#georeferencing — Public Fediverse posts

Are you at INTERGEO? Want to know about the latest 3D map visualization tech, #GeoSplats? Need a georeferencing solution, either offline or in the cloud? Catch up with Jaroslav Polacek; he's at the conference for all three days.

#INTERGEO2026 #GIS #3D #GeoSplats #Georeferencing

Are you at INTERGEO? Want to know about the latest 3D map visualization tech, #GeoSplats? Need a georeferencing solution, either offline or in the cloud? Catch up with Jaroslav Polacek; he's at the conference for all three days.

#INTERGEO2026 #GIS #3D #GeoSplats #Georeferencing

Are you at INTERGEO? Want to know about the latest 3D map visualization tech, #GeoSplats? Need a georeferencing solution, either offline or in the cloud? Catch up with Jaroslav Polacek; he's at the conference for all three days.

#INTERGEO2026 #GIS #3D #GeoSplats #Georeferencing

Are you at INTERGEO? Want to know about the latest 3D map visualization tech, #GeoSplats? Need a georeferencing solution, either offline or in the cloud? Catch up with Jaroslav Polacek; he's at the conference for all three days.

#INTERGEO2026 #GIS #3D #GeoSplats #Georeferencing

Are you at INTERGEO? Want to know about the latest 3D map visualization tech, #GeoSplats? Need a georeferencing solution, either offline or in the cloud? Catch up with Jaroslav Polacek; he's at the conference for all three days.

#INTERGEO2026 #GIS #3D #GeoSplats #Georeferencing

Danvk: Introducing mapsnap: Automated Georeferencing for Historic Sanborn Insurance Maps. “It is, for the most part, possible to automaticallly georeference Sanborn maps. Some maps are harder than others and it doesn’t get everything right, but it generally does a good job. My program to automatically georeference Sanborn maps is called mapsnap, and I’m excited to explain how it works!”

Danvk: Introducing mapsnap: Automated Georeferencing for Historic Sanborn Insurance Maps. “It is, for the most part, possible to automaticallly georeference Sanborn maps. Some maps are harder than others and it doesn’t get everything right, but it generally does a good job. My program to automatically georeference Sanborn maps is called mapsnap, and I’m excited to explain how it works!”

Danvk: Introducing mapsnap: Automated Georeferencing for Historic Sanborn Insurance Maps. “It is, for the most part, possible to automaticallly georeference Sanborn maps. Some maps are harder than others and it doesn’t get everything right, but it generally does a good job. My program to automatically georeference Sanborn maps is called mapsnap, and I’m excited to explain how it works!”

Danvk: Introducing mapsnap: Automated Georeferencing for Historic Sanborn Insurance Maps. “It is, for the most part, possible to automaticallly georeference Sanborn maps. Some maps are harder than others and it doesn’t get everything right, but it generally does a good job. My program to automatically georeference Sanborn maps is called mapsnap, and I’m excited to explain how it works!”

Danvk: Introducing mapsnap: Automated Georeferencing for Historic Sanborn Insurance Maps. “It is, for the most part, possible to automaticallly georeference Sanborn maps. Some maps are harder than others and it doesn’t get everything right, but it generally does a good job. My program to automatically georeference Sanborn maps is called mapsnap, and I’m excited to explain how it works!”

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

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

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

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

Using GPS in the year 1565 There’s a wonderful web app (“Allmaps Here”) that shows your GPS location on old maps. I love it. www.verbeeld.be/2024/11/17/u... #maps #allmapshere #cartography #georeferencing

Using GPS in the year 1565 There’s a wonderful web app (“Allmaps Here”) that shows your GPS location on old maps. I love it. www.verbeeld.be/2024/11/17/u... #maps #allmapshere #cartography #georeferencing

Using GPS in the year 1565 There’s a wonderful web app (“Allmaps Here”) that shows your GPS location on old maps. I love it. www.verbeeld.be/2024/11/17/u... #maps #allmapshere #cartography #georeferencing

Using GPS in the year 1565 There’s a wonderful web app (“Allmaps Here”) that shows your GPS location on old maps. I love it. www.verbeeld.be/2024/11/17/u... #maps #allmapshere #cartography #georeferencing

Using GPS in the year 1565 There’s a wonderful web app (“Allmaps Here”) that shows your GPS location on old maps. I love it. www.verbeeld.be/2024/11/17/u... #maps #allmapshere #cartography #georeferencing

I want to use QGIS to geo reference an image, then later edit the image in GNU IMP, but preserve the location tags.

