#georeferencing — Public Fediverse posts

Turn floor plans or drone photos into interactive maps in minutes. Our Georeferencer makes it easy; simply click matching points to align your image & get instant results. Ready to map your assets? maptiler.link/4ejyyKg #MapTiler #Georeferencing #GIS

University of North Carolina: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections. “A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This […]

University of North Carolina: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections. “A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This […]

Georeference a Scanned Map in QGIS

https://videos.qwast-gis.com/w/fHgHKiaYuJEs6Jwztagx73

Georeference a Scanned Map in QGIS

https://videos.qwast-gis.com/w/fHgHKiaYuJEs6Jwztagx73

Locating The Pā Sites From Hartley Ferrar’s Geological Maps
[spatial analysis, New Zealand]
--
https://doi.org/10.70460/jpa.v15i1.388 <-- shared paper
--
#GIS #spatial #mapping #geology #historicmaps #Māori #Maori #defensive #Pa #Pā #NewZealand #fort #Northland #NewZealand #LiDAR #remotesensing #model #modeling #geologist #HartleyFerrar #archaeology #spatialanalysis #remotesensing #earthobservation #georeferencing #digitisation #machinelearning #AI #imagery #survey #cartography #FirstNation #indigenous #culture #landscape #heritage #iwi #hapū #collaboration #legacydata #heritagemanagement

Locating The Pā Sites From Hartley Ferrar’s Geological Maps
[spatial analysis, New Zealand]
--
https://doi.org/10.70460/jpa.v15i1.388 <-- shared paper
--
#GIS #spatial #mapping #geology #historicmaps #Māori #Maori #defensive #Pa #Pā #NewZealand #fort #Northland #NewZealand #LiDAR #remotesensing #model #modeling #geologist #HartleyFerrar #archaeology #spatialanalysis #remotesensing #earthobservation #georeferencing #digitisation #machinelearning #AI #imagery #survey #cartography #FirstNation #indigenous #culture #landscape #heritage #iwi #hapū #collaboration #legacydata #heritagemanagement

Locating The Pā Sites From Hartley Ferrar’s Geological Maps
[spatial analysis, New Zealand]
--
https://doi.org/10.70460/jpa.v15i1.388 <-- shared paper
--
#GIS #spatial #mapping #geology #historicmaps #Māori #Maori #defensive #Pa #Pā #NewZealand #fort #Northland #NewZealand #LiDAR #remotesensing #model #modeling #geologist #HartleyFerrar #archaeology #spatialanalysis #remotesensing #earthobservation #georeferencing #digitisation #machinelearning #AI #imagery #survey #cartography #FirstNation #indigenous #culture #landscape #heritage #iwi #hapū #collaboration #legacydata #heritagemanagement

Locating The Pā Sites From Hartley Ferrar’s Geological Maps
[spatial analysis, New Zealand]
--
https://doi.org/10.70460/jpa.v15i1.388 <-- shared paper
--
#GIS #spatial #mapping #geology #historicmaps #Māori #Maori #defensive #Pa #Pā #NewZealand #fort #Northland #NewZealand #LiDAR #remotesensing #model #modeling #geologist #HartleyFerrar #archaeology #spatialanalysis #remotesensing #earthobservation #georeferencing #digitisation #machinelearning #AI #imagery #survey #cartography #FirstNation #indigenous #culture #landscape #heritage #iwi #hapū #collaboration #legacydata #heritagemanagement

Locating The Pā Sites From Hartley Ferrar’s Geological Maps
[spatial analysis, New Zealand]
--
https://doi.org/10.70460/jpa.v15i1.388 <-- shared paper
--
#GIS #spatial #mapping #geology #historicmaps #Māori #Maori #defensive #Pa #Pā #NewZealand #fort #Northland #NewZealand #LiDAR #remotesensing #model #modeling #geologist #HartleyFerrar #archaeology #spatialanalysis #remotesensing #earthobservation #georeferencing #digitisation #machinelearning #AI #imagery #survey #cartography #FirstNation #indigenous #culture #landscape #heritage #iwi #hapū #collaboration #legacydata #heritagemanagement

UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections – EurekAlert!

