#arcticdem — Public Fediverse posts

Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
--
https://doi.org/10.1029/2026EF008578 <-- shared paper
--
https://www.thearcticinstitute.org/climate-change-geopolitics-monitoring-thawing-permafrost/ | https://www.thearcticinstitute.org/dwindling-arctic-sea-ice-impacts-permafrost-health/ <-- shared technical articles
--
https://www.theguardian.com/cities/2016/oct/14/thawing-permafrost-destroying-arctic-cities-norilsk-russia <-- shared media article
--
https://news.grida.no/new-map-shows-extent-of-permafrost-in-northern-hemisphere <-- shared technical article
--
H/T @elias Manos
“Why the increase?
Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
#arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
--
https://doi.org/10.1029/2026EF008578 <-- shared paper
--
https://www.thearcticinstitute.org/climate-change-geopolitics-monitoring-thawing-permafrost/ | https://www.thearcticinstitute.org/dwindling-arctic-sea-ice-impacts-permafrost-health/ <-- shared technical articles
--
https://www.theguardian.com/cities/2016/oct/14/thawing-permafrost-destroying-arctic-cities-norilsk-russia <-- shared media article
--
https://news.grida.no/new-map-shows-extent-of-permafrost-in-northern-hemisphere <-- shared technical article
--
H/T @elias Manos
“Why the increase?
Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
#arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
--
https://doi.org/10.1029/2026EF008578 <-- shared paper
--
https://www.thearcticinstitute.org/climate-change-geopolitics-monitoring-thawing-permafrost/ | https://www.thearcticinstitute.org/dwindling-arctic-sea-ice-impacts-permafrost-health/ <-- shared technical articles
--
https://www.theguardian.com/cities/2016/oct/14/thawing-permafrost-destroying-arctic-cities-norilsk-russia <-- shared media article
--
https://news.grida.no/new-map-shows-extent-of-permafrost-in-northern-hemisphere <-- shared technical article
--
H/T @elias Manos
“Why the increase?
Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
#arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
--
https://doi.org/10.1029/2026EF008578 <-- shared paper
--
https://www.thearcticinstitute.org/climate-change-geopolitics-monitoring-thawing-permafrost/ | https://www.thearcticinstitute.org/dwindling-arctic-sea-ice-impacts-permafrost-health/ <-- shared technical articles
--
https://www.theguardian.com/cities/2016/oct/14/thawing-permafrost-destroying-arctic-cities-norilsk-russia <-- shared media article
--
https://news.grida.no/new-map-shows-extent-of-permafrost-in-northern-hemisphere <-- shared technical article
--
H/T @elias Manos
“Why the increase?
Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
#arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
--
https://doi.org/10.1029/2026EF008578 <-- shared paper
--
https://www.thearcticinstitute.org/climate-change-geopolitics-monitoring-thawing-permafrost/ | https://www.thearcticinstitute.org/dwindling-arctic-sea-ice-impacts-permafrost-health/ <-- shared technical articles
--
https://www.theguardian.com/cities/2016/oct/14/thawing-permafrost-destroying-arctic-cities-norilsk-russia <-- shared media article
--
https://news.grida.no/new-map-shows-extent-of-permafrost-in-northern-hemisphere <-- shared technical article
--
H/T @elias Manos
“Why the increase?
Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
#arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

Satellite data map reveals 33 subglacial lakes beneath the Canadian Arctic

Researchers have created the first map of a network of #subglacial #lakes in the Canadian #Arctic showing 33 bodies of water under glaciers. Using a decade of #ArcticDEM satellite data of Earth's surface height, a team of researchers including the University of Waterloo has developed a method that allowed them to track the draining and filling of active subglacial lakes in unprecedented detail. The team's paper is published in The Cryosphere.

https://phys.org/news/2026-04-satellite-reveals-subglacial-lakes-beneath.html

#Cryophere
#RemoteSensing

Rapid Mass Movements Triggered By An Atmospheric River In Disko Island, West Greenland
--
https://doi.org/10.1002/esp.70201 <-- shared paper, 2023 event(s)
--
https://doi.org/10.1007/s10346-022-01922-7 <-- shared paper, 2021 event(s)
--
https://www.polarresearch.at/the-great-landslide-of-assapaat/ <-- shared technical article
--
#landslide #massmovement #thawing #permafrost #extremewweather #cryosphere #Qeqertarsuaq #ArcticDEM #DiskoIsland #Greenland #ERA5 #CARRA #fluvial #pluvial #precipitation #rainfall #snow #ice #glacier #glacial #temperature #slushflow #GIS #spatial #mapping #geomorphology #elevation #slope #atmosphericriver #climate #weather #snowmelt #island #coast #coastal #damage #humanimpacts #infrastructure #arctic #monitoring #geohazard #risk #hazard #engineeringgeology #polar #change #remotesensing #spatialanalysis #spatiotemporal #earthobservation #debrisflow #remoteareas
#GreenlandEcosystemMonitoring | @europeanspaceagency | #CopernicusECMWF #Copernicus

