home.social

#geoai — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #geoai, aggregated by home.social.

  1. Mbongowo J Mbuh @[email protected] ·

    A city is a pattern. 🏙️ From above, roads, rooftops, and sprawl become data, and geospatial AI learns to read it, mapping how we build and where we grow, block by block. This is how machines see the places we live. 🛰️

    #GeoAI #RemoteSensing #Urban #SmartCities #Satellites #Geography

    #geoai #remotesensing #urban #smartcities #satellites #geography
  2. 💧🌏 Greg Cocks @[email protected] ·

    Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome
    --
    openscholar.uga.edu/record/269 <-- shared technical publication / dissertation
    --
    aag.org/award-grant/william-l- <-- shared @AAG William L. Garrison Award for Best Dissertation in Computational Geography
    --
    [again, way outside any expertise I might have, but fascinating spatial analysis use case…]
    H/T @Jielu Zhang | Postdoctoral Researcher @ Harvard University
    “[The authors] Ph.D. dissertation "Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome" [1st link above] has received the 2026 biennial William L. Garrison Award for Best Dissertation in Computational Geography from the American Association of Geographers… [2nd link above.]
    In [their] research, [they] develop[ed] Explainable GeoAI and Causal GeoAI methods that combine geographic data and artificial intelligence to expose and ameliorate health disparities by delivering models that not only predict risks but also illuminate how and where to intervene. While [their] dissertation focused on cardiovascular disease, these approaches are broadly applicable to public health, medicine, urban planning, environmental exposure, and resilience research…”
    #explainable #causal #AI #model #modeling #PublicHealth #GIS #spatial #mapping #spatialanalysis #spatiotemporal #AAG2026 #AAG #Award #geostatistics #Georgia #health #risk #hazard #cardiacarrest #cardiovacscular #usecase #metrics #midocine #urbanplanning #resilience #survival #OutofHospital #PhD #Dissertation #CardiacArrest #AutomatedExternalDefibrillator #SpatialOptimization #GeographicallyExplainableArtificialIntelligence #GeoAI #GeoXAI #SpatiallyAwareCausalInference #OverlayedSpatioTemporalOptimization #healthcare #medical #intervention #GIS #spatial #mappingt #spatialanalysis #spatiotemporal #heart #heartattack #AED #survival #survivaloutcomes #machinelearning #AI #publichealth #healthgeographers #counterfactual #explainable #deeplearning #model #modeling

    #explainable #causal #ai #model #modeling #publichealth
  3. 💧🌏 Greg Cocks @[email protected] ·

    Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome
    --
    openscholar.uga.edu/record/269 <-- shared technical publication / dissertation
    --
    aag.org/award-grant/william-l- <-- shared @AAG William L. Garrison Award for Best Dissertation in Computational Geography
    --
    [again, way outside any expertise I might have, but fascinating spatial analysis use case…]
    H/T @Jielu Zhang | Postdoctoral Researcher @ Harvard University
    “[The authors] Ph.D. dissertation "Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome" [1st link above] has received the 2026 biennial William L. Garrison Award for Best Dissertation in Computational Geography from the American Association of Geographers… [2nd link above.]
    In [their] research, [they] develop[ed] Explainable GeoAI and Causal GeoAI methods that combine geographic data and artificial intelligence to expose and ameliorate health disparities by delivering models that not only predict risks but also illuminate how and where to intervene. While [their] dissertation focused on cardiovascular disease, these approaches are broadly applicable to public health, medicine, urban planning, environmental exposure, and resilience research…”
    #explainable #causal #AI #model #modeling #PublicHealth #GIS #spatial #mapping #spatialanalysis #spatiotemporal #AAG2026 #AAG #Award #geostatistics #Georgia #health #risk #hazard #cardiacarrest #cardiovacscular #usecase #metrics #midocine #urbanplanning #resilience #survival #OutofHospital #PhD #Dissertation #CardiacArrest #AutomatedExternalDefibrillator #SpatialOptimization #GeographicallyExplainableArtificialIntelligence #GeoAI #GeoXAI #SpatiallyAwareCausalInference #OverlayedSpatioTemporalOptimization #healthcare #medical #intervention #GIS #spatial #mappingt #spatialanalysis #spatiotemporal #heart #heartattack #AED #survival #survivaloutcomes #machinelearning #AI #publichealth #healthgeographers #counterfactual #explainable #deeplearning #model #modeling

    #explainable #causal #ai #model #modeling #publichealth
  4. 💧🌏 Greg Cocks @[email protected] ·

    Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome
    --
    openscholar.uga.edu/record/269 <-- shared technical publication / dissertation
    --
    aag.org/award-grant/william-l- <-- shared @AAG William L. Garrison Award for Best Dissertation in Computational Geography
    --
    [again, way outside any expertise I might have, but fascinating spatial analysis use case…]
    H/T @Jielu Zhang | Postdoctoral Researcher @ Harvard University
    “[The authors] Ph.D. dissertation "Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome" [1st link above] has received the 2026 biennial William L. Garrison Award for Best Dissertation in Computational Geography from the American Association of Geographers… [2nd link above.]
    In [their] research, [they] develop[ed] Explainable GeoAI and Causal GeoAI methods that combine geographic data and artificial intelligence to expose and ameliorate health disparities by delivering models that not only predict risks but also illuminate how and where to intervene. While [their] dissertation focused on cardiovascular disease, these approaches are broadly applicable to public health, medicine, urban planning, environmental exposure, and resilience research…”
    #explainable #causal #AI #model #modeling #PublicHealth #GIS #spatial #mapping #spatialanalysis #spatiotemporal #AAG2026 #AAG #Award #geostatistics #Georgia #health #risk #hazard #cardiacarrest #cardiovacscular #usecase #metrics #midocine #urbanplanning #resilience #survival #OutofHospital #PhD #Dissertation #CardiacArrest #AutomatedExternalDefibrillator #SpatialOptimization #GeographicallyExplainableArtificialIntelligence #GeoAI #GeoXAI #SpatiallyAwareCausalInference #OverlayedSpatioTemporalOptimization #healthcare #medical #intervention #GIS #spatial #mappingt #spatialanalysis #spatiotemporal #heart #heartattack #AED #survival #survivaloutcomes #machinelearning #AI #publichealth #healthgeographers #counterfactual #explainable #deeplearning #model #modeling

    #explainable #causal #ai #model #modeling #publichealth
  5. 💧🌏 Greg Cocks @[email protected] ·

    Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome
    --
    openscholar.uga.edu/record/269 <-- shared technical publication / dissertation
    --
    aag.org/award-grant/william-l- <-- shared @AAG William L. Garrison Award for Best Dissertation in Computational Geography
    --
    [again, way outside any expertise I might have, but fascinating spatial analysis use case…]
    H/T @Jielu Zhang | Postdoctoral Researcher @ Harvard University
    “[The authors] Ph.D. dissertation "Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome" [1st link above] has received the 2026 biennial William L. Garrison Award for Best Dissertation in Computational Geography from the American Association of Geographers… [2nd link above.]
    In [their] research, [they] develop[ed] Explainable GeoAI and Causal GeoAI methods that combine geographic data and artificial intelligence to expose and ameliorate health disparities by delivering models that not only predict risks but also illuminate how and where to intervene. While [their] dissertation focused on cardiovascular disease, these approaches are broadly applicable to public health, medicine, urban planning, environmental exposure, and resilience research…”
    #explainable #causal #AI #model #modeling #PublicHealth #GIS #spatial #mapping #spatialanalysis #spatiotemporal #AAG2026 #AAG #Award #geostatistics #Georgia #health #risk #hazard #cardiacarrest #cardiovacscular #usecase #metrics #midocine #urbanplanning #resilience #survival #OutofHospital #PhD #Dissertation #CardiacArrest #AutomatedExternalDefibrillator #SpatialOptimization #GeographicallyExplainableArtificialIntelligence #GeoAI #GeoXAI #SpatiallyAwareCausalInference #OverlayedSpatioTemporalOptimization #healthcare #medical #intervention #GIS #spatial #mappingt #spatialanalysis #spatiotemporal #heart #heartattack #AED #survival #survivaloutcomes #machinelearning #AI #publichealth #healthgeographers #counterfactual #explainable #deeplearning #model #modeling

    #deeplearning #counterfactual #healthgeographers #machinelearning #survivaloutcomes #aed
  6. 💧🌏 Greg Cocks @GregCocks ·

    Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome
    --
    openscholar.uga.edu/record/269 <-- shared technical publication / dissertation
    --
    aag.org/award-grant/william-l- <-- shared @AAG William L. Garrison Award for Best Dissertation in Computational Geography
    --
    [again, way outside any expertise I might have, but fascinating spatial analysis use case…]
    H/T @Jielu Zhang | Postdoctoral Researcher @ Harvard University
    “[The authors] Ph.D. dissertation "Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome" [1st link above] has received the 2026 biennial William L. Garrison Award for Best Dissertation in Computational Geography from the American Association of Geographers… [2nd link above.]
    In [their] research, [they] develop[ed] Explainable GeoAI and Causal GeoAI methods that combine geographic data and artificial intelligence to expose and ameliorate health disparities by delivering models that not only predict risks but also illuminate how and where to intervene. While [their] dissertation focused on cardiovascular disease, these approaches are broadly applicable to public health, medicine, urban planning, environmental exposure, and resilience research…”

    #explainable #causal #ai #model #modeling #publichealth
  7. hansakwast @[email protected] ·

    Checking out the Bunting Labs AI Vectorizer Plugin for QGIS

    videos.qwast-gis.com/w/iuLymTy

    #ai #geoai #qgis #digitize
  8. 💧🌏 Greg Cocks @[email protected] ·

    Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

    #kinnaur #permafrost #distribution #gis #spatial #mapping
  9. 💧🌏 Greg Cocks @[email protected] ·

    Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

    #kinnaur #permafrost #distribution #gis #spatial #mapping
  10. 💧🌏 Greg Cocks @[email protected] ·

    Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

    #kinnaur #permafrost #distribution #gis #spatial #mapping
  11. 💧🌏 Greg Cocks @[email protected] ·

    Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

    #avalanche #downstream #publicsafety #hep #infrastructure #riskassessment
  12. 💧🌏 Greg Cocks @GregCocks ·

    Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”

    #kinnaur #permafrost #distribution #gis #spatial #mapping
  13. 💧🌏 Greg Cocks @[email protected] ·

    Spatial Signals [podcast]
    --
    americaview.podbean.com/ <-- shared podcast entry page
    --
    americaview.substack.com/ <-- shared Spatial Signals Substack page
    --
    [note that I am not associated with this podcast in any way, and this post should not be considered an endorsement]
    “Spatial Signals is a podcast about remote sensing, GIS, and geospatial technologies - but more importantly, it’s a podcast about the people who use and apply them. This podcast is sponsored by AmericaView, the national remote sensing network dedicated to empowering Earth Observation…”
    #SpatialSignals #AmericaView #Geospatial #RemoteSensing #GIS #spatial #mapping #GeoAI #OpenSource #podcast #remotesensing #earthobservation #spatialprofessionals #SMEs #socialmedia #GISgeeks #usecase #publicgood #spatialanalysis #spatiotemporal #model #modeling
    @SpatialSignals | @Youngstown State University | @AmericaView

    #spatialsignals #americaview #geospatial #remotesensing #gis #spatial
  14. Thierry Joliveau @[email protected] ·

    Dans le Michigan, des vélos équipés d'un système "léger" de caméra + IA pour cartographier les endroits à risque d'accident et programmer ou adapter des pistes cyclables. L'article aborde les problèmes associés à cette solution HighTech et les réticences observées.
    Note : le gouvernement Trump vient d'effacer les pages Web de l'organisme fédéral qui finançait cette initiative.
    #GeoAI #Bike #vélo #GéoIA
    urbanists.social/@TrufiAssoc/1

    #geoai #bike #velo #geoia