News Release 5-Dec-2025

UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections, University of North Carolina at Chapel Hill

A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This task has traditionally been slow, expensive and dependent on significant manual effort. The team found that LLMs can complete this work with near-human accuracy while being significantly faster and more cost-effective. 

“Our study explores how large language models can take on one of the biggest bottlenecks in digitizing plant collections,” said Yuyang Xie, first author and postdoctoral researcher in the department of biology at UNC. “We are pioneering the use of these tools for georeferencing, a breakthrough that will accelerate the digitization of plant specimens and unlock new possibilities for ecological research.” 

The research set out to answer a central question: Can AI automate one of the most time-consuming steps in digitizing natural history collections? The Carolina team found out that yes, it can. LLMs not only performed georeferencing with an error margin of less than 10 kilometers, outperforming traditional methods, but also completed the task at a fraction of the time and cost. 

“Recent advances in LLMs can potentially transform the georeferencing process, making it faster and more accurate,” said Xiao Feng, corresponding author and assistant professor in the department of biology at UNC. “This gives researchers unprecedented opportunities to advance our understanding of global biodiversity distributions.” 

The implications are significant. An estimated 2–3 billion herbarium specimens exist worldwide, but only a small fraction have been digitized. Without digital records and spatial data, researchers face major limitations in tracking biodiversity loss, understanding species movement under climate change and analyzing ecosystem shifts. By deploying AI-powered georeferencing, scientists may soon be able to rapidly digitize vast natural history collections that have remained largely inaccessible. 

“This technology allows us to unlock millions of records that are currently sitting in cabinets,” said Xie. “With the power of LLMs, we can rapidly digitize plant specimen data that will be critical for addressing global environmental challenges.” 

Traditional approaches to georeferencing rely on manual interpretation, specialized software, or multiple rounds of expert review. The UNC study is among the first to apply LLMs to this task and to show they can outperform existing methods in accuracy, efficiency, and scalability. This new approach opens the door to digitizing natural history collections at a speed never before possible. 

The research paper is available online in Nature Plants at: https://www.nature.com/articles/s41477-025-02162-y  

Continue/Read Original Article Here: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections | EurekAlert!

#AI #artificialIntelligence #BiologyDepartment #CarolinaTeam #Collections #DigitizeContent #EurekAlert #Georeferencing #LargeLanguageModelsLLM #LLMs #NaturalHistory #Nature #UNCChapelHill #XiaoFeng #YuyangXie

UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections – EurekAlert!

News Release 5-Dec-2025

UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections, University of North Carolina at Chapel Hill

A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This task has traditionally been slow, expensive and dependent on significant manual effort. The team found that LLMs can complete this work with near-human accuracy while being significantly faster and more cost-effective. 

“Our study explores how large language models can take on one of the biggest bottlenecks in digitizing plant collections,” said Yuyang Xie, first author and postdoctoral researcher in the department of biology at UNC. “We are pioneering the use of these tools for georeferencing, a breakthrough that will accelerate the digitization of plant specimens and unlock new possibilities for ecological research.” 

The research set out to answer a central question: Can AI automate one of the most time-consuming steps in digitizing natural history collections? The Carolina team found out that yes, it can. LLMs not only performed georeferencing with an error margin of less than 10 kilometers, outperforming traditional methods, but also completed the task at a fraction of the time and cost. 

“Recent advances in LLMs can potentially transform the georeferencing process, making it faster and more accurate,” said Xiao Feng, corresponding author and assistant professor in the department of biology at UNC. “This gives researchers unprecedented opportunities to advance our understanding of global biodiversity distributions.” 

The implications are significant. An estimated 2–3 billion herbarium specimens exist worldwide, but only a small fraction have been digitized. Without digital records and spatial data, researchers face major limitations in tracking biodiversity loss, understanding species movement under climate change and analyzing ecosystem shifts. By deploying AI-powered georeferencing, scientists may soon be able to rapidly digitize vast natural history collections that have remained largely inaccessible. 