Rapid Mass Movements Triggered By An Atmospheric River In Disko Island, West Greenland
--
https://doi.org/10.1002/esp.70201 <-- shared paper, 2023 event(s)
--
https://doi.org/10.1007/s10346-022-01922-7 <-- shared paper, 2021 event(s)
--
https://www.polarresearch.at/the-great-landslide-of-assapaat/ <-- shared technical article
--
#landslide #massmovement #thawing #permafrost #extremewweather #cryosphere #Qeqertarsuaq #ArcticDEM #DiskoIsland #Greenland #ERA5 #CARRA #fluvial #pluvial #precipitation #rainfall #snow #ice #glacier #glacial #temperature #slushflow #GIS #spatial #mapping #geomorphology #elevation #slope #atmosphericriver #climate #weather #snowmelt #island #coast #coastal #damage #humanimpacts #infrastructure #arctic #monitoring #geohazard #risk #hazard #engineeringgeology #polar #change #remotesensing #spatialanalysis #spatiotemporal #earthobservation #debrisflow #remoteareas
#GreenlandEcosystemMonitoring | @europeanspaceagency | #CopernicusECMWF #Copernicus

Rapid Mass Movements Triggered By An Atmospheric River In Disko Island, West Greenland
--
https://doi.org/10.1002/esp.70201 <-- shared paper, 2023 event(s)
--
https://doi.org/10.1007/s10346-022-01922-7 <-- shared paper, 2021 event(s)
--
https://www.polarresearch.at/the-great-landslide-of-assapaat/ <-- shared technical article
--
#landslide #massmovement #thawing #permafrost #extremewweather #cryosphere #Qeqertarsuaq #ArcticDEM #DiskoIsland #Greenland #ERA5 #CARRA #fluvial #pluvial #precipitation #rainfall #snow #ice #glacier #glacial #temperature #slushflow #GIS #spatial #mapping #geomorphology #elevation #slope #atmosphericriver #climate #weather #snowmelt #island #coast #coastal #damage #humanimpacts #infrastructure #arctic #monitoring #geohazard #risk #hazard #engineeringgeology #polar #change #remotesensing #spatialanalysis #spatiotemporal #earthobservation #debrisflow #remoteareas
#GreenlandEcosystemMonitoring | @europeanspaceagency | #CopernicusECMWF #Copernicus

Rapid Mass Movements Triggered By An Atmospheric River In Disko Island, West Greenland
--
https://doi.org/10.1002/esp.70201 <-- shared paper, 2023 event(s)
--
https://doi.org/10.1007/s10346-022-01922-7 <-- shared paper, 2021 event(s)
--
https://www.polarresearch.at/the-great-landslide-of-assapaat/ <-- shared technical article
--
#landslide #massmovement #thawing #permafrost #extremewweather #cryosphere #Qeqertarsuaq #ArcticDEM #DiskoIsland #Greenland #ERA5 #CARRA #fluvial #pluvial #precipitation #rainfall #snow #ice #glacier #glacial #temperature #slushflow #GIS #spatial #mapping #geomorphology #elevation #slope #atmosphericriver #climate #weather #snowmelt #island #coast #coastal #damage #humanimpacts #infrastructure #arctic #monitoring #geohazard #risk #hazard #engineeringgeology #polar #change #remotesensing #spatialanalysis #spatiotemporal #earthobservation #debrisflow #remoteareas
#GreenlandEcosystemMonitoring | @europeanspaceagency | #CopernicusECMWF #Copernicus

