scholarly journals Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 93 ◽  
Author(s):  
John Truckenbrodt ◽  
Terri Freemantle ◽  
Chris Williams ◽  
Tom Jones ◽  
David Small ◽  
...  
Keyword(s):  
Regional Differences ◽  
Open Data ◽  
Data Cube ◽  
Temporal Data ◽  
Test Bed ◽  
Digital Elevation ◽  
The Alps ◽  
Data Environment ◽  
Spatio Temporal ◽  
Sar Analysis

This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis readiness of different openly available digital elevation models (DEMs) and the capability of the software solutions SNAP and GAMMA in terms of overall usability as well as backscatter data quality. To achieve this, the study builds on the Python library pyroSAR for providing the workflow implementation test bed and provides a Jupyter notebook for transparency and future reproducibility of performed analyses. Two test sites were selected, over the Alps and Fiji, to be able to assess regional differences and support the establishment of the Swiss and Common Sensing Open Data cubes respectively.

Personalized Spatio-Temporal OLAP Queries Suggestion Based on User Behavior and a New Similarity Measure

2019 ◽  
pp. 105-128
Author(s):  
Olfa Layouni ◽  
Jalel Akaichi
Keyword(s):  
Probabilistic Model ◽  
User Behavior ◽  
Search Behavior ◽  
Relevant Information ◽  
Current User ◽  
Data Cube ◽  
Temporal Data ◽  
Query Suggestion ◽  
Spatio Temporal ◽  
User Search Behavior

Spatio-temporal data warehouses store enormous amount of data. They are usually exploited by spatio-temporal OLAP systems to extract relevant information. For extracting interesting information, the current user launches spatio-temporal OLAP (ST-OLAP) queries to navigate within a geographic data cube (Geo-cube). Very often choosing which part of the Geo-cube to navigate further, and thus designing the forthcoming ST-OLAP query, is a difficult task. So, to help the current user refine his queries after launching in the geo-cube his current query, we need a ST-OLAP queries suggestion by exploiting a Geo-cube. However, models that focus on adapting to a specific user can help to improve the probability of the user being satisfied. In this chapter, first, the authors focus on assessing the similarity between spatio-temporal OLAP queries in term of their GeoMDX queries. Then, they propose a personalized query suggestion model based on users' search behavior, where they inject relevance between queries in the current session and current user' search behavior into a basic probabilistic model.

scholarly journals CLUSTERING-BASED APPROACHES TO THE EXPLORATION OF SPATIO-TEMPORAL DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1387-1391 ◽  
Author(s):  
X. Wu ◽  
R. Zurita-Milla ◽  
M.-J. Kraak ◽  
E. Izquierdo-Verdiguier
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Temporal Patterns ◽  
Data Cube ◽  
Data Matrix ◽  
Temporal Data Mining ◽  
Temporal Data ◽  
Complex Patterns ◽  
Spatio Temporal ◽  
Temporal Dimensions

As one spatio-temporal data mining task, clustering helps the exploration of patterns in the data by grouping similar elements together. However, previous studies on spatial or temporal clustering are incapable of analysing complex patterns in spatio-temporal data. For instance, concurrent spatio-temporal patterns in 2D or 3D datasets. In this study we present two clustering algorithms for complex pattern analysis: (1) the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) which enables the concurrent analysis of spatio-temporal patterns in 2D data matrix, and (2) the Bregman cube average tri-clustering algorithm with I-divergence (BCAT_I) which enables the complete partitional analysis in 3D data cube. Here the use of the two clustering algorithms is illustrated by Dutch daily average temperature dataset from 28 weather stations from 1992 to 2011. For BBAC_I, it is applied to the averaged yearly dataset to identify station-year co-clusters which contain similar temperatures along stations and years, thus revealing patterns along both spatial and temporal dimensions. For BCAT_I, it is applied to the temperature dataset organized in a data cube with one spatial (stations) and two nested temporal dimensions (years and days). By partitioning the whole dataset into clusters of stations and years with similar within-year temperature similarity, BCAT_I explores the spatio-temporal patterns of intra-annual variability in the daily temperature dataset. As such, both BBAC_I and BCAT_I algorithms, combined with suitable geovisualization techniques, allow the exploration of complex spatial and temporal patterns, which contributes to a better understanding of complex patterns in spatio-temporal data.

Supporting Users to Find Appropriate Visualizations of Spatio-Temporal Open Data Sets

2020 ◽  
Author(s):  
Jan Schulte ◽  
Laura Helene Zepner ◽  
Stephan Mäs ◽  
Simon Jirka ◽  
Petra Sauer
Keyword(s):  
Open Data ◽  
Data Sets ◽  
Temporal Data ◽  
Public Agencies ◽  
Use Of Data ◽  
Research Activities ◽  
Static Data ◽  
Spatio Temporal ◽  
Set Up ◽  
User Friendly

