Applying a-priori knowledge for compressing digital elevation models

Giovanni Guzmán; Rolando Quintero; Miguel Torres; Marco Moreno

doi:10.1007/11892960_74

Applying a-priori knowledge for compressing digital elevation models

Giovanni Guzmán, Rolando Quintero, Miguel Torres, Marco Moreno

Centro de Investigación en Computación (CIC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Up-to-date, some algorithms related to compress digital elevation models (DEMs) or high-resolution DEMs, use wavelet and JPEG-LS encoding approaches to generate compressed DEM files with good compression factor. However, to access the original data (elevation values), it is necessary to decompress whole model. In this paper, we propose an algorithm oriented to compress a digital elevation model, which is based on a sequence of binary images encoded using RLE compression technique, according to a specific height (contour lines). The main goal of our algorithm is to obtain specific parameters of the DEM (altitudes and contours lines) without using a decompression stage, because the information is directly read from the compressed DEM.

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings
Publisher	Springer Verlag
Pages	614-622
Number of pages	9
ISBN (Print)	3540465359, 9783540465355
DOIs	https://doi.org/10.1007/11892960_74
State	Published - 2006
Event	10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006 - Bournemouth, United Kingdom Duration: 9 Oct 2006 → 11 Oct 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4251 LNAI - I
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006
Country/Territory	United Kingdom
City	Bournemouth
Period	9/10/06 → 11/10/06

Access to Document

10.1007/11892960_74

Cite this

Guzmán, G., Quintero, R., Torres, M., & Moreno, M. (2006). Applying a-priori knowledge for compressing digital elevation models. In Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings (pp. 614-622). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4251 LNAI - I). Springer Verlag. https://doi.org/10.1007/11892960_74

Guzmán, Giovanni ; Quintero, Rolando ; Torres, Miguel et al. / Applying a-priori knowledge for compressing digital elevation models. Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings. Springer Verlag, 2006. pp. 614-622 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Up-to-date, some algorithms related to compress digital elevation models (DEMs) or high-resolution DEMs, use wavelet and JPEG-LS encoding approaches to generate compressed DEM files with good compression factor. However, to access the original data (elevation values), it is necessary to decompress whole model. In this paper, we propose an algorithm oriented to compress a digital elevation model, which is based on a sequence of binary images encoded using RLE compression technique, according to a specific height (contour lines). The main goal of our algorithm is to obtain specific parameters of the DEM (altitudes and contours lines) without using a decompression stage, because the information is directly read from the compressed DEM.",

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Guzmán, G , Quintero, R , Torres, M & Moreno, M 2006, Applying a-priori knowledge for compressing digital elevation models. in Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4251 LNAI - I, Springer Verlag, pp. 614-622, 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006, Bournemouth, United Kingdom, 9/10/06. https://doi.org/10.1007/11892960_74

Applying a-priori knowledge for compressing digital elevation models. / Guzmán, Giovanni ; Quintero, Rolando ; Torres, Miguel et al.
Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings. Springer Verlag, 2006. p. 614-622 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4251 LNAI - I).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Up-to-date, some algorithms related to compress digital elevation models (DEMs) or high-resolution DEMs, use wavelet and JPEG-LS encoding approaches to generate compressed DEM files with good compression factor. However, to access the original data (elevation values), it is necessary to decompress whole model. In this paper, we propose an algorithm oriented to compress a digital elevation model, which is based on a sequence of binary images encoded using RLE compression technique, according to a specific height (contour lines). The main goal of our algorithm is to obtain specific parameters of the DEM (altitudes and contours lines) without using a decompression stage, because the information is directly read from the compressed DEM.

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Guzmán G , Quintero R , Torres M , Moreno M. Applying a-priori knowledge for compressing digital elevation models. In Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings. Springer Verlag. 2006. p. 614-622. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11892960_74

Applying a-priori knowledge for compressing digital elevation models

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