#georeferencing — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #georeferencing, aggregated by home.social.
-
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!”
https://rbfirehose.com/2026/09/13/introducing-mapsnap-automated-georeferencing-for-historic-sanborn-insurance-maps-danvk/ -
UNC-Chapel Hill study shows AI can dramatically speed up digitizing natural history collections – EurekAlert!
News Release 5-Dec-2025
Image: UNC research team check a plant specimen at the UNC Herbarium. view more Credit: Shanna OberreiterUNC-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!
Tags: AI, artificial intelligence, Biology Department, Carolina Team, Collections, Digitize Content, EurekAlert!, Georeferencing, Large Language Models (LLM), LLMs, Natural History, Nature, UNC-Chapel Hill, Xiao Feng, Yuyang Xie#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
Image: UNC research team check a plant specimen at the UNC Herbarium. view more Credit: Shanna OberreiterUNC-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!
Tags: AI, artificial intelligence, Biology Department, Carolina Team, Collections, Digitize Content, EurekAlert!, Georeferencing, Large Language Models (LLM), LLMs, Natural History, Nature, UNC-Chapel Hill, Xiao Feng, Yuyang Xie#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
Image: UNC research team check a plant specimen at the UNC Herbarium. view more Credit: Shanna OberreiterUNC-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!
Tags: AI, artificial intelligence, Biology Department, Carolina Team, Collections, Digitize Content, EurekAlert!, Georeferencing, Large Language Models (LLM), LLMs, Natural History, Nature, UNC-Chapel Hill, Xiao Feng, Yuyang Xie#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
Image: UNC research team check a plant specimen at the UNC Herbarium. view more Credit: Shanna OberreiterUNC-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
Image: UNC research team check a plant specimen at the UNC Herbarium. view more Credit: Shanna OberreiterUNC-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
-
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 🤣
-
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
🌺
🏷️ #OLC #QTH #SemanticFediverse #ActivityPub #Mastodon #Friendica #Pixelfed #Fediverse #Geocode #Georcoding #Georeferencing #OpenLocationCode #PlusCode #Maidenhead #HamRadio #AFU #CBFunk #CitizenBand #CiBi #ActivityVocabulary
-
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
-
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
-
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