“This technology allows us to unlock millions of records that are currently sitting in cabinets,” said Xie. “With the power of LLMs, we can rapidly digitize plant specimen data that will be critical for addressing global environmental challenges.” 

Traditional approaches to georeferencing rely on manual interpretation, specialized software, or multiple rounds of expert review. The UNC study is among the first to apply LLMs to this task and to show they can outperform existing methods in accuracy, efficiency, and scalability. This new approach opens the door to digitizing natural history collections at a speed never before possible. 

The research paper is available online in Nature Plants at: https://www.nature.com/articles/s41477-025-02162-y  

Continue/Read Original Article Here: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections | EurekAlert!

#AI #artificialIntelligence #BiologyDepartment #CarolinaTeam #Collections #DigitizeContent #EurekAlert #Georeferencing #LargeLanguageModelsLLM #LLMs #NaturalHistory #Nature #UNCChapelHill #XiaoFeng #YuyangXie

UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections – EurekAlert!

News Release 5-Dec-2025

UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections, University of North Carolina at Chapel Hill

A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This task has traditionally been slow, expensive and dependent on significant manual effort. The team found that LLMs can complete this work with near-human accuracy while being significantly faster and more cost-effective. 

“Our study explores how large language models can take on one of the biggest bottlenecks in digitizing plant collections,” said Yuyang Xie, first author and postdoctoral researcher in the department of biology at UNC. “We are pioneering the use of these tools for georeferencing, a breakthrough that will accelerate the digitization of plant specimens and unlock new possibilities for ecological research.” 

The research set out to answer a central question: Can AI automate one of the most time-consuming steps in digitizing natural history collections? The Carolina team found out that yes, it can. LLMs not only performed georeferencing with an error margin of less than 10 kilometers, outperforming traditional methods, but also completed the task at a fraction of the time and cost. 

“Recent advances in LLMs can potentially transform the georeferencing process, making it faster and more accurate,” said Xiao Feng, corresponding author and assistant professor in the department of biology at UNC. “This gives researchers unprecedented opportunities to advance our understanding of global biodiversity distributions.” 

The implications are significant. An estimated 2–3 billion herbarium specimens exist worldwide, but only a small fraction have been digitized. Without digital records and spatial data, researchers face major limitations in tracking biodiversity loss, understanding species movement under climate change and analyzing ecosystem shifts. By deploying AI-powered georeferencing, scientists may soon be able to rapidly digitize vast natural history collections that have remained largely inaccessible. 

“This technology allows us to unlock millions of records that are currently sitting in cabinets,” said Xie. “With the power of LLMs, we can rapidly digitize plant specimen data that will be critical for addressing global environmental challenges.” 

Traditional approaches to georeferencing rely on manual interpretation, specialized software, or multiple rounds of expert review. The UNC study is among the first to apply LLMs to this task and to show they can outperform existing methods in accuracy, efficiency, and scalability. This new approach opens the door to digitizing natural history collections at a speed never before possible. 

The research paper is available online in Nature Plants at: https://www.nature.com/articles/s41477-025-02162-y  

Continue/Read Original Article Here: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections | EurekAlert!

#AI #artificialIntelligence #BiologyDepartment #CarolinaTeam #Collections #DigitizeContent #EurekAlert #Georeferencing #LargeLanguageModelsLLM #LLMs #NaturalHistory #Nature #UNCChapelHill #XiaoFeng #YuyangXie

UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections – EurekAlert!

News Release 5-Dec-2025

UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections, University of North Carolina at Chapel Hill

A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This task has traditionally been slow, expensive and dependent on significant manual effort. The team found that LLMs can complete this work with near-human accuracy while being significantly faster and more cost-effective. 

“Our study explores how large language models can take on one of the biggest bottlenecks in digitizing plant collections,” said Yuyang Xie, first author and postdoctoral researcher in the department of biology at UNC. “We are pioneering the use of these tools for georeferencing, a breakthrough that will accelerate the digitization of plant specimens and unlock new possibilities for ecological research.” 

The research set out to answer a central question: Can AI automate one of the most time-consuming steps in digitizing natural history collections? The Carolina team found out that yes, it can. LLMs not only performed georeferencing with an error margin of less than 10 kilometers, outperforming traditional methods, but also completed the task at a fraction of the time and cost. 