Rapid Mass Movements Triggered By An Atmospheric River In Disko Island, West Greenland
--
https://doi.org/10.1002/esp.70201 <-- shared paper, 2023 event(s)
--
https://doi.org/10.1007/s10346-022-01922-7 <-- shared paper, 2021 event(s)
--
https://www.polarresearch.at/the-great-landslide-of-assapaat/ <-- shared technical article
--
#landslide #massmovement #thawing #permafrost #extremewweather #cryosphere #Qeqertarsuaq #ArcticDEM #DiskoIsland #Greenland #ERA5 #CARRA #fluvial #pluvial #precipitation #rainfall #snow #ice #glacier #glacial #temperature #slushflow #GIS #spatial #mapping #geomorphology #elevation #slope #atmosphericriver #climate #weather #snowmelt #island #coast #coastal #damage #humanimpacts #infrastructure #arctic #monitoring #geohazard #risk #hazard #engineeringgeology #polar #change #remotesensing #spatialanalysis #spatiotemporal #earthobservation #debrisflow #remoteareas
#GreenlandEcosystemMonitoring | @europeanspaceagency | #CopernicusECMWF #Copernicus

More [Canadian] High-Resolution Lidar [HRDEM] And Elevation Data Now Available
--
https://natural-resources.canada.ca/science-data/science-research/geomatics/more-high-resolution-lidar-elevation-data-now-available <-- shared technical press release
--
“... In this first article, highlights include:
• HRDEM & HRDEM Mosaic - over 709,000 km² of new LiDAR-derived elevation data added since May 2024, increasing coverage by 54%. This product now covers 244 of Canada’s 250 largest cities, and over 95% of the population.
• Northern HRDEM data - fully updated using ArcticDEM v4.1, improving quality for the entire Canadian Arctic.
• Automatically Extracted Buildings - Added 61 new projects and over 2.58 million building footprints, bringing the total to over 13.6 million.
• LiDAR Point Clouds - Expanded by over 200,000 km2, now totalling close to 364,000 km²…”
#GIS #spatial #mapping #Canada #HRDEM #mosaic #LiDAR #elevation #NationalElevationDataStrategy #pointcloud #ArcticDEM #building #footprints #geographic #coverage #progress #opendata #Canadian #arctic #remotesensing #earthobservation #NaturalResourcesCanada

More [Canadian] High-Resolution Lidar [HRDEM] And Elevation Data Now Available
--
https://natural-resources.canada.ca/science-data/science-research/geomatics/more-high-resolution-lidar-elevation-data-now-available <-- shared technical press release
--
“... In this first article, highlights include:
• HRDEM & HRDEM Mosaic - over 709,000 km² of new LiDAR-derived elevation data added since May 2024, increasing coverage by 54%. This product now covers 244 of Canada’s 250 largest cities, and over 95% of the population.
• Northern HRDEM data - fully updated using ArcticDEM v4.1, improving quality for the entire Canadian Arctic.
• Automatically Extracted Buildings - Added 61 new projects and over 2.58 million building footprints, bringing the total to over 13.6 million.
• LiDAR Point Clouds - Expanded by over 200,000 km2, now totalling close to 364,000 km²…”
#GIS #spatial #mapping #Canada #HRDEM #mosaic #LiDAR #elevation #NationalElevationDataStrategy #pointcloud #ArcticDEM #building #footprints #geographic #coverage #progress #opendata #Canadian #arctic #remotesensing #earthobservation #NaturalResourcesCanada

More [Canadian] High-Resolution Lidar [HRDEM] And Elevation Data Now Available
--
https://natural-resources.canada.ca/science-data/science-research/geomatics/more-high-resolution-lidar-elevation-data-now-available <-- shared technical press release
--
“... In this first article, highlights include:
• HRDEM & HRDEM Mosaic - over 709,000 km² of new LiDAR-derived elevation data added since May 2024, increasing coverage by 54%. This product now covers 244 of Canada’s 250 largest cities, and over 95% of the population.
• Northern HRDEM data - fully updated using ArcticDEM v4.1, improving quality for the entire Canadian Arctic.
• Automatically Extracted Buildings - Added 61 new projects and over 2.58 million building footprints, bringing the total to over 13.6 million.
• LiDAR Point Clouds - Expanded by over 200,000 km2, now totalling close to 364,000 km²…”
#GIS #spatial #mapping #Canada #HRDEM #mosaic #LiDAR #elevation #NationalElevationDataStrategy #pointcloud #ArcticDEM #building #footprints #geographic #coverage #progress #opendata #Canadian #arctic #remotesensing #earthobservation #NaturalResourcesCanada

