home.social

#landsat β€” Public Fediverse posts

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

  1. You Can Spell Your Name is Aerial Images Thanks to NASA

    β€˜PetaPixel’ typed out in images captured by Landsat. Following on from Earth Day (April 22), NASA has publicized…
    #NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Science #earthday #Interactive #Landsat #NASA #satelliteimage
    newsbeep.com/us/610463/

  2. You Can Spell Your Name is Aerial Images Thanks to NASA

    β€˜PetaPixel’ typed out in images captured by Landsat. Following on from Earth Day (April 22), NASA has publicized…
    #NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Science #earthday #Interactive #Landsat #NASA #satelliteimage
    newsbeep.com/us/610463/

  3. You Can Spell Your Name is Aerial Images Thanks to NASA

    β€˜PetaPixel’ typed out in images captured by Landsat. Following on from Earth Day (April 22), NASA has publicized…
    #NewsBeep #News #Science #earthday #GB #Interactive #Landsat #NASA #satelliteimage #UK #UnitedKingdom
    newsbeep.com/uk/554164/

  4. πŸ’» I took several completely independent datasets and "pitted" them against each other. One of the results is shown in this chart: the more "concrete" (roads, buildings, parking lots) my machine learning model identified in a community, the higher the surface temperature recorded by the thermal sensor.

    πŸ”₯ The result: Data from different sources confirm one another. The difference in surface temperature between "green" and "concrete" residential areas averages 8–10Β°C throughout the summer. On certain days, this gap is likely even wider.

    πŸ“‰ This chart shows only established residential communities. If industrial zones were included, the trend would be even more dramatic. While modeling errors certainly exist, the overall physical pattern is undeniable.

    #Calgary #OpenData #UrbanHeat #LULC #DataScience #ClimateAction #YYC #GreennesOfCalgary #ClimateEquity #EnvironmentalEquity #CityPlanning #MachineLearning #RemoteSensing #RStats #Sentinel1 #Sentinel2 #Landsat #fossgis

  5. πŸ’» I took several completely independent datasets and "pitted" them against each other. One of the results is shown in this chart: the more "concrete" (roads, buildings, parking lots) my machine learning model identified in a community, the higher the surface temperature recorded by the thermal sensor.

    πŸ”₯ The result: Data from different sources confirm one another. The difference in surface temperature between "green" and "concrete" residential areas averages 8–10Β°C throughout the summer. On certain days, this gap is likely even wider.

    πŸ“‰ This chart shows only established residential communities. If industrial zones were included, the trend would be even more dramatic. While modeling errors certainly exist, the overall physical pattern is undeniable.

    #Calgary #OpenData #UrbanHeat #LULC #DataScience #ClimateAction #YYC #GreennesOfCalgary #ClimateEquity #EnvironmentalEquity #CityPlanning #MachineLearning #RemoteSensing #RStats #Sentinel1 #Sentinel2 #Landsat #fossgis

  6. πŸ’» I took several completely independent datasets and "pitted" them against each other. One of the results is shown in this chart: the more "concrete" (roads, buildings, parking lots) my machine learning model identified in a community, the higher the surface temperature recorded by the thermal sensor.

    πŸ”₯ The result: Data from different sources confirm one another. The difference in surface temperature between "green" and "concrete" residential areas averages 8–10Β°C throughout the summer. On certain days, this gap is likely even wider.

    πŸ“‰ This chart shows only established residential communities. If industrial zones were included, the trend would be even more dramatic. While modeling errors certainly exist, the overall physical pattern is undeniable.

    #Calgary #OpenData #UrbanHeat #LULC #DataScience #ClimateAction #YYC #GreennesOfCalgary #ClimateEquity #EnvironmentalEquity #CityPlanning #MachineLearning #RemoteSensing #RStats #Sentinel1 #Sentinel2 #Landsat #fossgis

  7. πŸ’» I took several completely independent datasets and "pitted" them against each other. One of the results is shown in this chart: the more "concrete" (roads, buildings, parking lots) my machine learning model identified in a community, the higher the surface temperature recorded by the thermal sensor.

