Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets

Point cloud datasets provided by LiDAR have become an integral part in many research fields including archaeology, forestry, and ecology. Facilitated by technological advances, the volume of these datasets has steadily increased, with modern airborne laser scanning surveys now providing high-resolut...
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Autor*in:	C. Meijer [verfasserIn] M.W. Grootes [verfasserIn] Z. Koma [verfasserIn] Y. Dzigan [verfasserIn] R. Gonçalves [verfasserIn] B. Andela [verfasserIn] G. van den Oord [verfasserIn] E. Ranguelova [verfasserIn] N. Renaud [verfasserIn] W.D. Kissling [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	LiDAR Point cloud Feature extraction Airborne Laser Scanning (ALS) Biodiversity Ecology

Übergeordnetes Werk:	In: SoftwareX - Elsevier, 2016, 12(2020), Seite 100626-
Übergeordnetes Werk:	volume:12 ; year:2020 ; pages:100626-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.softx.2020.100626

Katalog-ID:	DOAJ058698493

Internformat


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520			\|a Point cloud datasets provided by LiDAR have become an integral part in many research fields including archaeology, forestry, and ecology. Facilitated by technological advances, the volume of these datasets has steadily increased, with modern airborne laser scanning surveys now providing high-resolution, (super-)national scale, multi-terabyte point clouds. However, their wider scientific exploitation is hindered by the scarcity of open source software tools capable of handling the challenges of accessing, processing, and extracting meaningful information from massive datasets, as well as by the domain-specificity of existing tools. Here we present Laserchicken, a user-extendable, cross-platform Python tool for extracting statistical properties of flexibly defined subsets of point cloud data, aimed at enabling efficient, scalable, distributed processing of multi-terabyte datasets. We demonstrate Laserchicken’s ability to unlock these transformative new resources, e.g. in macroecology and species distribution modelling, where it is used to characterize the 3D vegetation structure at high resolution (<10 m) across whole countries or regions. We further discuss its potential as a domain agnostic, flexible tool that can also facilitate novel applications in other research fields.
650		4	\|a LiDAR
650		4	\|a Point cloud
650		4	\|a Feature extraction
650		4	\|a Airborne Laser Scanning (ALS)
650		4	\|a Biodiversity
650		4	\|a Ecology
653		0	\|a Computer software
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700	0		\|a N. Renaud \|e verfasserin \|4 aut
700	0		\|a W.D. Kissling \|e verfasserin \|4 aut
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allfields_unstemmed	10.1016/j.softx.2020.100626 doi (DE-627)DOAJ058698493 (DE-599)DOAJ467490adc60249c6b778891ad9032bd0 DE-627 ger DE-627 rakwb eng QA76.75-76.765 C. Meijer verfasserin aut Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point cloud datasets provided by LiDAR have become an integral part in many research fields including archaeology, forestry, and ecology. Facilitated by technological advances, the volume of these datasets has steadily increased, with modern airborne laser scanning surveys now providing high-resolution, (super-)national scale, multi-terabyte point clouds. However, their wider scientific exploitation is hindered by the scarcity of open source software tools capable of handling the challenges of accessing, processing, and extracting meaningful information from massive datasets, as well as by the domain-specificity of existing tools. Here we present Laserchicken, a user-extendable, cross-platform Python tool for extracting statistical properties of flexibly defined subsets of point cloud data, aimed at enabling efficient, scalable, distributed processing of multi-terabyte datasets. We demonstrate Laserchicken’s ability to unlock these transformative new resources, e.g. in macroecology and species distribution modelling, where it is used to characterize the 3D vegetation structure at high resolution (<10 m) across whole countries or regions. We further discuss its potential as a domain agnostic, flexible tool that can also facilitate novel applications in other research fields. LiDAR Point cloud Feature extraction Airborne Laser Scanning (ALS) Biodiversity Ecology Computer software M.W. Grootes verfasserin aut Z. Koma verfasserin aut Y. Dzigan verfasserin aut R. Gonçalves verfasserin aut B. Andela verfasserin aut G. van den Oord verfasserin aut E. Ranguelova verfasserin aut N. Renaud verfasserin aut W.D. Kissling verfasserin aut In SoftwareX Elsevier, 2016 12(2020), Seite 100626- (DE-627)824451805 (DE-600)2819369-6 23527110 nnns volume:12 year:2020 pages:100626- https://doi.org/10.1016/j.softx.2020.100626 kostenfrei https://doaj.org/article/467490adc60249c6b778891ad9032bd0 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352711020303393 kostenfrei https://doaj.org/toc/2352-7110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 100626-