“Recent advances in LLMs can potentially transform the georeferencing process, making it faster and more accurate,” said Xiao Feng, corresponding author and assistant professor in the department of biology at UNC. “This gives researchers unprecedented opportunities to advance our understanding of global biodiversity distributions.” 

The implications are significant. An estimated 2–3 billion herbarium specimens exist worldwide, but only a small fraction have been digitized. Without digital records and spatial data, researchers face major limitations in tracking biodiversity loss, understanding species movement under climate change and analyzing ecosystem shifts. By deploying AI-powered georeferencing, scientists may soon be able to rapidly digitize vast natural history collections that have remained largely inaccessible. 

“This technology allows us to unlock millions of records that are currently sitting in cabinets,” said Xie. “With the power of LLMs, we can rapidly digitize plant specimen data that will be critical for addressing global environmental challenges.” 

Traditional approaches to georeferencing rely on manual interpretation, specialized software, or multiple rounds of expert review. The UNC study is among the first to apply LLMs to this task and to show they can outperform existing methods in accuracy, efficiency, and scalability. This new approach opens the door to digitizing natural history collections at a speed never before possible. 

The research paper is available online in Nature Plants at: https://www.nature.com/articles/s41477-025-02162-y  

Continue/Read Original Article Here: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections | EurekAlert!

#AI #artificialIntelligence #BiologyDepartment #CarolinaTeam #Collections #DigitizeContent #EurekAlert #Georeferencing #LargeLanguageModelsLLM #LLMs #NaturalHistory #Nature #UNCChapelHill #XiaoFeng #YuyangXie

UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections – EurekAlert!

News Release 5-Dec-2025

UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections, University of North Carolina at Chapel Hill

A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This task has traditionally been slow, expensive and dependent on significant manual effort. The team found that LLMs can complete this work with near-human accuracy while being significantly faster and more cost-effective. 

“Our study explores how large language models can take on one of the biggest bottlenecks in digitizing plant collections,” said Yuyang Xie, first author and postdoctoral researcher in the department of biology at UNC. “We are pioneering the use of these tools for georeferencing, a breakthrough that will accelerate the digitization of plant specimens and unlock new possibilities for ecological research.” 

The research set out to answer a central question: Can AI automate one of the most time-consuming steps in digitizing natural history collections? The Carolina team found out that yes, it can. LLMs not only performed georeferencing with an error margin of less than 10 kilometers, outperforming traditional methods, but also completed the task at a fraction of the time and cost. 

“Recent advances in LLMs can potentially transform the georeferencing process, making it faster and more accurate,” said Xiao Feng, corresponding author and assistant professor in the department of biology at UNC. “This gives researchers unprecedented opportunities to advance our understanding of global biodiversity distributions.” 

The implications are significant. An estimated 2–3 billion herbarium specimens exist worldwide, but only a small fraction have been digitized. Without digital records and spatial data, researchers face major limitations in tracking biodiversity loss, understanding species movement under climate change and analyzing ecosystem shifts. By deploying AI-powered georeferencing, scientists may soon be able to rapidly digitize vast natural history collections that have remained largely inaccessible. 

“This technology allows us to unlock millions of records that are currently sitting in cabinets,” said Xie. “With the power of LLMs, we can rapidly digitize plant specimen data that will be critical for addressing global environmental challenges.” 

Traditional approaches to georeferencing rely on manual interpretation, specialized software, or multiple rounds of expert review. The UNC study is among the first to apply LLMs to this task and to show they can outperform existing methods in accuracy, efficiency, and scalability. This new approach opens the door to digitizing natural history collections at a speed never before possible. 

The research paper is available online in Nature Plants at: https://www.nature.com/articles/s41477-025-02162-y  

Continue/Read Original Article Here: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections | EurekAlert!