More [Canadian] High-Resolution Lidar [HRDEM] And Elevation Data Now Available
--
https://natural-resources.canada.ca/science-data/science-research/geomatics/more-high-resolution-lidar-elevation-data-now-available <-- shared technical press release
--
“... In this first article, highlights include:
• HRDEM & HRDEM Mosaic - over 709,000 km² of new LiDAR-derived elevation data added since May 2024, increasing coverage by 54%. This product now covers 244 of Canada’s 250 largest cities, and over 95% of the population.
• Northern HRDEM data - fully updated using ArcticDEM v4.1, improving quality for the entire Canadian Arctic.
• Automatically Extracted Buildings - Added 61 new projects and over 2.58 million building footprints, bringing the total to over 13.6 million.
• LiDAR Point Clouds - Expanded by over 200,000 km2, now totalling close to 364,000 km²…”
#GIS #spatial #mapping #Canada #HRDEM #mosaic #LiDAR #elevation #NationalElevationDataStrategy #pointcloud #ArcticDEM #building #footprints #geographic #coverage #progress #opendata #Canadian #arctic #remotesensing #earthobservation #NaturalResourcesCanada

More [Canadian] High-Resolution Lidar [HRDEM] And Elevation Data Now Available
--
https://natural-resources.canada.ca/science-data/science-research/geomatics/more-high-resolution-lidar-elevation-data-now-available <-- shared technical press release
--
“... In this first article, highlights include:
• HRDEM & HRDEM Mosaic - over 709,000 km² of new LiDAR-derived elevation data added since May 2024, increasing coverage by 54%. This product now covers 244 of Canada’s 250 largest cities, and over 95% of the population.
• Northern HRDEM data - fully updated using ArcticDEM v4.1, improving quality for the entire Canadian Arctic.
• Automatically Extracted Buildings - Added 61 new projects and over 2.58 million building footprints, bringing the total to over 13.6 million.
• LiDAR Point Clouds - Expanded by over 200,000 km2, now totalling close to 364,000 km²…”
#GIS #spatial #mapping #Canada #HRDEM #mosaic #LiDAR #elevation #NationalElevationDataStrategy #pointcloud #ArcticDEM #building #footprints #geographic #coverage #progress #opendata #Canadian #arctic #remotesensing #earthobservation #NaturalResourcesCanada

New #DigitalElevationModel data shows Tintina faultine in northern Canada is more geologically active than previously thought and could be due for a major quake #earthquakes https://gogeomatics.ca/canadian-geospatial-digest-for-august-4-2025/ #ArcticDEM #LidarDem

New #DigitalElevationModel data shows Tintina faultine in northern Canada is more geologically active than previously thought and could be due for a major quake #earthquakes https://gogeomatics.ca/canadian-geospatial-digest-for-august-4-2025/ #ArcticDEM #LidarDem

Just heard on #Cryolist that the Polar Geospatial center at U. of Minnesota is losing their funding. These guys have a huge range of data products including super high resolution digital elevation models of both Arctic and Antarctica.

(#ArcticDEM + #REMA )

This is just naked vandalism.

Just heard on #Cryolist that the Polar Geospatial center at U. of Minnesota is losing their funding. These guys have a huge range of data products including super high resolution digital elevation models of both Arctic and Antarctica.

(#ArcticDEM + #REMA )

This is just naked vandalism.

There goes the Polar Geospatial Center too. Goodness only knows what this means for #ArcticDEM + #REMA? www.pgc.umn.edu/news/suspend...

Does #OSMAnd have a bug/feature bounty program that one can sponsor?

Because the heightmap issue is annoying me a surprising amount and I alas have other stuff I need to focus on ...

So if someone feels like converting the #ArcticDEM data set into a 10x10m heightmap layer that works with Osmand, do get in touch 🙂

https://github.com/osmandapp/OsmAnd/issues/18992

#OpenSource #BugBounty #FeatureBounty

Does #OSMAnd have a bug/feature bounty program that one can sponsor?

Because the heightmap issue is annoying me a surprising amount and I alas have other stuff I need to focus on ...

So if someone feels like converting the #ArcticDEM data set into a 10x10m heightmap layer that works with Osmand, do get in touch 🙂

https://github.com/osmandapp/OsmAnd/issues/18992

#OpenSource #BugBounty #FeatureBounty

Arcane gdal incantations, undocumented #OsmAnd processing scripts, Gigabytes of #ArcticDEM data vs one disgruntled me.

