#early-warning — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #early-warning, aggregated by home.social.
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Hardliners inside IRGC pushing for an attack against US base in turkey.
The only untouched US base in the region.
The early warning radars at US bases in Turkey has been the lifeline for the US to detect Iranian missiles and to protect their bases as well as Israeli targets.
An attack against the US base in turkey will be seen as an attack on Turkey as well as NATO, which may trigger both the Mecca accord as well as NATO article 5.
Question is, will IRI leadership, specially Khamenei give the green light for an attack that may push the criminal hypocrite Erdogan to finally show his true face and initiate an attack against Iran?
#Turkey #NATO #Waroniran #EarlyWarning #Radar #WestAsia #Iran
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Hardliners inside IRGC pushing for an attack against US base in turkey.
The only untouched US base in the region.
The early warning radars at US bases in Turkey has been the lifeline for the US to detect Iranian missiles and to protect their bases as well as Israeli targets.
An attack against the US base in turkey will be seen as an attack on Turkey as well as NATO, which may trigger both the Mecca accord as well as NATO article 5.
Question is, will IRI leadership, specially Khamenei give the green light for an attack that may push the criminal hypocrite Erdogan to finally show his true face and initiate an attack against Iran?
#Turkey #NATO #Waroniran #EarlyWarning #Radar #WestAsia #Iran
-
Hardliners inside IRGC pushing for an attack against US base in turkey.
The only untouched US base in the region.
The early warning radars at US bases in Turkey has been the lifeline for the US to detect Iranian missiles and to protect their bases as well as Israeli targets.
An attack against the US base in turkey will be seen as an attack on Turkey as well as NATO, which may trigger both the Mecca accord as well as NATO article 5.
Question is, will IRI leadership, specially Khamenei give the green light for an attack that may push the criminal hypocrite Erdogan to finally show his true face and initiate an attack against Iran?
#Turkey #NATO #Waroniran #EarlyWarning #Radar #WestAsia #Iran
-
Hardliners inside IRGC pushing for an attack against US base in turkey.
The only untouched US base in the region.
The early warning radars at US bases in Turkey has been the lifeline for the US to detect Iranian missiles and to protect their bases as well as Israeli targets.
An attack against the US base in turkey will be seen as an attack on Turkey as well as NATO, which may trigger both the Mecca accord as well as NATO article 5.
Question is, will IRI leadership, specially Khamenei give the green light for an attack that may push the criminal hypocrite Erdogan to finally show his true face and initiate an attack against Iran?
#Turkey #NATO #Waroniran #EarlyWarning #Radar #WestAsia #Iran
-
Hardliners inside IRGC pushing for an attack against US base in turkey.
The only untouched US base in the region.
The early warning radars at US bases in Turkey has been the lifeline for the US to detect Iranian missiles and to protect their bases as well as Israeli targets.
An attack against the US base in turkey will be seen as an attack on Turkey as well as NATO, which may trigger both the Mecca accord as well as NATO article 5.
Question is, will IRI leadership, specially Khamenei give the green light for an attack that may push the criminal hypocrite Erdogan to finally show his true face and initiate an attack against Iran?
#Turkey #NATO #Waroniran #EarlyWarning #Radar #WestAsia #Iran
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Estonian Foreign Intelligence Chief: “Everyone in Russia Knows Mobilization Is Coming” https://www.byteseu.com/2328462/ #EarlyWarning #Estonia
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Estonian Foreign Intelligence Chief: “Everyone in Russia Knows Mobilization Is Coming”
The option is on the table, and there is a real probability that it will happen. The question…
#NATO #OTAN #Europe #Europa #EU #News #earlywarning
https://www.europesays.com/nato/12729/ -
#Rasuwa #flood: Why did #earlywarning fail despite systems in place?
