Towards a compact representation of temporal rasters

Big research efforts have been devoted to efficiently manage spatio-temporal data. However, most works focused on vectorial data, and much less, on raster data. This work presents a new representation for raster data that evolve along time named Temporal k^2 raster. It faces the two main issues that arise when dealing with spatio-temporal data: the space consumption and the query response times. It extends a compact data structure for raster data in order to manage time and thus, it is possible to query it directly in compressed form, instead of the classical approach that requires a complete decompression before any manipulation. In addition, in the same compressed space, the new data structure includes two indexes: a spatial index and an index on the values of the cells, thus becoming a self-index for raster data.Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941. Published in SPIRE 201

Reformulating Space Syntax: The Automatic Definition and Generation of Axial Lines and Axial Maps

Space syntax is a technique for measuring the relative accessibility of different locations in a spatial system which has been loosely partitioned into convex spaces.These spaces are approximated by straight lines, called axial lines, and the topological graph associated with their intersection is used to generate indices of distance, called integration, which are then used as proxies for accessibility. The most controversial problem in applying the technique involves the definition of these lines. There is no unique method for their generation, hence different users generate different sets of lines for the same application. In this paper, we explore this problem, arguing that to make progress, there need to be unambiguous, agreed procedures for generating such maps. The methods we suggest for generating such lines depend on defining viewsheds, called isovists, which can be approximated by their maximum diameters,these lengths being used to form axial maps similar to those used in space syntax. We propose a generic algorithm for sorting isovists according to various measures,approximating them by their diameters and using the axial map as a summary of the extent to which isovists overlap (intersect) and are accessible to one another. We examine the fields created by these viewsheds and the statistical properties of the maps created. We demonstrate our techniques for the small French town of Gassin used originally by Hillier and Hanson (1984) to illustrate the theory, exploring different criteria for sorting isovists, and different axial maps generated by changing the scale of resolution. This paper throws up as many problems as it solves but we believe it points the way to firmer foundations for space syntax

Photometric Mapping with ISOPHOT using the "P32" Astronomical Observation Template

The ``P32'' Astronomical Observation Template (AOT) provided a means to map large areas of sky (up to 45 x 45 arcmin) in the far-infrared (FIR) at high redundancy and with sampling close to the Nyquist limit using the ISOPHOT C100 (3 x 3) and C200 (2 x 2) detector arrays on board the Infrared Space Observatory (ISO). However, the transient response behaviour of the Ga:Ge detectors, if uncorrected, can lead to severe systematic photometric errors and distortions of source morphology on maps. We describe the basic concepts of an algorithm which can successfully correct for transient response artifacts in P32 observations. Examples are given to demonstrate the photometric and imaging performance of ISOPHOT P32 observations of point and extended sources corrected using the algorithm. For extended sources we give the integrated flux densities of the nearby galaxies NGC6946, M51 and M101 and an image of M101 at 100 micron.Comment: 15 pages, 16 figures, published in A&A 410, 107

Frequency-modulated continuous-wave LiDAR compressive depth-mapping

We present an inexpensive architecture for converting a frequency-modulated continuous-wave LiDAR system into a compressive-sensing based depth-mapping camera. Instead of raster scanning to obtain depth-maps, compressive sensing is used to significantly reduce the number of measurements. Ideally, our approach requires two difference detectors. % but can operate with only one at the cost of doubling the number of measurments. Due to the large flux entering the detectors, the signal amplification from heterodyne detection, and the effects of background subtraction from compressive sensing, the system can obtain higher signal-to-noise ratios over detector-array based schemes while scanning a scene faster than is possible through raster-scanning. %Moreover, we show how a single total-variation minimization and two fast least-squares minimizations, instead of a single complex nonlinear minimization, can efficiently recover high-resolution depth-maps with minimal computational overhead. Moreover, by efficiently storing only $2m$ data points from $m<n$ measurements of an $n$ pixel scene, we can easily extract depths by solving only two linear equations with efficient convex-optimization methods

Trends and concerns in digital cartography

CISRG discussion paper ;

Entropy reduction via simplified image contourization

The process of contourization is presented which converts a raster image into a set of plateaux or contours. These contours can be grouped into a hierarchical structure, defining total spatial inclusion, called a contour tree. A contour coder has been developed which fully describes these contours in a compact and efficient manner and is the basis for an image compression method. Simplification of the contour tree has been undertaken by merging contour tree nodes thus lowering the contour tree's entropy. This can be exploited by the contour coder to increase the image compression ratio. By applying general and simple rules derived from physiological experiments on the human vision system, lossy image compression can be achieved which minimizes noticeable artifacts in the simplified image

Space-Efficient Representations of Raster Time Series

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Raster time series, a.k.a. temporal rasters, are collections of rasters covering the same region at consecutive timestamps. These data have been used in many different applications ranging from weather forecast systems to monitoring of forest degradation or soil contamination. Many different sensors are generating this type of data, which makes such analyses possible, but also challenges the technological capacity to store and retrieve the data. In this work, we propose a space-efficient representation of raster time series that is based on Compact Data Structures (CDS). Our method uses a strategy of snapshots and logs to represent the data, in which both components are represented using CDS. We study two variants of this strategy, one with regular sampling and another one based on a heuristic that determines at which timestamps should the snapshots be created to reduce the space redundancy. We perform a comprehensive experimental evaluation using real datasets. The results show that the proposed strategy is competitive in space with alternatives based on pure data compression, while providing much more efficient query times for different types of queries.The data used in this study were acquired as part of the mission of NASA’s Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). Funding: CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01). This work was also supported by Xunta de Galicia/FEDER-UE under Grants [IG240.2020.1.185; IN852A 2018/14]; Ministerio de Ciencia, Innovación y Universidades under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7; PID2019- 105221RB-C41/AEI/10.13039/501100011033]; ANID - Millennium Science Initiative Program - Code ICN17_002; Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo (CYTED) [Grant No. 519RT0579]Xunta de Galicia; ED431G 2019/01Xunta de Galicia; IG240.2020.1.185Xunta de Galicia; IN852A 2018/14Chile. Agencia Nacional de Investigación y Desarrollo; ICN17_00