    πŸ”₯ The result: Data from different sources confirm one another. The difference in surface temperature between "green" and "concrete" residential areas averages 8–10Β°C throughout the summer. On certain days, this gap is likely even wider.

    πŸ“‰ This chart shows only established residential communities. If industrial zones were included, the trend would be even more dramatic. While modeling errors certainly exist, the overall physical pattern is undeniable.

    #Calgary #OpenData #UrbanHeat #LULC #DataScience #ClimateAction #YYC #GreennesOfCalgary #ClimateEquity #EnvironmentalEquity #CityPlanning #MachineLearning #RemoteSensing #RStats #Sentinel1 #Sentinel2 #Landsat #fossgis

  8. πŸ’» I took several completely independent datasets and "pitted" them against each other. One of the results is shown in this chart: the more "concrete" (roads, buildings, parking lots) my machine learning model identified in a community, the higher the surface temperature recorded by the thermal sensor.

    πŸ”₯ The result: Data from different sources confirm one another. The difference in surface temperature between "green" and "concrete" residential areas averages 8–10Β°C throughout the summer. On certain days, this gap is likely even wider.

    πŸ“‰ This chart shows only established residential communities. If industrial zones were included, the trend would be even more dramatic. While modeling errors certainly exist, the overall physical pattern is undeniable.

    #Calgary #OpenData #UrbanHeat #LULC #DataScience #ClimateAction #YYC #GreennesOfCalgary #ClimateEquity #EnvironmentalEquity #CityPlanning #MachineLearning #RemoteSensing #RStats #Sentinel1 #Sentinel2 #Landsat #fossgis

  9. Assessment of Shoreline Change in Southeast Ireland Using Geospatial Techniques
    --
    doi.org/10.3390/su18073280 <-- shared paper
    --
    "... KEY INSIGHTS:
    β€’ Coastlines are highly dynamic β€” 57% accretion vs 42% erosion
    β€’ Strong contrasts between east-facing (Irish Sea) and south-facing (Atlantic) coasts
    β€’ Identification of critical erosion hotspots (e.g., Tramore) and accretion zones in embayments
    β€’ Coastal change is driven by a combination of wave climate, sediment availability, geology, and human activity
    --
    #GIS #spatial #mapping #Ireland #coast #coastal #dynamics #erosion #accretion #shoreline #change #digitalshoreline #spatialanalysis #spatiotemporal #remotesensing #earthobservation #SoutheastIreland #embayments #wave #climate #stormsurge #geology #humanimpacts #coastalmanagement #risk #hazard #mitigation #sealevel #RSL #risingsealevels #climatechanage #adaption #extremeweather #stormintensity #planning #monitoring #sustainable #Landsat #satellite #regional

  10. Assessment of Shoreline Change in Southeast Ireland Using Geospatial Techniques
    --
    doi.org/10.3390/su18073280 <-- shared paper
    --
    "... KEY INSIGHTS:
    β€’ Coastlines are highly dynamic β€” 57% accretion vs 42% erosion
    β€’ Strong contrasts between east-facing (Irish Sea) and south-facing (Atlantic) coasts
    β€’ Identification of critical erosion hotspots (e.g., Tramore) and accretion zones in embayments
    β€’ Coastal change is driven by a combination of wave climate, sediment availability, geology, and human activity
    --

  11. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    β€œHIGHLIGHTS:
    β€’ ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    β€’ District-level RF/XGBoost model training with expert-weighted validation.
    β€’ Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    β€’ RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    β€’ NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    #Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

  12. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    β€œHIGHLIGHTS:
    β€’ ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    β€’ District-level RF/XGBoost model training with expert-weighted validation.
    β€’ Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    β€’ RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    β€’ NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    #Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

  13. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    β€œHIGHLIGHTS:
    β€’ ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    β€’ District-level RF/XGBoost model training with expert-weighted validation.
    β€’ Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    β€’ RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    β€’ NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    #Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