This report names two main factors:
* tech - sensors, servers, networks
* cross-border communicationsI think in addition to these local/regional factors, a global question:
there are #satellite systems seeing this happen in near-realtime -
and "AI"s analyzing.
yet noone cares to issue a warning .... (?)#Nepal #Tibet #glacierCollapse #landslide
#AI #earthobservation -
#Rasuwa #flood: Why did #earlywarning fail despite systems in place?
This report names two main factors:
* tech - sensors, servers, networks
* cross-border communicationsI think in addition to these local/regional factors, a global question:
there are #satellite systems seeing this happen in near-realtime -
and "AI"s analyzing.
yet noone cares to issue a warning .... (?)#Nepal #Tibet #glacierCollapse #landslide
#AI #earthobservation -
#Rasuwa #flood: Why did #earlywarning fail despite systems in place?
This report names two main factors:
* tech - sensors, servers, networks
* cross-border communicationsI think in addition to these local/regional factors, a global question:
there are #satellite systems seeing this happen in near-realtime -
and "AI"s analyzing.
yet noone cares to issue a warning .... (?)#Nepal #Tibet #glacierCollapse #landslide
#AI #earthobservation -
#Rasuwa #flood: Why did #earlywarning fail despite systems in place?
This report names two main factors:
* tech - sensors, servers, networks
* cross-border communicationsI think in addition to these local/regional factors, a global question:
there are #satellite systems seeing this happen in near-realtime -
and "AI"s analyzing.
yet noone cares to issue a warning .... (?)#Nepal #Tibet #glacierCollapse #landslide
#AI #earthobservation -
#Rasuwa #flood: Why did #earlywarning fail despite systems in place?
This report names two main factors:
* tech - sensors, servers, networks
* cross-border communicationsI think in addition to these local/regional factors, a global question:
there are #satellite systems seeing this happen in near-realtime -
and "AI"s analyzing.
yet noone cares to issue a warning .... (?)#Nepal #Tibet #glacierCollapse #landslide
#AI #earthobservation -
A new study from an international research team, including WorldPop at the University of Southampton, introduces the Chinese Multi-Hazard Early Warning Dataset - over one million official warning records (2022–2025) across four administrative levels. The open dataset supports research into risk communication, disaster preparedness and resilience.
#OpenData #DisasterRiskReduction #EarlyWarning #ClimateRisk #Hazards #Resilience #ResearchData
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A new study from an international research team, including WorldPop at the University of Southampton, introduces the Chinese Multi-Hazard Early Warning Dataset - over one million official warning records (2022–2025) across four administrative levels. The open dataset supports research into risk communication, disaster preparedness and resilience.
#OpenData #DisasterRiskReduction #EarlyWarning #ClimateRisk #Hazards #Resilience #ResearchData
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A new study from an international research team, including WorldPop at the University of Southampton, introduces the Chinese Multi-Hazard Early Warning Dataset - over one million official warning records (2022–2025) across four administrative levels. The open dataset supports research into risk communication, disaster preparedness and resilience.
#OpenData #DisasterRiskReduction #EarlyWarning #ClimateRisk #Hazards #Resilience #ResearchData
-
A new study from an international research team, including WorldPop at the University of Southampton, introduces the Chinese Multi-Hazard Early Warning Dataset - over one million official warning records (2022–2025) across four administrative levels. The open dataset supports research into risk communication, disaster preparedness and resilience.
#OpenData #DisasterRiskReduction #EarlyWarning #ClimateRisk #Hazards #Resilience #ResearchData
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WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
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https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones/ <-- shared technical Google DeepMind blog post
--
https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
--
https://deepmind.google/science/weatherlab/ <-- shared data
--
https://github.com/google-deepmind/weathernext <-- shared GitHub repository
--
H/T @juliet Rothenberg | Product Director of Earth & Resilience AI at Google
[this post should not be considered an endorsement of a particular organisation or their approach]
“[The Google WeatherNext AI team] are showing how the WeatherNext AI model from Google DeepMind and Google Research has achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, the WeatherNext Cyclones model gives forecasters an extra day’s worth of predictive accuracy- delivering an advance equivalent to roughly a decade of historical meteorological progress 🌀
Here is how WeatherNext is transforming cyclone forecasting:
• Gaining an Extra Day of Advanced Warning: WN 3-day forecasts are as good as what prior models were able to provide for 2-day forecasts, giving critical time for emergency response.