Is this difficult? Do I need to save off the location data somehow?

#QGIS #georeferencing

I want to use QGIS to geo reference an image, then later edit the image in GNU IMP, but preserve the location tags.

Is this difficult? Do I need to save off the location data somehow?

#QGIS #georeferencing

I want to use QGIS to geo reference an image, then later edit the image in GNU IMP, but preserve the location tags.

Is this difficult? Do I need to save off the location data somehow?

#QGIS #georeferencing

I want to use QGIS to geo reference an image, then later edit the image in GNU IMP, but preserve the location tags.

Is this difficult? Do I need to save off the location data somehow?

#QGIS #georeferencing

I want to use QGIS to geo reference an image, then later edit the image in GNU IMP, but preserve the location tags.

Is this difficult? Do I need to save off the location data somehow?

#QGIS #georeferencing

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 […]

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

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

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/

#Georeferencing #historical #maps by their coordinates is not without its pitfalls. A location in LAT / LON can only be intrepreted with spatial context. While there are some historical proj-strings out there (e.g. DHDN, EPSG:4314), one needs to calculate them for other historical spatial reference systems (SRS) using identical coordinates. Thanks to @jjimenezshaw there is now an accessible solution in #python 🤩 👇
https://github.com/jjimenezshaw/helmert-calc/ @fidkarten @historicum_net @DHd @oldmapgallery

#Georeferencing #historical #maps by their coordinates is not without its pitfalls. A location in LAT / LON can only be intrepreted with spatial context. While there are some historical proj-strings out there (e.g. DHDN, EPSG:4314), one needs to calculate them for other historical spatial reference systems (SRS) using identical coordinates. Thanks to @jjimenezshaw there is now an accessible solution in #python 🤩 👇
https://github.com/jjimenezshaw/helmert-calc/ @fidkarten @historicum_net @DHd @oldmapgallery

#Georeferencing #historical #maps by their coordinates is not without its pitfalls. A location in LAT / LON can only be intrepreted with spatial context. While there are some historical proj-strings out there (e.g. DHDN, EPSG:4314), one needs to calculate them for other historical spatial reference systems (SRS) using identical coordinates. Thanks to @jjimenezshaw there is now an accessible solution in #python 🤩 👇
https://github.com/jjimenezshaw/helmert-calc/ @fidkarten @historicum_net @DHd @oldmapgallery

#Georeferencing #historical #maps by their coordinates is not without its pitfalls. A location in LAT / LON can only be intrepreted with spatial context. While there are some historical proj-strings out there (e.g. DHDN, EPSG:4314), one needs to calculate them for other historical spatial reference systems (SRS) using identical coordinates. Thanks to @jjimenezshaw there is now an accessible solution in #python 🤩 👇
https://github.com/jjimenezshaw/helmert-calc/ @fidkarten @historicum_net @DHd @oldmapgallery

#Georeferencing #historical #maps by their coordinates is not without its pitfalls. A location in LAT / LON can only be intrepreted with spatial context. While there are some historical proj-strings out there (e.g. DHDN, EPSG:4314), one needs to calculate them for other historical spatial reference systems (SRS) using identical coordinates. Thanks to @jjimenezshaw there is now an accessible solution in #python 🤩 👇
https://github.com/jjimenezshaw/helmert-calc/ @fidkarten @historicum_net @DHd @oldmapgallery

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

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

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

Georeferencing old maps with Allmaps to have a time machine that allows you to take tours of past cities and landscapes #georeferencing

https://www.verbeeld.be/2024/11/17/using-gps-in-the-year-1565/

Georeferencing old maps with Allmaps to have a time machine that allows you to take tours of past cities and landscapes #georeferencing

https://www.verbeeld.be/2024/11/17/using-gps-in-the-year-1565/