  14. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    β€œHIGHLIGHTS:
    β€’ ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    β€’ District-level RF/XGBoost model training with expert-weighted validation.
    β€’ Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    β€’ RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    β€’ NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    #Forestdisturbance #forest #disturbance #remotesensing #LandTrendr #workflow #timeseries #ViT #RF #XGBoost #GEE #Nepal #ForestNepal #spatial #GIS #mapping #earthobservation #landsat #Himalayas #mountains #alpine #vegetation #AI #multispectral #monitoring #spatialanalysis #spatiotemporal #loss #change #machinelearning #NDR #conservation #planning #policy #mitagion #ecology #Karnali #Bagmati, #Darchula #Siwalik #GlobalForestChange #Degradation

  15. Improving Forest Loss Mapping In Nepal Using Landtrendr Time-Series And Machine Learning
    --
    doi.org/10.1016/j.rsase.2025.1 <-- share paper
    --
    β€œHIGHLIGHTS:
    β€’ ViT-based forest mask, multispectral ensemble LandTrendr and terrain shadow mask.
    β€’ District-level RF/XGBoost model training with expert-weighted validation.
    β€’ Outperformed GFC and REDD + AI benchmarks in accuracy and F1 performance.
    β€’ RF excelled in High Mountains/Himalayas; XGBoost in the lower Mountain regions.
    β€’ NBR contributed the most; snow-impacted forest loss uncertainty was observed..."
    ,

  16. In the distant 2016, when I was a member of an environmental NGO in Kryvyi Rih (Ukraine), I started using satellite Earth Observation data to monitor the condition of large industrial tailings ponds.

    At that time, environmental regulations required these storage facilities to be either flooded or at least kept moist to prevent dust storms.
    Industrial operators often ignored these rules, leaving huge dry surfaces exposed β€” which created massive dust pollution affecting nearby communities.

    Using Sentinel-2 and Landsat-8 imagery with false-color composites, I developed a simple but effective method to map dry, moist, and water-covered zones of tailings ponds.

    Local residents and journalists were absolutely delighted! Industrial companies, on the other hand… reacted very differently 🀣

    These maps are from 2016–2017 and show several tailings facilities around #KryvyiRih.

    #RemoteSensing #EarthObservation #Sentinel2 #OpenData #EnvironmentalMonitoring
    #Tailings #Mining #DustPollution #GIS #QGIS #Ukraine #Landsat

  17. The Disappearance of Lac [Lake] Rouge [remote sensing]
    --
    earthobservatory.nasa.gov/imag <-- Shared NASA Earth Observatory Images Of The Day
    --
    cbc.ca/news/canada/north/lake- <-- shared media article
    --
    β€œA landscape in central Quebec transformed suddenly in spring 2025 when a lake burst its banks and drained. Members of the nearby community of Waswanipi who use the area for hunting, fishing, and trapping learned of the curious incident in early May, after reports of a washed-out road. Further air- and ground-based investigations revealed that land around the lake had collapsed and that Lac Rouge had emptied..."
    #GIS #spatial #mapping #spatiotemporal #water #hydrology #lake #LacRouge #Quebec #Canada #Waswanipi #Cree #FirstNation #wildfire #geomorphometry #outflow #OLI2 #Landsat #Landsat9 #earthobservation #remotesensing #wildlife #habitat #change #ecology #sediment # #snowmelt #rainfall

  18. The Disappearance of Lac [Lake] Rouge [remote sensing]
    --
    earthobservatory.nasa.gov/imag <-- Shared NASA Earth Observatory Images Of The Day
    --
    cbc.ca/news/canada/north/lake- <-- shared media article
    --
    β€œA landscape in central Quebec transformed suddenly in spring 2025 when a lake burst its banks and drained. Members of the nearby community of Waswanipi who use the area for hunting, fishing, and trapping learned of the curious incident in early May, after reports of a washed-out road. Further air- and ground-based investigations revealed that land around the lake had collapsed and that Lac Rouge had emptied..."
    #