• Overcoming Traditional Trade-offs: WN bridges the gap between massive global atmospheric currents (which steer a cyclone's path) and fine-grained thermodynamic processes around its core (which drive its intensity) into a single AI model.
• Unprecedented Ensemble Scale: Using Functional Generative Networks (FGNs), WN now generates 1,000-member ensembles in less than a minute on a TPU to capture rare, consequential tail-risks like sudden rapid intensification – which means forecasters can see a broader range of possible scenarios.
• Real-World Impact: During the 2025 Atlantic hurricane season, the WN model helped the National Hurricane Center (NHC) make a historic forecast for Hurricane Melissa by predicting rapid intensification and landfall five days in advance.
[The] teams are open sourcing the operationalized models (WeatherNext Cyclones and WeatherNext 2), alongside a compact version (WeatherNext 2-mini) that can run on a single TPU in a free public Colab notebook – all with a goal of empowering local organizations worldwide.
Weather affects everyone. By combining advanced AI with the real-world expertise of human forecasters, we can build a collaborative ecosystem that saves lives and helps communities adapt to a changing climate…”
#Google #DeepMind #GoogleResearch #AI #ensembles #FunctionalGenerativeNetworks #WeatherNext #cyclone #operationalised #model #modeling #forecasting #spatialanalyis #spatiotemporal #track #intensity #windstructure #hurricane #weather #climate #metrology #cyclonetrack #risk #hazard #emergencyresponse #planning #tool #earlywarning #scale #magnitude #path #track #thermodynamic #scenarios #opensource #impacts #tropicalcyclones #WeatherNextCyclones #weathermodel #atmospheric #predictions #mitigation #warning #robust #publicsafety #infrastructure
@Google | @WeatherNext -
WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
--
https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones/ <-- shared technical Google DeepMind blog post
--
https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
--
https://deepmind.google/science/weatherlab/ <-- shared data
--
https://github.com/google-deepmind/weathernext <-- shared GitHub repository
--
H/T @juliet Rothenberg | Product Director of Earth & Resilience AI at Google
[this post should not be considered an endorsement of a particular organisation or their approach]
“[The Google WeatherNext AI team] are showing how the WeatherNext AI model from Google DeepMind and Google Research has achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, the WeatherNext Cyclones model gives forecasters an extra day’s worth of predictive accuracy- delivering an advance equivalent to roughly a decade of historical meteorological progress 🌀
Here is how WeatherNext is transforming cyclone forecasting:
• Gaining an Extra Day of Advanced Warning: WN 3-day forecasts are as good as what prior models were able to provide for 2-day forecasts, giving critical time for emergency response.
• Overcoming Traditional Trade-offs: WN bridges the gap between massive global atmospheric currents (which steer a cyclone's path) and fine-grained thermodynamic processes around its core (which drive its intensity) into a single AI model.
• Unprecedented Ensemble Scale: Using Functional Generative Networks (FGNs), WN now generates 1,000-member ensembles in less than a minute on a TPU to capture rare, consequential tail-risks like sudden rapid intensification – which means forecasters can see a broader range of possible scenarios.
• Real-World Impact: During the 2025 Atlantic hurricane season, the WN model helped the National Hurricane Center (NHC) make a historic forecast for Hurricane Melissa by predicting rapid intensification and landfall five days in advance.
[The] teams are open sourcing the operationalized models (WeatherNext Cyclones and WeatherNext 2), alongside a compact version (WeatherNext 2-mini) that can run on a single TPU in a free public Colab notebook – all with a goal of empowering local organizations worldwide.