Let's see who fails first.

Arcane gdal incantations, undocumented #OsmAnd processing scripts, Gigabytes of #ArcticDEM data vs one disgruntled me.

Let's see who fails first.

The terrain / slope / contour lines data for #Hornstrandir in #OSMAnd is still wrong.

Their support (Pro subscriber) refers me to #OpenStreetMap. OSM says "Terrain data is another data set, no can do".

According to OSMAnd's docs, this is from #ArcticDEM. Which doesn't have this artifact in their online explorer.

When I search the web on what to do, my first hit is my old post on Mastodon asking for what to do.

_rubs temples_

The terrain / slope / contour lines data for #Hornstrandir in #OSMAnd is still wrong.

Their support (Pro subscriber) refers me to #OpenStreetMap. OSM says "Terrain data is another data set, no can do".

According to OSMAnd's docs, this is from #ArcticDEM. Which doesn't have this artifact in their online explorer.

When I search the web on what to do, my first hit is my old post on Mastodon asking for what to do.

_rubs temples_

#30DayMapChallenge, Day 1: Points. Lighthouses of the Arctic Circle. Data from #OpenStreetMap and #ArcticDem. More lighthouses, yes, but different, I hope.

#rayshader adventures, an #rstats tale

#30DayMapChallenge, Day 1: Points. Lighthouses of the Arctic Circle. Data from #OpenStreetMap and #ArcticDem. More lighthouses, yes, but different, I hope.

#rayshader adventures, an #rstats tale

#earthArt #SciArt

https://mapstodon.space/@g_fiske/111286164346534045
[email protected] - Downloading some of the new #ArcticDEM data for enhancing our abrupt thaw mapping models... couldn't help but to stop and take a 👀 at few rivers.
#screenshot

#earthArt #SciArt

https://mapstodon.space/@g_fiske/111286164346534045
[email protected] - Downloading some of the new #ArcticDEM data for enhancing our abrupt thaw mapping models... couldn't help but to stop and take a 👀 at few rivers.
#screenshot

Downloading some of the new #ArcticDEM data for enhancing our abrupt thaw mapping models... couldn't help but to stop and take a 👀 at few rivers.
#screenshot

Downloading some of the new #ArcticDEM data for enhancing our abrupt thaw mapping models... couldn't help but to stop and take a 👀 at few rivers.
#screenshot

I think it's a really nice example of how you can combine very diverse datasets, which at first might not seem to have much to do with each other, to get a deep understanding of a key process. First, @agrinsted used a published database of crevasses based on a satellite DEM over the Arctic (#ArcticDEM), combined this with temperatures from the new very high resolution regional #Climate reanalysis (#CARRA), plus ice thickness data from #BedMachine + #IceVelocity data from many many #satellites

I think it's a really nice example of how you can combine very diverse datasets, which at first might not seem to have much to do with each other, to get a deep understanding of a key process. First, @agrinsted used a published database of crevasses based on a satellite DEM over the Arctic (#ArcticDEM), combined this with temperatures from the new very high resolution regional #Climate reanalysis (#CARRA), plus ice thickness data from #BedMachine + #IceVelocity data from many many #satellites

I think it's a really nice example of how you can combine very diverse datasets, which at first might not seem to have much to do with each other, to get a deep understanding of a key process. First, @agrinsted used a published database of crevasses based on a satellite DEM over the Arctic (#ArcticDEM), combined this with temperatures from the new very high resolution regional #Climate reanalysis (#CARRA), plus ice thickness data from #BedMachine + #IceVelocity data from many many #satellites

I think it's a really nice example of how you can combine very diverse datasets, which at first might not seem to have much to do with each other, to get a deep understanding of a key process. First, @agrinsted used a published database of crevasses based on a satellite DEM over the Arctic (#ArcticDEM), combined this with temperatures from the new very high resolution regional #Climate reanalysis (#CARRA), plus ice thickness data from #BedMachine + #IceVelocity data from many many #satellites

I think it's a really nice example of how you can combine very diverse datasets, which at first might not seem to have much to do with each other, to get a deep understanding of a key process. First, @agrinsted used a published database of crevasses based on a satellite DEM over the Arctic (#ArcticDEM), combined this with temperatures from the new very high resolution regional #Climate reanalysis (#CARRA), plus ice thickness data from #BedMachine + #IceVelocity data from many many #satellites