1,720,962 research outputs found
Multi-sensor Evolution Analysis: an advanced GIS for interactive time series analysis and modelling based on satellite data
No full textArchives of Earth remote sensing data, acquired from orbiting satellites, contain large amounts of information that can be used both for research activities and decision support. Thematic categorization is one method to extract from satellite data meaningful information that humans can directly comprehend. An interactive system that permits to analyse geo-referenced thematic data and its evolution over time is proposed as a tool to efficiently exploit such vast and growing amount of data. This thesis describes the approach used in building the system, the data processing methodology, details architectural elements and graphical interfaces. Finally, this thesis provides an evaluation of potential uses of the features provided, performance levels and usability of an implementation hosting an archive of 15 years moderate resolution (1 Km, from the ATSR instrument) thematic data
Multi sensor Evolution Analysis (MEA): Land use and land cover analysis applied to (A)ATSR time series.
No full textThe problem of (better) exploiting long-term satellite image databases is not yet resolved. Meanwhile the continuous growth of satellite data is generating an unprecedented increase in data types and volume. All this makes unrealistic to proceed with the current,
mainly manual, image processing. Therefore the upcoming challenge is to find new methods permitting in near real-time to store and access large data volumes and to simplify or even automate the extraction of meaningful information for application domains, such
as Land Use / Land Cover Change (LU/LCC) mapping. In the framework of the ESA Support by Pre-classification to Specific Applications (SPA) project a fully automatic LU/LCC application (initially named (A)ATSR Land Classification System (ALCS)) known
as Multi sensor Evolution Analysis (MEA) system, has been implemented and tested. MEA data store is built using 15 years of ATSR2-AATSR data (C1P 4713,C1P 5016)
Land Classification System (LCS) - A system architecture for geospatial information processing for multi-temporal analysis
No full textASQuLD: an Advanced Semantic Query System for Large Satellite Database
No full textIn the context of a complete increase on Remote Sensing data amount, with improvement of
resolution, spectral capabilities and revisiting time, the upcoming challenge is to find a way to
manage (store, make available) the data information content (meaningful information) among all the
available data sources in near real-time, and thus to reduce screening and calculation times. As an
example, since 1995 the ATSR-2 and AATSR sensors have been collecting more than 20TB of data
stored by ESA into magnetic and optical media while newly collected data is available through an
FTP based rolling archive. Stored Landsat Archives consist of more than 500TB of stored data. No
more than 10% of this data is currently accessed and used.
A challenging application is not only to extract meaningful information from large volume of satellite
data, but also searching the right images to be processed among all scenes contained on a large image
database: advanced and effective large database query systems are application/context dependant,
with specific semantic included into the image database and dedicated user-interfaces.
An online database with preliminary ATSR-2/AATSR classification maps was studied and
implemented in the framework of the “Classification Application-services and Reference Datasets”
(CARD) ESA project. The preliminary classification maps are created using the SOIL MAPPER®
software, a fully automated, multi-sensor, spectral rule-based preliminary classifier of Earth
Observation images that generates fully objective land cover maps where each pixel is associated
with one label out of a discrete set of spectral categories where each spectral category has a semantic
meaning. A dedicated database structure hosting image standard information (acquisition time and
location) and semantic information (number of pixels for each of the land cover classes) was
created. A dedicated query interface was implemented as a web service onto the SSE portal. The
final system allows performing four-dimensional queries on the entire database: geographic (2
dimensional), temporal and semantic (land cover) parameters can be used to search through the
entire database. The data access structure resulted very effective on having the entire (A)ATSR
database on line and browsable.
Based on the wide applicability of the SOIL MAPPPER® software (it can process in the same way
twelve different sensors from low to very-high spatial resolution data), a generalised data access
system named ASQuLD (Advanced Semantic Query System for Large Satellite Database) is being
implemented, to provide a standard infrastructure for advanced database queries based on land cover
types. In the framework of the Support by Pre-classification to specific Applications (SPA) ESA
project the ASQuLD infrastructure is being designed and implemented for a test database of three
years of ALOS-AVNIR-2 data.
ASQuLD, for its characteristic, is the ideal tool to improve the accessibility and usability of large
optical satellite databases, improving the exploitation of data that are normally not used because
difficult to identify
The multi-sensor land classification system (LCS): automatic multitemporal land use classification system for multi-resolution data.
No full textProviding land use/land cover change maps through the use of satellite imagery is very challenging and demanding in terms of
human interaction, mainly because of limited process automation. One main cause is that most of land use/land cover change
applications require multi-temporal acquisitions over the same area, that introduces the need for accurate pre-processing of the
dataset, in both geo-referencing and radiometry. Moreover, single multi-spectral images can be hundred of megabytes in size and
therefore image time series are even more difficult to be handled and processed in real time. The approach here proposed foresees
the use of a robust land cover classification system named SOIL MAPPER® to reduce input data size by assigning a semantic
meaning (in the land cover domain) to each pixel of a single image. Land cover transitions and land use maps can be expressed as
evolutions of land cover classes (features) on temporal domain. This permits to define ‘trajectories’ in the features – time space, that
define specific transition or periodic behaviour. The target system, named Land Classification System, provides fully automatic and
real time land use/land cover change analysis and includes fundamental sub-systems for accurate radiometric calibration, accurate
geo-referencing (with geolocation within the pixel size) and accurate remapping onto an Earth fixed grid. The characteristics of the
selected pre-classification system and Earth fixed grid allow general application across different sensors. Land Classification System
has been prototyped over 15 years of global (A)ATSR data and foresees integration of over 3 years of regional ALOS-AVNIR-2
data
A twelve years ATSR-2/AATSR preliminary classification maps database
No full textSince 1995 the ATSR-2 and AATSR sensors have been collecting more than 20TB of data stored by ESA into magnetic and optical media while newly collected data is available through an FTP based Rolling Archive. This report describes the system architecture, the development status and future activities for an online database
with preliminary ATSR-2/AATSR classification maps produced by the spectral rule based SOIL MAPPER® software that efficiently associates a class / semantic meaning to each pixel, providing high
data compression, enhancing data access and simplifying any following processing
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Multitemporal data management and exploitation infrastructure
No full textThe development of new technologies and tools for as-much-as-possible automatic multi-temporal data analysis has been a goal for most of the institutions that aim at promoting the use of satellite data in different application domains. In the framework of the Support by Pre-classification to specific Applications Project, started in 2008, the European Space Agency has requested the development of a specific platform, named Multi-sensor Evolution Analysis (MEA), with the scope of demonstrating that long term satellite datasets coming from different sensors can be accessed and exploited in almost real time (few seconds) from a web application as user interface. The MEA system has been implemented based on 15 years of global (A)ATSR data (1 km resolution), together with 5 years of regional AVNIR-2 data (10 m resolution), with the final aim of permitting on-the-fly Land Use / Land Cover Change analysis. Moreover, a modified version of MEA has been set-up to permit the multi-temporal analysis of pollution maps coming from satellite observations and ground measurements, demonstrating the generality of the pursued approach. The present work aims at introducing the basis of the MEA system, describing the two implementations for land cover and pollution multi-temporal analysis, including external validation activities being performed for the first application by third parties
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