Weather affects everyone. By combining advanced AI with the real-world expertise of human forecasters, we can build a collaborative ecosystem that saves lives and helps communities adapt to a changing climate…”
#Google #DeepMind #GoogleResearch #AI #ensembles #FunctionalGenerativeNetworks #WeatherNext #cyclone #operationalised #model #modeling #forecasting #spatialanalyis #spatiotemporal #track #intensity #windstructure #hurricane #weather #climate #metrology #cyclonetrack #risk #hazard #emergencyresponse #planning #tool #earlywarning #scale #magnitude #path #track #thermodynamic #scenarios #opensource #impacts #tropicalcyclones #WeatherNextCyclones #weathermodel #atmospheric #predictions #mitigation #warning #robust #publicsafety #infrastructure
@Google | @WeatherNext -
WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
--
https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones/ <-- shared technical Google DeepMind blog post
--
https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
--
https://deepmind.google/science/weatherlab/ <-- shared data
--
https://github.com/google-deepmind/weathernext <-- shared GitHub repository
--
H/T @juliet Rothenberg | Product Director of Earth & Resilience AI at Google
[this post should not be considered an endorsement of a particular organisation or their approach]
“[The Google WeatherNext AI team] are showing how the WeatherNext AI model from Google DeepMind and Google Research has achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, the WeatherNext Cyclones model gives forecasters an extra day’s worth of predictive accuracy- delivering an advance equivalent to roughly a decade of historical meteorological progress 🌀
Here is how WeatherNext is transforming cyclone forecasting:
• Gaining an Extra Day of Advanced Warning: WN 3-day forecasts are as good as what prior models were able to provide for 2-day forecasts, giving critical time for emergency response.
• Overcoming Traditional Trade-offs: WN bridges the gap between massive global atmospheric currents (which steer a cyclone's path) and fine-grained thermodynamic processes around its core (which drive its intensity) into a single AI model.
• Unprecedented Ensemble Scale: Using Functional Generative Networks (FGNs), WN now generates 1,000-member ensembles in less than a minute on a TPU to capture rare, consequential tail-risks like sudden rapid intensification – which means forecasters can see a broader range of possible scenarios.
• Real-World Impact: During the 2025 Atlantic hurricane season, the WN model helped the National Hurricane Center (NHC) make a historic forecast for Hurricane Melissa by predicting rapid intensification and landfall five days in advance.
[The] teams are open sourcing the operationalized models (WeatherNext Cyclones and WeatherNext 2), alongside a compact version (WeatherNext 2-mini) that can run on a single TPU in a free public Colab notebook – all with a goal of empowering local organizations worldwide.
Weather affects everyone. By combining advanced AI with the real-world expertise of human forecasters, we can build a collaborative ecosystem that saves lives and helps communities adapt to a changing climate…”
#Google #DeepMind #GoogleResearch #AI #ensembles #FunctionalGenerativeNetworks #WeatherNext #cyclone #operationalised #model #modeling #forecasting #spatialanalyis #spatiotemporal #track #intensity #windstructure #hurricane #weather #climate #metrology #cyclonetrack #risk #hazard #emergencyresponse #planning #tool #earlywarning #scale #magnitude #path #track #thermodynamic #scenarios #opensource #impacts #tropicalcyclones #WeatherNextCyclones #weathermodel #atmospheric #predictions #mitigation #warning #robust #publicsafety #infrastructure
@Google | @WeatherNext -
WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
--
https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones/ <-- shared technical Google DeepMind blog post
--
https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
--
https://deepmind.google/science/weatherlab/ <-- shared data
--
https://github.com/google-deepmind/weathernext <-- shared GitHub repository
--
H/T @juliet Rothenberg | Product Director of Earth & Resilience AI at Google
[this post should not be considered an endorsement of a particular organisation or their approach]
“[The Google WeatherNext AI team] are showing how the WeatherNext AI model from Google DeepMind and Google Research has achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, the WeatherNext Cyclones model gives forecasters an extra day’s worth of predictive accuracy- delivering an advance equivalent to roughly a decade of historical meteorological progress 🌀
Here is how WeatherNext is transforming cyclone forecasting:
• Gaining an Extra Day of Advanced Warning: WN 3-day forecasts are as good as what prior models were able to provide for 2-day forecasts, giving critical time for emergency response.