#AI #artificialIntelligence #BiologyDepartment #CarolinaTeam #Collections #DigitizeContent #EurekAlert #Georeferencing #LargeLanguageModelsLLM #LLMs #NaturalHistory #Nature #UNCChapelHill #XiaoFeng #YuyangXie

University of North Carolina: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections. “A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This […]

https://rbfirehose.com/2025/12/06/university-of-north-carolina-unc-chapel-hill-study-shows-ai-can-dramatically-speed-up-digitizing-natural-history-collections/

University of North Carolina: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections. “A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This […]

https://rbfirehose.com/2025/12/06/university-of-north-carolina-unc-chapel-hill-study-shows-ai-can-dramatically-speed-up-digitizing-natural-history-collections/

University of North Carolina: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections. “A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This […]

https://rbfirehose.com/2025/12/06/university-of-north-carolina-unc-chapel-hill-study-shows-ai-can-dramatically-speed-up-digitizing-natural-history-collections/

University of North Carolina: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections. “A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This […]

https://rbfirehose.com/2025/12/06/university-of-north-carolina-unc-chapel-hill-study-shows-ai-can-dramatically-speed-up-digitizing-natural-history-collections/

University of North Carolina: UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections. “A new study from UNC-Chapel Hill researchers shows that advanced artificial intelligence tools, specifically large language models (LLMs), can accurately determine the locations where plant specimens were originally collected, a process known as georeferencing. This […]

https://rbfirehose.com/2025/12/06/university-of-north-carolina-unc-chapel-hill-study-shows-ai-can-dramatically-speed-up-digitizing-natural-history-collections/

Join our #crowdsourcing project! Help georeference 1,497 digitised historical #maps from the #ethlibrary

https://library.ethz.ch/en/locations-and-media/media-types/geodata-maps-aerial-photos/crowdsourcing-project.html

#karten #citizenscience #georeferencing #ethbibliothek

Join our #crowdsourcing project! Help georeference 1,497 digitised historical #maps from the #ethlibrary

https://library.ethz.ch/en/locations-and-media/media-types/geodata-maps-aerial-photos/crowdsourcing-project.html

#karten #citizenscience #georeferencing #ethbibliothek

OldInsuranceMaps.net: Crowdsourcing georeferenced Sanborn maps. Very incomplete archive but totally the right idea.
https://oldinsurancemaps.net/
#georeferencing #fireinsurance #crowdsourcing #sanborn #history #maps #+

OldInsuranceMaps.net: Crowdsourcing georeferenced Sanborn maps. Very incomplete archive but totally the right idea.
https://oldinsurancemaps.net/
#georeferencing #fireinsurance #crowdsourcing #sanborn #history #maps #+

Hacking Global Positioning Systems Onto 16th-Century Maps - What if GPS had existed in 1565? No satellites or microelectronics, sure—but let’s... - https://hackaday.com/2024/11/25/hacking-global-positioning-systems-onto-16th-century-maps/ #georeferencing #historicalmap #georeference #navigation #gpshacks #history #gps #map

Hacking Global Positioning Systems Onto 16th-Century Maps - What if GPS had existed in 1565? No satellites or microelectronics, sure—but let’s... - https://hackaday.com/2024/11/25/hacking-global-positioning-systems-onto-16th-century-maps/ #georeferencing #historicalmap #georeference #navigation #gpshacks #history #gps #map

When I saw this, I was like "uh oh, it looks like someone screwed up the ground control points..."

But no, it turns out that's what the street actually looks like 🤣

#Georeferencing
#NighttimeLights
#Dortmund

When I saw this, I was like "uh oh, it looks like someone screwed up the ground control points..."

But no, it turns out that's what the street actually looks like 🤣

#Georeferencing
#NighttimeLights
#Dortmund

When I saw this, I was like "uh oh, it looks like someone screwed up the ground control points..."

But no, it turns out that's what the street actually looks like 🤣

#Georeferencing
#NighttimeLights
#Dortmund

When I saw this, I was like "uh oh, it looks like someone screwed up the ground control points..."

But no, it turns out that's what the street actually looks like 🤣

#Georeferencing
#NighttimeLights
#Dortmund

When I saw this, I was like "uh oh, it looks like someone screwed up the ground control points..."