allfieldsGer	10.1016/j.softx.2020.100626 doi (DE-627)DOAJ058698493 (DE-599)DOAJ467490adc60249c6b778891ad9032bd0 DE-627 ger DE-627 rakwb eng QA76.75-76.765 C. Meijer verfasserin aut Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point cloud datasets provided by LiDAR have become an integral part in many research fields including archaeology, forestry, and ecology. Facilitated by technological advances, the volume of these datasets has steadily increased, with modern airborne laser scanning surveys now providing high-resolution, (super-)national scale, multi-terabyte point clouds. However, their wider scientific exploitation is hindered by the scarcity of open source software tools capable of handling the challenges of accessing, processing, and extracting meaningful information from massive datasets, as well as by the domain-specificity of existing tools. Here we present Laserchicken, a user-extendable, cross-platform Python tool for extracting statistical properties of flexibly defined subsets of point cloud data, aimed at enabling efficient, scalable, distributed processing of multi-terabyte datasets. We demonstrate Laserchicken’s ability to unlock these transformative new resources, e.g. in macroecology and species distribution modelling, where it is used to characterize the 3D vegetation structure at high resolution (<10 m) across whole countries or regions. We further discuss its potential as a domain agnostic, flexible tool that can also facilitate novel applications in other research fields. LiDAR Point cloud Feature extraction Airborne Laser Scanning (ALS) Biodiversity Ecology Computer software M.W. Grootes verfasserin aut Z. Koma verfasserin aut Y. Dzigan verfasserin aut R. Gonçalves verfasserin aut B. Andela verfasserin aut G. van den Oord verfasserin aut E. Ranguelova verfasserin aut N. Renaud verfasserin aut W.D. Kissling verfasserin aut In SoftwareX Elsevier, 2016 12(2020), Seite 100626- (DE-627)824451805 (DE-600)2819369-6 23527110 nnns volume:12 year:2020 pages:100626- https://doi.org/10.1016/j.softx.2020.100626 kostenfrei https://doaj.org/article/467490adc60249c6b778891ad9032bd0 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352711020303393 kostenfrei https://doaj.org/toc/2352-7110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 100626-
allfieldsSound	10.1016/j.softx.2020.100626 doi (DE-627)DOAJ058698493 (DE-599)DOAJ467490adc60249c6b778891ad9032bd0 DE-627 ger DE-627 rakwb eng QA76.75-76.765 C. Meijer verfasserin aut Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point cloud datasets provided by LiDAR have become an integral part in many research fields including archaeology, forestry, and ecology. Facilitated by technological advances, the volume of these datasets has steadily increased, with modern airborne laser scanning surveys now providing high-resolution, (super-)national scale, multi-terabyte point clouds. However, their wider scientific exploitation is hindered by the scarcity of open source software tools capable of handling the challenges of accessing, processing, and extracting meaningful information from massive datasets, as well as by the domain-specificity of existing tools. Here we present Laserchicken, a user-extendable, cross-platform Python tool for extracting statistical properties of flexibly defined subsets of point cloud data, aimed at enabling efficient, scalable, distributed processing of multi-terabyte datasets. We demonstrate Laserchicken’s ability to unlock these transformative new resources, e.g. in macroecology and species distribution modelling, where it is used to characterize the 3D vegetation structure at high resolution (<10 m) across whole countries or regions. We further discuss its potential as a domain agnostic, flexible tool that can also facilitate novel applications in other research fields. LiDAR Point cloud Feature extraction Airborne Laser Scanning (ALS) Biodiversity Ecology Computer software M.W. Grootes verfasserin aut Z. Koma verfasserin aut Y. Dzigan verfasserin aut R. Gonçalves verfasserin aut B. Andela verfasserin aut G. van den Oord verfasserin aut E. Ranguelova verfasserin aut N. Renaud verfasserin aut W.D. Kissling verfasserin aut In SoftwareX Elsevier, 2016 12(2020), Seite 100626- (DE-627)824451805 (DE-600)2819369-6 23527110 nnns volume:12 year:2020 pages:100626- https://doi.org/10.1016/j.softx.2020.100626 kostenfrei https://doaj.org/article/467490adc60249c6b778891ad9032bd0 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352711020303393 kostenfrei https://doaj.org/toc/2352-7110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 12 2020 100626-
language	English
source	In SoftwareX 12(2020), Seite 100626- volume:12 year:2020 pages:100626-
sourceStr	In SoftwareX 12(2020), Seite 100626- volume:12 year:2020 pages:100626-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	LiDAR Point cloud Feature extraction Airborne Laser Scanning (ALS) Biodiversity Ecology Computer software
isfreeaccess_bool	true
container_title	SoftwareX
authorswithroles_txt_mv	C. Meijer @@aut@@ M.W. Grootes @@aut@@ Z. Koma @@aut@@ Y. Dzigan @@aut@@ R. Gonçalves @@aut@@ B. Andela @@aut@@ G. van den Oord @@aut@@ E. Ranguelova @@aut@@ N. Renaud @@aut@@ W.D. Kissling @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	824451805
id	DOAJ058698493
language_de	englisch
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author	C. Meijer
spellingShingle	C. Meijer misc QA76.75-76.765 misc LiDAR misc Point cloud misc Feature extraction misc Airborne Laser Scanning (ALS) misc Biodiversity misc Ecology misc Computer software Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets
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topic_title	QA76.75-76.765 Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets LiDAR Point cloud Feature extraction Airborne Laser Scanning (ALS) Biodiversity Ecology
topic	misc QA76.75-76.765 misc LiDAR misc Point cloud misc Feature extraction misc Airborne Laser Scanning (ALS) misc Biodiversity misc Ecology misc Computer software
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title	Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets
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title_sort	laserchicken—a tool for distributed feature calculation from massive lidar point cloud datasets
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title_auth	Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets
abstract	Point cloud datasets provided by LiDAR have become an integral part in many research fields including archaeology, forestry, and ecology. Facilitated by technological advances, the volume of these datasets has steadily increased, with modern airborne laser scanning surveys now providing high-resolution, (super-)national scale, multi-terabyte point clouds. However, their wider scientific exploitation is hindered by the scarcity of open source software tools capable of handling the challenges of accessing, processing, and extracting meaningful information from massive datasets, as well as by the domain-specificity of existing tools. Here we present Laserchicken, a user-extendable, cross-platform Python tool for extracting statistical properties of flexibly defined subsets of point cloud data, aimed at enabling efficient, scalable, distributed processing of multi-terabyte datasets. We demonstrate Laserchicken’s ability to unlock these transformative new resources, e.g. in macroecology and species distribution modelling, where it is used to characterize the 3D vegetation structure at high resolution (<10 m) across whole countries or regions. We further discuss its potential as a domain agnostic, flexible tool that can also facilitate novel applications in other research fields.
abstractGer	Point cloud datasets provided by LiDAR have become an integral part in many research fields including archaeology, forestry, and ecology. Facilitated by technological advances, the volume of these datasets has steadily increased, with modern airborne laser scanning surveys now providing high-resolution, (super-)national scale, multi-terabyte point clouds. However, their wider scientific exploitation is hindered by the scarcity of open source software tools capable of handling the challenges of accessing, processing, and extracting meaningful information from massive datasets, as well as by the domain-specificity of existing tools. Here we present Laserchicken, a user-extendable, cross-platform Python tool for extracting statistical properties of flexibly defined subsets of point cloud data, aimed at enabling efficient, scalable, distributed processing of multi-terabyte datasets. We demonstrate Laserchicken’s ability to unlock these transformative new resources, e.g. in macroecology and species distribution modelling, where it is used to characterize the 3D vegetation structure at high resolution (<10 m) across whole countries or regions. We further discuss its potential as a domain agnostic, flexible tool that can also facilitate novel applications in other research fields.
abstract_unstemmed	Point cloud datasets provided by LiDAR have become an integral part in many research fields including archaeology, forestry, and ecology. Facilitated by technological advances, the volume of these datasets has steadily increased, with modern airborne laser scanning surveys now providing high-resolution, (super-)national scale, multi-terabyte point clouds. However, their wider scientific exploitation is hindered by the scarcity of open source software tools capable of handling the challenges of accessing, processing, and extracting meaningful information from massive datasets, as well as by the domain-specificity of existing tools. Here we present Laserchicken, a user-extendable, cross-platform Python tool for extracting statistical properties of flexibly defined subsets of point cloud data, aimed at enabling efficient, scalable, distributed processing of multi-terabyte datasets. We demonstrate Laserchicken’s ability to unlock these transformative new resources, e.g. in macroecology and species distribution modelling, where it is used to characterize the 3D vegetation structure at high resolution (<10 m) across whole countries or regions. We further discuss its potential as a domain agnostic, flexible tool that can also facilitate novel applications in other research fields.
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title_short	Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets
url	https://doi.org/10.1016/j.softx.2020.100626 https://doaj.org/article/467490adc60249c6b778891ad9032bd0 http://www.sciencedirect.com/science/article/pii/S2352711020303393 https://doaj.org/toc/2352-7110
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We demonstrate Laserchicken’s ability to unlock these transformative new resources, e.g. in macroecology and species distribution modelling, where it is used to characterize the 3D vegetation structure at high resolution (<10 m) across whole countries or regions. 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Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets

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