• Overcoming Traditional Trade-offs: WN bridges the gap between massive global atmospheric currents (which steer a cyclone's path) and fine-grained thermodynamic processes around its core (which drive its intensity) into a single AI model.
• Unprecedented Ensemble Scale: Using Functional Generative Networks (FGNs), WN now generates 1,000-member ensembles in less than a minute on a TPU to capture rare, consequential tail-risks like sudden rapid intensification – which means forecasters can see a broader range of possible scenarios.
• Real-World Impact: During the 2025 Atlantic hurricane season, the WN model helped the National Hurricane Center (NHC) make a historic forecast for Hurricane Melissa by predicting rapid intensification and landfall five days in advance.
[The] teams are open sourcing the operationalized models (WeatherNext Cyclones and WeatherNext 2), alongside a compact version (WeatherNext 2-mini) that can run on a single TPU in a free public Colab notebook – all with a goal of empowering local organizations worldwide.
Weather affects everyone. By combining advanced AI with the real-world expertise of human forecasters, we can build a collaborative ecosystem that saves lives and helps communities adapt to a changing climate…”
#Google #DeepMind #GoogleResearch #AI #ensembles #FunctionalGenerativeNetworks #WeatherNext #cyclone #operationalised #model #modeling #forecasting #spatialanalyis #spatiotemporal #track #intensity #windstructure #hurricane #weather #climate #metrology #cyclonetrack #risk #hazard #emergencyresponse #planning #tool #earlywarning #scale #magnitude #path #track #thermodynamic #scenarios #opensource #impacts #tropicalcyclones #WeatherNextCyclones #weathermodel #atmospheric #predictions #mitigation #warning #robust #publicsafety #infrastructure
@Google | @WeatherNext -
WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
--
https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones/ <-- shared technical Google DeepMind blog post
--
https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
--
https://deepmind.google/science/weatherlab/ <-- shared data
--
https://github.com/google-deepmind/weathernext <-- shared GitHub repository
--
H/T @juliet Rothenberg | Product Director of Earth & Resilience AI at Google
[this post should not be considered an endorsement of a particular organisation or their approach]
“[The Google WeatherNext AI team] are showing how the WeatherNext AI model from Google DeepMind and Google Research has achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, the WeatherNext Cyclones model gives forecasters an extra day’s worth of predictive accuracy- delivering an advance equivalent to roughly a decade of historical meteorological progress 🌀
Here is how WeatherNext is transforming cyclone forecasting:
• Gaining an Extra Day of Advanced Warning: WN 3-day forecasts are as good as what prior models were able to provide for 2-day forecasts, giving critical time for emergency response.
• Overcoming Traditional Trade-offs: WN bridges the gap between massive global atmospheric currents (which steer a cyclone's path) and fine-grained thermodynamic processes around its core (which drive its intensity) into a single AI model.
• Unprecedented Ensemble Scale: Using Functional Generative Networks (FGNs), WN now generates 1,000-member ensembles in less than a minute on a TPU to capture rare, consequential tail-risks like sudden rapid intensification – which means forecasters can see a broader range of possible scenarios.
• Real-World Impact: During the 2025 Atlantic hurricane season, the WN model helped the National Hurricane Center (NHC) make a historic forecast for Hurricane Melissa by predicting rapid intensification and landfall five days in advance.
[The] teams are open sourcing the operationalized models (WeatherNext Cyclones and WeatherNext 2), alongside a compact version (WeatherNext 2-mini) that can run on a single TPU in a free public Colab notebook – all with a goal of empowering local organizations worldwide.
Weather affects everyone. By combining advanced AI with the real-world expertise of human forecasters, we can build a collaborative ecosystem that saves lives and helps communities adapt to a changing climate…”
#Google #DeepMind #GoogleResearch #AI #ensembles #FunctionalGenerativeNetworks #WeatherNext #cyclone #operationalised #model #modeling #forecasting #spatialanalyis #spatiotemporal #track #intensity #windstructure #hurricane #weather #climate #metrology #cyclonetrack #risk #hazard #emergencyresponse #planning #tool #earlywarning #scale #magnitude #path #track #thermodynamic #scenarios #opensource #impacts #tropicalcyclones #WeatherNextCyclones #weathermodel #atmospheric #predictions #mitigation #warning #robust #publicsafety #infrastructure
@Google | @WeatherNext -
AI could transform how we predict hunger and respond to food crises. But a new expert statement warns that algorithms cannot replace human judgement.
In conflict-affected and data-poor regions, AI-generated information can be incomplete, biased or misleading. Human experts must remain at the heart of life-saving forecasts.
-
AI could transform how we predict hunger and respond to food crises. But a new expert statement warns that algorithms cannot replace human judgement.
In conflict-affected and data-poor regions, AI-generated information can be incomplete, biased or misleading. Human experts must remain at the heart of life-saving forecasts.
-
AI could transform how we predict hunger and respond to food crises. But a new expert statement warns that algorithms cannot replace human judgement.
In conflict-affected and data-poor regions, AI-generated information can be incomplete, biased or misleading. Human experts must remain at the heart of life-saving forecasts.
-
AI could transform how we predict hunger and respond to food crises. But a new expert statement warns that algorithms cannot replace human judgement.
In conflict-affected and data-poor regions, AI-generated information can be incomplete, biased or misleading. Human experts must remain at the heart of life-saving forecasts.
-
AI could transform how we predict hunger and respond to food crises. But a new expert statement warns that algorithms cannot replace human judgement.
In conflict-affected and data-poor regions, AI-generated information can be incomplete, biased or misleading. Human experts must remain at the heart of life-saving forecasts.
-
GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
--
https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
--
https://zenodo.org/records/17627111 <-- shared open data
--
H/T @kyle Davis
“Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
#IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
@FAO - Food and Agriculture Organization -
GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
--
https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
--
https://zenodo.org/records/17627111 <-- shared open data
--
H/T @kyle Davis
“Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
#IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
@FAO - Food and Agriculture Organization -
GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
--
https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
--
https://zenodo.org/records/17627111 <-- shared open data
--
H/T @kyle Davis
“Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
#IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
@FAO - Food and Agriculture Organization -
GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
--
https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
--
https://zenodo.org/records/17627111 <-- shared open data
--
H/T @kyle Davis
“Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
#IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
@FAO - Food and Agriculture Organization -
GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
--
https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
--
https://zenodo.org/records/17627111 <-- shared open data
--
H/T @kyle Davis
“Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
#IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
@FAO - Food and Agriculture Organization -
🟡 Early Warning | 7/10
🇮🇱Early warnings in northern Israel
Early warning alerts are in effect in Kiryat Shmona and surrounding areas of the confrontation line in northern Israel. -
🟡 Early Warning | 7/10
🇮🇱Early warnings in northern Israel
Early warning alerts are in effect in Kiryat Shmona and surrounding areas of the confrontation line in northern Israel. -
🟡 Early Warning | 7/10
🇮🇱Early warnings in northern Israel
Early warning alerts are in effect in Kiryat Shmona and surrounding areas of the confrontation line in northern Israel. -
https://medium.com/the-story-well/the-early-warning-dashboard-2f3437108cfc
Small signals stack up. When they line up in the wrong way, they push a community into a fast slide that’s hard to stop.
#SHTF #teotwawki #collapse #psychology #mediumpublication #medium #EarlyWarningSigns #EarlyWarning #preparedness
-
https://medium.com/the-story-well/the-early-warning-dashboard-2f3437108cfc
Small signals stack up. When they line up in the wrong way, they push a community into a fast slide that’s hard to stop.
