilizes over a dozen different large-scale vision datasets/subsets, including multiple face-clustering datasets and ImageNet for both in-domain and, more importantly, out-of-domain object clustering. Across multiple deep features clustering tasks with very different characteristics, our novel automated threshold selection performs well, often outperforming state-of-the-art clustering techniques even when they select parameters on the test set

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 299)

This bibliography lists 96 reports, articles, and other documents introduced into the NASA scientific and technical information system in June, 1987

Earthquake record selection for dynamic structural analysis : an application with a web interface

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

This book aims to provide state-of-the-art information on computer architecture and simulation in industry, engineering, and clinical scenarios. Accepted submissions are high in scientific value and provide a significant contribution to computer architecture. Each submission expands upon novel and innovative research where the methods, analysis, and conclusions are robust and of the highest standard. This book is a valuable resource for researchers, students, non-governmental organizations, and key decision-makers involved in earthquake disaster management systems at the national, regional, and local levels

Dimensional affect recognition from HRV: an approach based on supervised SOM and ELM

Dimensional affect recognition is a challenging topic and current techniques do not yet provide the accuracy necessary for HCI applications. In this work we propose two new methods. The first is a novel self-organizing model that learns from similarity between features and affects. This method produces a graphical representation of the multidimensional data which may assist the expert analysis. The second method uses extreme learning machines, an emerging artificial neural network model. Aiming for minimum intrusiveness, we use only the heart rate variability, which can be recorded using a small set of sensors. The methods were validated with two datasets. The first is composed of 16 sessions with different participants and was used to evaluate the models in a classification task. The second one was the publicly available Remote Collaborative and Affective Interaction (RECOLA) dataset, which was used for dimensional affect estimation. The performance evaluation used the kappa score, unweighted average recall and the concordance correlation coefficient. The concordance coefficient on the RECOLA test partition was 0.421 in arousal and 0.321 in valence. Results show that our models outperform state-of-the-art models on the same data and provides new ways to analyze affective states

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations