#hazard — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #hazard, aggregated by home.social.
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Coastal Flood Exposure Mapper USA, NOAA]
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https://coast.noaa.gov/floodexposure/ <-- shared NOAA page/resource
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H/T @joe Trimboli
“🌊 Sea-Level Rise Is More Than a Rising Waterline
One thing [the H/T has] noticed… is how much more NOAA data is now being maintained, organized, and made accessible for analysis…
That matters when we look at sea-level rise.
Modern sea-level mapping is moving beyond simply adding one, three, or six feet of water to a coastline. Current science considers multiple interacting processes, including ocean warming, ice-sheet and glacier loss, regional ocean circulation, vertical land movement, tides, storm surge, and local terrain.
The NOAA Sea Level Rise Viewer brings many of these concepts together spatially, allowing users to examine potential inundation under different scenarios.
For [them], the larger GIS lesson is that the value is not necessarily in finding one perfect map. It is in having enough maintained, interoperable data to build a more complete understanding of the system.
Sea-level rise can therefore be viewed as a geospatial system of evidence connecting observations, projections, elevation, tidal conditions, hydraulic connectivity, flooding frequency, and uncertainty.
The more [they] work with these datasets, the more [the H/T] see[s] GIS shifting from simply making maps toward integrating continually expanding bodies of scientific evidence into a spatial model of change…”
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“Jumpstart community discussions about local coastal flooding hazards by developing maps that show the people, places, and natural resources at risk...”
--
“This online visualization tool supports communities that are assessing their coastal hazard risks and vulnerabilities. The tool creates a collection of user-defined maps that show the people, places, and natural resources exposed to coastal flooding. The maps can be saved, downloaded, or shared to communicate flood exposure and potential impacts. In addition, the tool provides guidance for using these maps to engage community members and stakeholders. The current geography includes the East Coast, West Coast, Gulf of America, Great Lakes, and islands in the Pacific and Caribbean.
FEATURES:
• Visualize people, places, and natural resources exposed to coastal flood hazards
• Share online maps to communicate with and engage stakeholders…”
#GIS #SeaLevelRise #NOAA #CoastalResilience #GeospatialScience USA #coast #coastal #spatial #mapping #opendata #fedscience #publicgood #risk #hazard #earlywarning #cost #economics #infrastructure #sealevel #SLR #model #modeling #humanimpacts #naturalhazard #mitigation #NOAASeaLevelRiseViewer #spatialanalysis #spatiotemporal #inundation #interoperable #observations #monitoring #projections #elevation #tidal #tide #hydraulicconnectivity #flood #flooding #frequency #uncertainty
@NOAA -
Coastal Flood Exposure Mapper USA, NOAA]
--
https://coast.noaa.gov/floodexposure/ <-- shared NOAA page/resource
--
H/T @joe Trimboli
“🌊 Sea-Level Rise Is More Than a Rising Waterline
One thing [the H/T has] noticed… is how much more NOAA data is now being maintained, organized, and made accessible for analysis…
That matters when we look at sea-level rise.
Modern sea-level mapping is moving beyond simply adding one, three, or six feet of water to a coastline. Current science considers multiple interacting processes, including ocean warming, ice-sheet and glacier loss, regional ocean circulation, vertical land movement, tides, storm surge, and local terrain.
The NOAA Sea Level Rise Viewer brings many of these concepts together spatially, allowing users to examine potential inundation under different scenarios.
For [them], the larger GIS lesson is that the value is not necessarily in finding one perfect map. It is in having enough maintained, interoperable data to build a more complete understanding of the system.
Sea-level rise can therefore be viewed as a geospatial system of evidence connecting observations, projections, elevation, tidal conditions, hydraulic connectivity, flooding frequency, and uncertainty.
The more [they] work with these datasets, the more [the H/T] see[s] GIS shifting from simply making maps toward integrating continually expanding bodies of scientific evidence into a spatial model of change…”
--
“Jumpstart community discussions about local coastal flooding hazards by developing maps that show the people, places, and natural resources at risk...”
--
“This online visualization tool supports communities that are assessing their coastal hazard risks and vulnerabilities. The tool creates a collection of user-defined maps that show the people, places, and natural resources exposed to coastal flooding. The maps can be saved, downloaded, or shared to communicate flood exposure and potential impacts. In addition, the tool provides guidance for using these maps to engage community members and stakeholders. The current geography includes the East Coast, West Coast, Gulf of America, Great Lakes, and islands in the Pacific and Caribbean.
FEATURES:
• Visualize people, places, and natural resources exposed to coastal flood hazards
• Share online maps to communicate with and engage stakeholders…”
#GIS #SeaLevelRise #NOAA #CoastalResilience #GeospatialScience USA #coast #coastal #spatial #mapping #opendata #fedscience #publicgood #risk #hazard #earlywarning #cost #economics #infrastructure #sealevel #SLR #model #modeling #humanimpacts #naturalhazard #mitigation #NOAASeaLevelRiseViewer #spatialanalysis #spatiotemporal #inundation #interoperable #observations #monitoring #projections #elevation #tidal #tide #hydraulicconnectivity #flood #flooding #frequency #uncertainty
@NOAA -
Coastal Flood Exposure Mapper USA, NOAA]
--
https://coast.noaa.gov/floodexposure/ <-- shared NOAA page/resource
--
H/T @joe Trimboli
“🌊 Sea-Level Rise Is More Than a Rising Waterline
One thing [the H/T has] noticed… is how much more NOAA data is now being maintained, organized, and made accessible for analysis…
That matters when we look at sea-level rise.
Modern sea-level mapping is moving beyond simply adding one, three, or six feet of water to a coastline. Current science considers multiple interacting processes, including ocean warming, ice-sheet and glacier loss, regional ocean circulation, vertical land movement, tides, storm surge, and local terrain.
The NOAA Sea Level Rise Viewer brings many of these concepts together spatially, allowing users to examine potential inundation under different scenarios.
For [them], the larger GIS lesson is that the value is not necessarily in finding one perfect map. It is in having enough maintained, interoperable data to build a more complete understanding of the system.
Sea-level rise can therefore be viewed as a geospatial system of evidence connecting observations, projections, elevation, tidal conditions, hydraulic connectivity, flooding frequency, and uncertainty.
The more [they] work with these datasets, the more [the H/T] see[s] GIS shifting from simply making maps toward integrating continually expanding bodies of scientific evidence into a spatial model of change…”
--
“Jumpstart community discussions about local coastal flooding hazards by developing maps that show the people, places, and natural resources at risk...”
--
“This online visualization tool supports communities that are assessing their coastal hazard risks and vulnerabilities. The tool creates a collection of user-defined maps that show the people, places, and natural resources exposed to coastal flooding. The maps can be saved, downloaded, or shared to communicate flood exposure and potential impacts. In addition, the tool provides guidance for using these maps to engage community members and stakeholders. The current geography includes the East Coast, West Coast, Gulf of America, Great Lakes, and islands in the Pacific and Caribbean.
FEATURES:
• Visualize people, places, and natural resources exposed to coastal flood hazards
• Share online maps to communicate with and engage stakeholders…”
#GIS #SeaLevelRise #NOAA #CoastalResilience #GeospatialScience USA #coast #coastal #spatial #mapping #opendata #fedscience #publicgood #risk #hazard #earlywarning #cost #economics #infrastructure #sealevel #SLR #model #modeling #humanimpacts #naturalhazard #mitigation #NOAASeaLevelRiseViewer #spatialanalysis #spatiotemporal #inundation #interoperable #observations #monitoring #projections #elevation #tidal #tide #hydraulicconnectivity #flood #flooding #frequency #uncertainty
@NOAA -
Coastal Flood Exposure Mapper USA, NOAA]
--
https://coast.noaa.gov/floodexposure/ <-- shared NOAA page/resource
--
H/T @joe Trimboli
“🌊 Sea-Level Rise Is More Than a Rising Waterline
One thing [the H/T has] noticed… is how much more NOAA data is now being maintained, organized, and made accessible for analysis…
That matters when we look at sea-level rise.
Modern sea-level mapping is moving beyond simply adding one, three, or six feet of water to a coastline. Current science considers multiple interacting processes, including ocean warming, ice-sheet and glacier loss, regional ocean circulation, vertical land movement, tides, storm surge, and local terrain.
The NOAA Sea Level Rise Viewer brings many of these concepts together spatially, allowing users to examine potential inundation under different scenarios.
For [them], the larger GIS lesson is that the value is not necessarily in finding one perfect map. It is in having enough maintained, interoperable data to build a more complete understanding of the system.
Sea-level rise can therefore be viewed as a geospatial system of evidence connecting observations, projections, elevation, tidal conditions, hydraulic connectivity, flooding frequency, and uncertainty.
The more [they] work with these datasets, the more [the H/T] see[s] GIS shifting from simply making maps toward integrating continually expanding bodies of scientific evidence into a spatial model of change…”
--
“Jumpstart community discussions about local coastal flooding hazards by developing maps that show the people, places, and natural resources at risk...”
--
“This online visualization tool supports communities that are assessing their coastal hazard risks and vulnerabilities. The tool creates a collection of user-defined maps that show the people, places, and natural resources exposed to coastal flooding. The maps can be saved, downloaded, or shared to communicate flood exposure and potential impacts. In addition, the tool provides guidance for using these maps to engage community members and stakeholders. The current geography includes the East Coast, West Coast, Gulf of America, Great Lakes, and islands in the Pacific and Caribbean.
FEATURES:
• Visualize people, places, and natural resources exposed to coastal flood hazards
• Share online maps to communicate with and engage stakeholders…”
#GIS #SeaLevelRise #NOAA #CoastalResilience #GeospatialScience USA #coast #coastal #spatial #mapping #opendata #fedscience #publicgood #risk #hazard #earlywarning #cost #economics #infrastructure #sealevel #SLR #model #modeling #humanimpacts #naturalhazard #mitigation #NOAASeaLevelRiseViewer #spatialanalysis #spatiotemporal #inundation #interoperable #observations #monitoring #projections #elevation #tidal #tide #hydraulicconnectivity #flood #flooding #frequency #uncertainty
@NOAA -
GeoAI For Infrastructure Resilience - Mapping Exposure To Invasive Albizia Trees In Hawai’i
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https://doi.org/10.3390/su181910061 <-- shared paper
--
https://dlnr.hawaii.gov/hisc/info/invasive-species-profiles/albizia/ <-- shared link, Albizia,
Hawaii Invasive Species Council (HISC)
--
H/T @Jolie Wanger
“By integrating scalable invasive-tree detection with infrastructure exposure assessment, the framework provides a spatial decision-support tool for prioritizing vegetation management, hazard mitigation, and infrastructure maintenance…”
--
“Invasive albizia (Falcataria falcata (L.) Greuter & R. Rankin) trees pose an increasing threat to infrastructure, transportation networks, and public safety in Hawai’i because their rapid growth and shallow root systems make them susceptible to windthrow during severe weather events. However, comprehensive spatial information on albizia distribution remains limited, constraining invasive-species management, hazard mitigation, and sustainable infrastructure planning. This study evaluates the applicability of a GeoAI-based framework for identifying invasive-tree-related infrastructure exposure at a regional planning scale. This study develops a GeoAI framework using a U-Net convolutional neural network with a ResNet-34 backbone to detect and map albizia canopy from 0.6 m National Agriculture Imagery Program (NAIP) aerial imagery. Training data from two geographically distinct areas, Mānoa and Kahalu’u, were used for model development and iterative refinement. The final model achieved a precision of 0.76, compared with 0.64 in the initial iteration, and was applied to estimate potential tree-fall exposure through spatial proximity analysis of roads and buildings. Following manual quality control, approximately 2 km2 of albizia canopy were identified within the study area. More than 53 km of roads and 2300 buildings were located within the potential tree-fall exposure zone, including portions of major transportation corridors such as Pali Highway, Likelike Highway, and Interstate H-3. By integrating scalable invasive-tree detection with infrastructure exposure assessment, the framework provides a spatial decision-support tool for prioritizing vegetation management, hazard mitigation, and infrastructure maintenance. The approach contributes to sustainability by supporting more targeted use of management resources, reducing potential disruption to critical infrastructure and essential access, and strengthening long-term community and infrastructure resilience…”
#GeoAI #sustainableinfrastructure #infrastructureresilience #invasivespecies #management #Albizia #deeplearning #infrastructureexposure #disasterpreparedness #climateadaptation #climatechange #decisionsupport #planning #policy #GIS #spatial #mapping #model #treedetection #AI #Hawai’i #Hawaii #rapidgrowth #shallowrootsystem #windthrow #windfall #vegetation #nonnative #risk #hazard #infrastructure #economics #mitigation #vegetationmanagement #infrastructuremaintenance #utilities #extremeweather #wind #storm -
GeoAI For Infrastructure Resilience - Mapping Exposure To Invasive Albizia Trees In Hawai’i
--
https://doi.org/10.3390/su181910061 <-- shared paper
--
https://dlnr.hawaii.gov/hisc/info/invasive-species-profiles/albizia/ <-- shared link, Albizia,
Hawaii Invasive Species Council (HISC)
--
H/T @Jolie Wanger
“By integrating scalable invasive-tree detection with infrastructure exposure assessment, the framework provides a spatial decision-support tool for prioritizing vegetation management, hazard mitigation, and infrastructure maintenance…”
--
“Invasive albizia (Falcataria falcata (L.) Greuter & R. Rankin) trees pose an increasing threat to infrastructure, transportation networks, and public safety in Hawai’i because their rapid growth and shallow root systems make them susceptible to windthrow during severe weather events. However, comprehensive spatial information on albizia distribution remains limited, constraining invasive-species management, hazard mitigation, and sustainable infrastructure planning. This study evaluates the applicability of a GeoAI-based framework for identifying invasive-tree-related infrastructure exposure at a regional planning scale. This study develops a GeoAI framework using a U-Net convolutional neural network with a ResNet-34 backbone to detect and map albizia canopy from 0.6 m National Agriculture Imagery Program (NAIP) aerial imagery. Training data from two geographically distinct areas, Mānoa and Kahalu’u, were used for model development and iterative refinement. The final model achieved a precision of 0.76, compared with 0.64 in the initial iteration, and was applied to estimate potential tree-fall exposure through spatial proximity analysis of roads and buildings. Following manual quality control, approximately 2 km2 of albizia canopy were identified within the study area. More than 53 km of roads and 2300 buildings were located within the potential tree-fall exposure zone, including portions of major transportation corridors such as Pali Highway, Likelike Highway, and Interstate H-3. By integrating scalable invasive-tree detection with infrastructure exposure assessment, the framework provides a spatial decision-support tool for prioritizing vegetation management, hazard mitigation, and infrastructure maintenance. The approach contributes to sustainability by supporting more targeted use of management resources, reducing potential disruption to critical infrastructure and essential access, and strengthening long-term community and infrastructure resilience…”
#GeoAI #sustainableinfrastructure #infrastructureresilience #invasivespecies #management #Albizia #deeplearning #infrastructureexposure #disasterpreparedness #climateadaptation #climatechange #decisionsupport #planning #policy #GIS #spatial #mapping #model #treedetection #AI #Hawai’i #Hawaii #rapidgrowth #shallowrootsystem #windthrow #windfall #vegetation #nonnative #risk #hazard #infrastructure #economics #mitigation #vegetationmanagement #infrastructuremaintenance #utilities #extremeweather #wind #storm -
GeoAI For Infrastructure Resilience - Mapping Exposure To Invasive Albizia Trees In Hawai’i
--
https://doi.org/10.3390/su181910061 <-- shared paper
--
https://dlnr.hawaii.gov/hisc/info/invasive-species-profiles/albizia/ <-- shared link, Albizia,
Hawaii Invasive Species Council (HISC)
--
H/T @Jolie Wanger
“By integrating scalable invasive-tree detection with infrastructure exposure assessment, the framework provides a spatial decision-support tool for prioritizing vegetation management, hazard mitigation, and infrastructure maintenance…”
--
“Invasive albizia (Falcataria falcata (L.) Greuter & R. Rankin) trees pose an increasing threat to infrastructure, transportation networks, and public safety in Hawai’i because their rapid growth and shallow root systems make them susceptible to windthrow during severe weather events. However, comprehensive spatial information on albizia distribution remains limited, constraining invasive-species management, hazard mitigation, and sustainable infrastructure planning. This study evaluates the applicability of a GeoAI-based framework for identifying invasive-tree-related infrastructure exposure at a regional planning scale. This study develops a GeoAI framework using a U-Net convolutional neural network with a ResNet-34 backbone to detect and map albizia canopy from 0.6 m National Agriculture Imagery Program (NAIP) aerial imagery. Training data from two geographically distinct areas, Mānoa and Kahalu’u, were used for model development and iterative refinement. The final model achieved a precision of 0.76, compared with 0.64 in the initial iteration, and was applied to estimate potential tree-fall exposure through spatial proximity analysis of roads and buildings. Following manual quality control, approximately 2 km2 of albizia canopy were identified within the study area. More than 53 km of roads and 2300 buildings were located within the potential tree-fall exposure zone, including portions of major transportation corridors such as Pali Highway, Likelike Highway, and Interstate H-3. By integrating scalable invasive-tree detection with infrastructure exposure assessment, the framework provides a spatial decision-support tool for prioritizing vegetation management, hazard mitigation, and infrastructure maintenance. The approach contributes to sustainability by supporting more targeted use of management resources, reducing potential disruption to critical infrastructure and essential access, and strengthening long-term community and infrastructure resilience…”
#GeoAI #sustainableinfrastructure #infrastructureresilience #invasivespecies #management #Albizia #deeplearning #infrastructureexposure #disasterpreparedness #climateadaptation #climatechange #decisionsupport #planning #policy #GIS #spatial #mapping #model #treedetection #AI #Hawai’i #Hawaii #rapidgrowth #shallowrootsystem #windthrow #windfall #vegetation #nonnative #risk #hazard #infrastructure #economics #mitigation #vegetationmanagement #infrastructuremaintenance #utilities #extremeweather #wind #storm -
GeoAI For Infrastructure Resilience - Mapping Exposure To Invasive Albizia Trees In Hawai’i
--
https://doi.org/10.3390/su181910061 <-- shared paper
--
https://dlnr.hawaii.gov/hisc/info/invasive-species-profiles/albizia/ <-- shared link, Albizia,
Hawaii Invasive Species Council (HISC)
--
H/T @Jolie Wanger
“By integrating scalable invasive-tree detection with infrastructure exposure assessment, the framework provides a spatial decision-support tool for prioritizing vegetation management, hazard mitigation, and infrastructure maintenance…”
--
“Invasive albizia (Falcataria falcata (L.) Greuter & R. Rankin) trees pose an increasing threat to infrastructure, transportation networks, and public safety in Hawai’i because their rapid growth and shallow root systems make them susceptible to windthrow during severe weather events. However, comprehensive spatial information on albizia distribution remains limited, constraining invasive-species management, hazard mitigation, and sustainable infrastructure planning. This study evaluates the applicability of a GeoAI-based framework for identifying invasive-tree-related infrastructure exposure at a regional planning scale. This study develops a GeoAI framework using a U-Net convolutional neural network with a ResNet-34 backbone to detect and map albizia canopy from 0.6 m National Agriculture Imagery Program (NAIP) aerial imagery. Training data from two geographically distinct areas, Mānoa and Kahalu’u, were used for model development and iterative refinement. The final model achieved a precision of 0.76, compared with 0.64 in the initial iteration, and was applied to estimate potential tree-fall exposure through spatial proximity analysis of roads and buildings. Following manual quality control, approximately 2 km2 of albizia canopy were identified within the study area. More than 53 km of roads and 2300 buildings were located within the potential tree-fall exposure zone, including portions of major transportation corridors such as Pali Highway, Likelike Highway, and Interstate H-3. By integrating scalable invasive-tree detection with infrastructure exposure assessment, the framework provides a spatial decision-support tool for prioritizing vegetation management, hazard mitigation, and infrastructure maintenance. The approach contributes to sustainability by supporting more targeted use of management resources, reducing potential disruption to critical infrastructure and essential access, and strengthening long-term community and infrastructure resilience…”
#GeoAI #sustainableinfrastructure #infrastructureresilience #invasivespecies #management #Albizia #deeplearning #infrastructureexposure #disasterpreparedness #climateadaptation #climatechange #decisionsupport #planning #policy #GIS #spatial #mapping #model #treedetection #AI #Hawai’i #Hawaii #rapidgrowth #shallowrootsystem #windthrow #windfall #vegetation #nonnative #risk #hazard #infrastructure #economics #mitigation #vegetationmanagement #infrastructuremaintenance #utilities #extremeweather #wind #storm