#SHTF #teotwawki #collapse #psychology #mediumpublication #medium #EarlyWarningSigns #EarlyWarning #preparedness
-
https://medium.com/the-story-well/the-early-warning-dashboard-2f3437108cfc
Small signals stack up. When they line up in the wrong way, they push a community into a fast slide that’s hard to stop.
#SHTF #teotwawki #collapse #psychology #mediumpublication #medium #EarlyWarningSigns #EarlyWarning #preparedness
-
https://medium.com/the-story-well/the-early-warning-dashboard-2f3437108cfc
Small signals stack up. When they line up in the wrong way, they push a community into a fast slide that’s hard to stop.
#SHTF #teotwawki #collapse #psychology #mediumpublication #medium #EarlyWarningSigns #EarlyWarning #preparedness
-
https://medium.com/the-story-well/the-early-warning-dashboard-2f3437108cfc
Small signals stack up. When they line up in the wrong way, they push a community into a fast slide that’s hard to stop.
#SHTF #teotwawki #collapse #psychology #mediumpublication #medium #EarlyWarningSigns #EarlyWarning #preparedness
-
#NoNaturalDisasters [UN]
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https://www.undrr.org/our-impact/campaigns/no-natural-disasters | https://www.nonaturaldisasters.com/ <-- shared technical articles
--
#Resilience #ClimateAction #Governance #risk #hazard #riskassessment #naturalhazard #disaster #humanimpacts #engineeringgeology #NoNaturalDisasters #natural #nature #earthquake #storm #flood #flooding #tsunami #volcano #desertification #wildfire #fires #publicsafety #planning #policy #preparedness #ISO31000 #monitoring #buildingcodes #engineering #landuse #earlywarning #humanmaderisk #exposure #vulnerability #socioeconomic #ecosystems #habitat #livelihoods #farming #infrastructure #buildings #structures #industrial #cost #recovery #economics #homes #extremeweather #hurricane #tornado #resilience #sustainability #poverty #assessment #UNDRR #UN #riskreduction
@United Nations Office for Disaster Risk Reduction -
#NoNaturalDisasters [UN]
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https://www.undrr.org/our-impact/campaigns/no-natural-disasters | https://www.nonaturaldisasters.com/ <-- shared technical articles
--
#Resilience #ClimateAction #Governance #risk #hazard #riskassessment #naturalhazard #disaster #humanimpacts #engineeringgeology #NoNaturalDisasters #natural #nature #earthquake #storm #flood #flooding #tsunami #volcano #desertification #wildfire #fires #publicsafety #planning #policy #preparedness #ISO31000 #monitoring #buildingcodes #engineering #landuse #earlywarning #humanmaderisk #exposure #vulnerability #socioeconomic #ecosystems #habitat #livelihoods #farming #infrastructure #buildings #structures #industrial #cost #recovery #economics #homes #extremeweather #hurricane #tornado #resilience #sustainability #poverty #assessment #UNDRR #UN #riskreduction
@United Nations Office for Disaster Risk Reduction -
#NoNaturalDisasters [UN]
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https://www.undrr.org/our-impact/campaigns/no-natural-disasters | https://www.nonaturaldisasters.com/ <-- shared technical articles
--
#Resilience #ClimateAction #Governance #risk #hazard #riskassessment #naturalhazard #disaster #humanimpacts #engineeringgeology #NoNaturalDisasters #natural #nature #earthquake #storm #flood #flooding #tsunami #volcano #desertification #wildfire #fires #publicsafety #planning #policy #preparedness #ISO31000 #monitoring #buildingcodes #engineering #landuse #earlywarning #humanmaderisk #exposure #vulnerability #socioeconomic #ecosystems #habitat #livelihoods #farming #infrastructure #buildings #structures #industrial #cost #recovery #economics #homes #extremeweather #hurricane #tornado #resilience #sustainability #poverty #assessment #UNDRR #UN #riskreduction