But no, it turns out that's what the street actually looks like 🤣

#Georeferencing
#NighttimeLights
#Dortmund

1/3 Got 3D?

#GoogleEarthPro is free software that simulates 3D. It can be installed on PC/Mac/Linux. In addition to the screen controls you can rightclick-hold and slowly move your pointing device.

Here are example #GIS KMZ files for Ventura County, CA.

Trails and roads. Transparent background.
https://mappingsupport.com/p2/kmz_demo/ventura_county_all.kmz 21MB

Trails and roads. Scanned USGS topos.
https://mappingsupport.com/p2/kmz_demo/ventura_county_all_topo.kmz 37MB

#Georeference #Georeferencing #Geospatial #Maps #Mapping

1/3 Got 3D?

#GoogleEarthPro is free software that simulates 3D. It can be installed on PC/Mac/Linux. In addition to the screen controls you can rightclick-hold and slowly move your pointing device.

Here are example #GIS KMZ files for Ventura County, CA.

Trails and roads. Transparent background.
https://mappingsupport.com/p2/kmz_demo/ventura_county_all.kmz 21MB

Trails and roads. Scanned USGS topos.
https://mappingsupport.com/p2/kmz_demo/ventura_county_all_topo.kmz 37MB

#Georeference #Georeferencing #Geospatial #Maps #Mapping

At the #NaturalHistoryMuseumLondon, we are not only imaging our collections, we also do #georeferencing. Amazingly, the more we digitise, the more data we get, and we can even reconstruct travel routes.
E.g.; Oleg #Polunin (https://bionomia.net/Q266663) collected in the "Kolahoi valley", #Kashmir, in 8/1956.
He started his travel from Pahalgam in the south, via Liddarwat (now a camping ground) towards a "pass" on 14422 ft. altitude where Polunin collected several plants.

At the #NaturalHistoryMuseumLondon, we are not only imaging our collections, we also do #georeferencing. Amazingly, the more we digitise, the more data we get, and we can even reconstruct travel routes.
E.g.; Oleg #Polunin (https://bionomia.net/Q266663) collected in the "Kolahoi valley", #Kashmir, in 8/1956.
He started his travel from Pahalgam in the south, via Liddarwat (now a camping ground) towards a "pass" on 14422 ft. altitude where Polunin collected several plants.

@lapingvino @factotum

Btw, #normalization of #georeference|s is not trivial, because it requires techniques called #InstanceMatching.

It's an entire field of ongoing research.

A search for one of the #Berlin|s should be able to come up with

→ #QTHJO62
→ #OLC9F4M

🌺

🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocode #Georcoding #Georeferencing #OpenLocationCode #PlusCode #Maidenhead #HamRadio #AFU #CBFunk #CitizenBand #CiBi #ActivityVocabulary

@lapingvino @factotum

Btw, #normalization of #georeference|s is not trivial, because it requires techniques called #InstanceMatching.

It's an entire field of ongoing research.

A search for one of the #Berlin|s should be able to come up with

→ #QTHJO62
→ #OLC9F4M

🌺

🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocode #Georcoding #Georeferencing #OpenLocationCode #PlusCode #Maidenhead #HamRadio #AFU #CBFunk #CitizenBand #CiBi #ActivityVocabulary

@lapingvino @factotum

People who want to use exact #georeferencing in a posting should have a way to do so, for example for offline events or to describe #geospatial (or #marsian) features.

🌺

🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocode #Georcoding #OpenLocationCode #PlusCode #Maidenhead #HamRadio #AFU #CBFunk #CitizenBand #CiBi #ActivityVocabulary

@lapingvino @factotum

People who want to use exact #georeferencing in a posting should have a way to do so, for example for offline events or to describe #geospatial (or #marsian) features.

🌺

🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocode #Georcoding #OpenLocationCode #PlusCode #Maidenhead #HamRadio #AFU #CBFunk #CitizenBand #CiBi #ActivityVocabulary

@lapingvino @factotum

I take it for granted that users must be able to decide upon the precision of the #geocoding they use for their postings.

And that they must also be able to decide upon the geogaphic range of search when they lookup #georef|erenced postings.

