giswqs commited on
Commit
c6e60d9
·
1 Parent(s): be0237a

Updated dataset categories

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Files changed (1) hide show
  1. apps/gee_datasets.py +32 -3
apps/gee_datasets.py CHANGED
@@ -7,6 +7,7 @@ HEIGHT = 600
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  def function():
 
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  Map = geemap.Map()
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  Map.to_streamlit(WIDTH, HEIGHT)
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@@ -126,19 +127,47 @@ def app():
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  "Native Land (Indigenous Land Maps)": "function()",
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  },
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  "Land Use and Land Cover": {
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  "Global Mangrove Watch": "global_mangrove_watch()",
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  "Mississippi River Basin Floodplain Land Use Change (1941-2000)": "lulc_mrb_floodplain()",
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  }
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- row1_col1, row1_col2, _ = st.columns([1, 2, 1])
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  with row1_col1:
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- category = st.selectbox("Select a category", datasets.keys(), index=1)
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  with row1_col2:
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  dataset = st.selectbox("Select a dataset", datasets[category].keys())
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  def function():
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+ st.write("Not implemented yet.")
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  Map = geemap.Map()
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  Map.to_streamlit(WIDTH, HEIGHT)
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  "Native Land (Indigenous Land Maps)": "function()",
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  },
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+ "Geophysical, Biological & Biogeochemical": {
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+ "Geomorpho90m Geomorphometric Layers": "function()",
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+ },
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+
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  "Land Use and Land Cover": {
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  "Global Mangrove Watch": "global_mangrove_watch()",
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  "Mississippi River Basin Floodplain Land Use Change (1941-2000)": "lulc_mrb_floodplain()",
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+ },
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+
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+ "Hydrology": {
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+ "Global Shoreline Dataset": "function()",
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+ },
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+
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+ "Agriculture, Vegetation and Forestry": {
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+ "Landfire Mosaics LF v2.0.0": "function()",
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+ },
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+
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+ "Global Utilities, Assets and Amenities Layers": {
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+ "Global Power": "function()",
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+ },
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+
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+ "EarthEnv Biodiversity ecosystems & climate Layers": {
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+ "Global Consensus Landcover": "function()",
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+ },
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+
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+ "Weather and Climate Layers": {
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+ "Global Reference Evapotranspiration Layers": "function()",
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+ },
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+
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+ "Global Events Layers": {
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+ "Global Fire Atlas (2003-2016)": "function()",
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+ },
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  }
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+ row1_col1, row1_col2, _ = st.columns([1.2, 1.8, 1])
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  with row1_col1:
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+ category = st.selectbox("Select a category", datasets.keys(), index=2)
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  with row1_col2:
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  dataset = st.selectbox("Select a dataset", datasets[category].keys())
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