@United Nations Office for Disaster Risk Reduction -
#NoNaturalDisasters [UN]
--
https://www.undrr.org/our-impact/campaigns/no-natural-disasters | https://www.nonaturaldisasters.com/ <-- shared technical articles
--
#Resilience #ClimateAction #Governance #risk #hazard #riskassessment #naturalhazard #disaster #humanimpacts #engineeringgeology #NoNaturalDisasters #natural #nature #earthquake #storm #flood #flooding #tsunami #volcano #desertification #wildfire #fires #publicsafety #planning #policy #preparedness #ISO31000 #monitoring #buildingcodes #engineering #landuse #earlywarning #humanmaderisk #exposure #vulnerability #socioeconomic #ecosystems #habitat #livelihoods #farming #infrastructure #buildings #structures #industrial #cost #recovery #economics #homes #extremeweather #hurricane #tornado #resilience #sustainability #poverty #assessment #UNDRR #UN #riskreduction
@United Nations Office for Disaster Risk Reduction -
#NoNaturalDisasters [UN]
--
https://www.undrr.org/our-impact/campaigns/no-natural-disasters | https://www.nonaturaldisasters.com/ <-- shared technical articles
--
#Resilience #ClimateAction #Governance #risk #hazard #riskassessment #naturalhazard #disaster #humanimpacts #engineeringgeology #NoNaturalDisasters #natural #nature #earthquake #storm #flood #flooding #tsunami #volcano #desertification #wildfire #fires #publicsafety #planning #policy #preparedness #ISO31000 #monitoring #buildingcodes #engineering #landuse #earlywarning #humanmaderisk #exposure #vulnerability #socioeconomic #ecosystems #habitat #livelihoods #farming #infrastructure #buildings #structures #industrial #cost #recovery #economics #homes #extremeweather #hurricane #tornado #resilience #sustainability #poverty #assessment #UNDRR #UN #riskreduction
@United Nations Office for Disaster Risk Reduction -
https://www.europesays.com/africa/267136/ Somalia launches Early Warning for All Initiative to strengthen climate resilience #ArabStates #ClimateChangeAndDisasterRiskReduction #EarlyWarning #Goal13:ClimateAction #Somalia
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🟡 Early Warning | 7/10
🇮🇱Early Warning Alerts in Confrontation Line, Israel
Early warning alerts are in effect in the Confrontation Line region, Northern Israel. -
🟡 Early Warning | 7/10
🇮🇱Early Warning Alerts in Confrontation Line, Israel
Early warning alerts are in effect in the Confrontation Line region, Northern Israel. -
🟡 Early Warning | 7/10
🇮🇱Early Warning Alerts in Confrontation Line, Israel
Early warning alerts are in effect in the Confrontation Line region, Northern Israel. -
🟡 Early Warning | 6/10
🇮🇱Early warning alert in northern Israel
Early warning alerts issued for Kiryat Shmona, confrontation line, and Golan Heights areas. -
🟡 Early Warning | 6/10
🇮🇱Early warning alert in northern Israel
Early warning alerts issued for Kiryat Shmona, confrontation line, and Golan Heights areas. -
🟡 Early Warning | 6/10
🇮🇱Early warning alert in northern Israel
Early warning alerts issued for Kiryat Shmona, confrontation line, and Golan Heights areas. -
🟡 Early Warning | 8/10
🇮🇱Early warnings on Confrontation Line, Northern Israel
Early warning alerts are in effect in the Confrontation Line region, Northern Israel. -
🟡 Early Warning | 8/10
🇮🇱Early warnings on Confrontation Line, Northern Israel
Early warning alerts are in effect in the Confrontation Line region, Northern Israel. -
🟡 Early Warning | 8/10
🇮🇱Early warnings on Confrontation Line, Northern Israel
Early warning alerts are in effect in the Confrontation Line region, Northern Israel.