🌺

🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocode #Georeferencing #OpenLocationCode #PlusCode #Maidenhead #HamRadio #AFU #CBFunk #CitizenBand #CiBi #ActivityVocabulary

@lapingvino @factotum

I take it for granted that users must be able to decide upon the precision of the #geocoding they use for their postings.

And that they must also be able to decide upon the geogaphic range of search when they lookup #georef|erenced postings.

🌺

🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocode #Georeferencing #OpenLocationCode #PlusCode #Maidenhead #HamRadio #AFU #CBFunk #CitizenBand #CiBi #ActivityVocabulary

@lapingvino @helge @factotum

The location property of the #ActivityVocabulary does not only support exact locations but also areas in the sense that it allows for ›logical locations‹.

A logical location could be ›near Berlin«, but also a #geocode with reduced precision.

🌺

🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocoding #Georef #Georeferencing #OpenLocationCode #PlusCode #Maidenhead #HamRadio #AFU #CBFunk #CitizenBand #CiBi

@lapingvino @helge @factotum

The location property of the #ActivityVocabulary does not only support exact locations but also areas in the sense that it allows for ›logical locations‹.

A logical location could be ›near Berlin«, but also a #geocode with reduced precision.

🌺

🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocoding #Georef #Georeferencing #OpenLocationCode #PlusCode #Maidenhead #HamRadio #AFU #CBFunk #CitizenBand #CiBi

@helge @factotum @lapingvino

Relax, no one is seeking to re-invent any wheel.

This thread is more about usability than it is about implementation.

No one can make use of #semantic properties in a vocabulary as long as they do not show up in frontends.

So, rather than deter consideration, let's keep the dialogue going.

🌺

🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocode #Geocoding #Georef #Georeferencing #OpenLocationCode #PlusCode #Maidenhead

@helge @factotum @lapingvino

Relax, no one is seeking to re-invent any wheel.

This thread is more about usability than it is about implementation.

No one can make use of #semantic properties in a vocabulary as long as they do not show up in frontends.

So, rather than deter consideration, let's keep the dialogue going.

🌺

🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocode #Geocoding #Georef #Georeferencing #OpenLocationCode #PlusCode #Maidenhead

@Ustinoff Nein, nicht mit meiner Methode. Ich zuppele an den vier Ecken bis es so einigermaßen… Und dann hängt es am Carthographen und dem Maßstab, ob es passt.
Auf der anderen Seite will man ja auch kein Hubbabubba https://www.flickr.com/photos/mprove/32424779033/in/datetaken-public/

#rectification #georeferencing #oldmaps

@Ustinoff Nein, nicht mit meiner Methode. Ich zuppele an den vier Ecken bis es so einigermaßen… Und dann hängt es am Carthographen und dem Maßstab, ob es passt.
Auf der anderen Seite will man ja auch kein Hubbabubba https://www.flickr.com/photos/mprove/32424779033/in/datetaken-public/

#rectification #georeferencing #oldmaps

@Ustinoff Nein, nicht mit meiner Methode. Ich zuppele an den vier Ecken bis es so einigermaßen… Und dann hängt es am Carthographen und dem Maßstab, ob es passt.
Auf der anderen Seite will man ja auch kein Hubbabubba https://www.flickr.com/photos/mprove/32424779033/in/datetaken-public/

#rectification #georeferencing #oldmaps

@Ustinoff Nein, nicht mit meiner Methode. Ich zuppele an den vier Ecken bis es so einigermaßen… Und dann hängt es am Carthographen und dem Maßstab, ob es passt.
Auf der anderen Seite will man ja auch kein Hubbabubba https://www.flickr.com/photos/mprove/32424779033/in/datetaken-public/

#rectification #georeferencing #oldmaps

@Ustinoff Nein, nicht mit meiner Methode. Ich zuppele an den vier Ecken bis es so einigermaßen… Und dann hängt es am Carthographen und dem Maßstab, ob es passt.
Auf der anderen Seite will man ja auch kein Hubbabubba https://www.flickr.com/photos/mprove/32424779033/in/datetaken-public/

#rectification #georeferencing #oldmaps