<p><span>Over the last few years, a broad range of open data portals has been set-up. The aim of these portals is to improve the discoverability of open data resources and to strengthen the re-use of data generated by public agencies as well as research activities.</span></p><p><span>Often, such open data portals offer an immense amount of different types of data that may be relevant for a user. Thus, in order to facilitate the efficient and user-friendly exploration of available data sets, it is essential to visualize the data as quickly and easily as possible. While the visuali</span><span>z</span><span>ation of static data sets is already well covered, selecting appropriate visuali</span><span>z</span><span>ation approaches for potentially highly-dynamic spatio-temporal data sets is often still a challenge.</span></p><p><span>Within our contribution, we will introduce a preliminary study conducted by the mVIZ project which is funded by the German Federal Ministry of Transport and Digital Infrastructure as part of the mFUND programm. This project </span><span>introduces</span> <span>a </span><span>methodology to support the selection and creation of user-friendly visualizations for data discoverable via the open data portals such as the mCLOUD. During this process, specific consideration </span><span>are </span><span>given to properties and metadata of the datasets as input for a decision workflow to suggest appropriate visuali</span><span>z</span><span>ation types. A resulting guideline will describe the methodology and serve as a basis for the conception, extension or improvement of visualization tools or for their further development and integration into open data portals.</span></p><p><span>The project focuses particularly on the creation of an inventory of open spatiotemporal data in open data portals as well as an overview of available visualization and analysis tools, the development of a methodology for selecting appropriate visualizations for the spatio-temporal data, and the development of a demonstrator for supporting the visualization of selected data sets.</span></p>

Spatio-Temporal OLAP Queries Similarity Measure and Algorithm

2019 ◽  
Vol 15 (2) ◽  
pp. 22-41
Author(s):  
Olfa Layouni ◽  
Jalel Akaichi
Keyword(s):  
Similarity Measure ◽  
Temporal Analysis ◽  
Data Cube ◽  
Multidimensional Data ◽  
Temporal Data ◽  
Important Class ◽  
Similarity Assessment ◽  
Spatial Similarity ◽  
Query Recommendation ◽  
Spatio Temporal

Spatio-temporal data warehouses store large volumes of consolidated and historized multidimensional data, to be explored and analyzed by various users in order to make the best decision. A spatio-temporal OLAP user interactively navigates a spatio-temporal data cube (Geo-cube) by launching a sequence of spatio-temporal OLAP queries (GeoMDX queries) in order to analyze the data. One important class of spatio-temporal analysis is computing spatio-temporal queries similarity. In this article, the authors focus on assessing the similarity between spatio-temporal OLAP queries in term of their GeoMDX queries. The problem of measuring spatio-temporal OLAP queries similarities has not been studied so far. Therefore, this article aims at filling this gap by proposing a new similarity measure and its corresponding algorithm. The proposed measure and algorithm can be used either in developing query recommendation, personalization systems or speeding-up query evolution. It takes into account the temporal similarity and the basic components of spatial similarity assessment relationships.

Sar Analysis Ready Data and Tools for the Open Data Cube

2020 ◽  
Author(s):  
Ake Rosenqvist ◽  
Brian D. Killough ◽  
Andrew M. Lubawy ◽  
John C. Rattz
Keyword(s):  
Open Data ◽  
Data Cube ◽  
Sar Analysis

Phänologische Trends bei den Waldbäumen in der Schweiz | Phenological trends regarding the forest trees in Switzerland

2005 ◽  
Vol 156 (6) ◽  
pp. 207-210 ◽  
Author(s):  
Claudio Defila
Keyword(s):  
Regional Differences ◽  
Early Growth ◽  
Horse Chestnut ◽  
Forest Trees ◽  
Spring Season ◽  
Arrival Times ◽  
The Past ◽  
Early Arrival ◽  
Southern Side ◽  
The Alps

Numerous publications are devoted to plant phenological trends of all trees, shrubs and herbs. In this work we focus on trees of the forest. We take into account the spring season (leaf and needle development) as well as the autumn (colour turning and shedding of leaves) for larch, spruce and beech, and,owing to the lack of further autumn phases, the horse chestnut. The proportion of significant trends is variable, depending on the phenological phase. The strongest trend to early arrival in spring was measured for needles of the larch for the period between 1951 and 2000 with over 20 days. The leaves of the horse chestnut show the earliest trend to turn colour in autumn. Beech leaves have also changed colour somewhat earlier over the past 50 years. The trend for shedding leaves, on the other hand, is slightly later. Regional differences were examined for the growth of needles in the larch where the weakest trends towards early growth are found in Canton Jura and the strongest on the southern side of the Alps. The warming of the climate strongly influences phenological arrival times. Trees in the forest react to this to in a similar way to other plants that have been observed (other trees, shrubs and herbs).

Spatial-temporal data collection process models using mobile sinks

2019 ◽  
Vol 942 (12) ◽  
pp. 22-28
Author(s):  
A.V. Materuhin ◽  
V.V. Shakhov ◽  
O.D. Sokolova
Keyword(s):  
Energy Consumption ◽  
Operation Time ◽  
Point Of View ◽  
Process Models ◽  
Temporal Data ◽  
Network Nodes ◽  
Mobile Sinks ◽  
Autonomous Operation ◽  
Data Collection Process ◽  
Spatio Temporal

Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.

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