#greennessofcalgary — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #greennessofcalgary, aggregated by home.social.
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My experiment with land-cover classification for Calgary using satellite imagery and with a machine-learning model trained on data from another continent.
The results turned out surprisingly good — most classes transferred almost perfectly.
The only noticeable shift was the Forest class: tree and shrub vegetation in the source region differs from Calgary’s, so the model mapped it conservatively here.Still, the general structure of the landscape was captured very well, and community-level land-cover profiles look consistent.
#Rstats #RemoteSensing #GIS #MachineLearning #LandCover #Calgary #EarthObservation #LULC #GreennessOfCalgary #QGIS #UrbanHealth #Alberta #Canada #Sentinel #Copernicus #CopernicusSentinel #Sentinel1 #Sentinel2 #ESA #DataScience #FOSS #UrbanEcology #UrbanNature
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My experiment with land-cover classification for Calgary using satellite imagery and with a machine-learning model trained on data from another continent.
The results turned out surprisingly good — most classes transferred almost perfectly.
The only noticeable shift was the Forest class: tree and shrub vegetation in the source region differs from Calgary’s, so the model mapped it conservatively here.Still, the general structure of the landscape was captured very well, and community-level land-cover profiles look consistent.
#Rstats #RemoteSensing #GIS #MachineLearning #LandCover #Calgary #EarthObservation #LULC #GreennessOfCalgary #QGIS #UrbanHealth #Alberta #Canada #Sentinel #Copernicus #CopernicusSentinel #Sentinel1 #Sentinel2 #ESA #DataScience #FOSS #UrbanEcology #UrbanNature
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My experiment with land-cover classification for Calgary using satellite imagery and with a machine-learning model trained on data from another continent.
The results turned out surprisingly good — most classes transferred almost perfectly.
The only noticeable shift was the Forest class: tree and shrub vegetation in the source region differs from Calgary’s, so the model mapped it conservatively here.Still, the general structure of the landscape was captured very well, and community-level land-cover profiles look consistent.
#Rstats #RemoteSensing #GIS #MachineLearning #LandCover #Calgary #EarthObservation #LULC #GreennessOfCalgary #QGIS #UrbanHealth #Alberta #Canada #Sentinel #Copernicus #CopernicusSentinel #Sentinel1 #Sentinel2 #ESA #DataScience #FOSS #UrbanEcology #UrbanNature
-
My experiment with land-cover classification for Calgary using satellite imagery and with a machine-learning model trained on data from another continent.
The results turned out surprisingly good — most classes transferred almost perfectly.
The only noticeable shift was the Forest class: tree and shrub vegetation in the source region differs from Calgary’s, so the model mapped it conservatively here.Still, the general structure of the landscape was captured very well, and community-level land-cover profiles look consistent.
#Rstats #RemoteSensing #GIS #MachineLearning #LandCover #Calgary #EarthObservation #LULC #GreennessOfCalgary #QGIS #UrbanHealth #Alberta #Canada #Sentinel #Copernicus #CopernicusSentinel #Sentinel1 #Sentinel2 #ESA #DataScience #FOSS #UrbanEcology #UrbanNature