Fix/smart surface lulc overture

.github/requirements.txt

@@ -15,6 +15,6 @@ s3fs==2024.5.0
 geemap==0.32.0
 pip==23.3.1
 boto3==1.34.124
-scikit-learn==1.5.0
+scikit-learn==1.5.1
 overturemaps==0.6.0
 exactextract==0.2.0.dev252

city_metrix/layers/smart_surface_lulc.py

@@ -13,6 +13,7 @@
 
 from .layer import Layer, get_utm_zone_epsg, create_fishnet_grid, MAX_TILE_SIZE
 from .open_street_map import OpenStreetMap, OpenStreetMapClass
+from .overture_buildings import OvertureBuildings
 from ..models.building_classifier.building_classifier import BuildingClassifier
 
 
@@ -33,12 +34,12 @@ def get_data(self, bbox):
 
         # Open space
         open_space_osm = OpenStreetMap(osm_class=OpenStreetMapClass.OPEN_SPACE_HEAT).get_data(bbox).to_crs(crs).reset_index()
-        open_space_osm['Value'] = np.int8(10)
+        open_space_osm['Value'] = 10
 
 
         # Water
         water_osm = OpenStreetMap(osm_class=OpenStreetMapClass.WATER).get_data(bbox).to_crs(crs).reset_index()
-        water_osm['Value'] = np.int8(20)
+        water_osm['Value'] = 20
 
 
         # Roads
@@ -59,7 +60,7 @@ def get_data(self, bbox):
             roads_osm['lanes'] = roads_osm['lanes'].fillna(roads_osm['avg_lanes'])
 
             # Add value field (30)
-            roads_osm['Value'] = np.int8(30)
+            roads_osm['Value'] = 30
 
             # Buffer roads by lanes * 10 ft (3.048 m)
             # https://nacto.org/publication/urban-street-design-guide/street-design-elements/lane-width/#:~:text=wider%20lane%20widths.-,Lane%20widths%20of%2010%20feet%20are%20appropriate%20in%20urban%20areas,be%20used%20in%20each%20direction
@@ -73,14 +74,14 @@ def get_data(self, bbox):
             )
         else:
             # Add value field (30)
-            roads_osm['Value'] = np.int8(30)
+            roads_osm['Value'] = 30
 
 
         # Building
         ulu_lulc_1m = BuildingClassifier().get_data_ulu(bbox, crs, esa_1m)
         anbh_1m = BuildingClassifier().get_data_anbh(bbox, esa_1m)
         # get building features
-        buildings = BuildingClassifier().get_data_buildings(bbox, crs)
+        buildings = OvertureBuildings().get_data(bbox).to_crs(crs)
         # extract ULU, ANBH, and Area_m
         buildings['ULU'] = exact_extract(ulu_lulc_1m, buildings, ["majority"], output='pandas')['majority']
         buildings['ANBH'] = exact_extract(anbh_1m, buildings, ["mean"], output='pandas')['mean']
@@ -112,7 +113,7 @@ def get_data(self, bbox):
 
         # Parking
         parking_osm = OpenStreetMap(osm_class=OpenStreetMapClass.PARKING).get_data(bbox).to_crs(crs).reset_index()
-        parking_osm['Value'] = np.int8(50)
+        parking_osm['Value'] = 50
 
 
         # combine features: open space, water, road, building, parking

city_metrix/models/building_classifier/building_classifier.py

@@ -16,8 +16,6 @@
 from ...layers.esa_world_cover import EsaWorldCover, EsaWorldCoverClass
 from ...layers.urban_land_use import UrbanLandUse
 from ...layers.average_net_building_height import AverageNetBuildingHeight
-from ...layers.open_street_map import OpenStreetMap, OpenStreetMapClass
-from ...layers.open_buildings import OpenBuildings
 
 
 class BuildingClassifier(Layer):
@@ -52,8 +50,8 @@ def get_data_esa_reclass(self, bbox, crs):
         # Perform the reclassification
         reclassified_esa = reclassify(esa_world_cover, bins=list(reclass_map.keys()), new_values=list(reclass_map.values()))
 
-        # Convert to int8 and chunk the data for Dask processing
-        reclassified_esa = reclassified_esa.astype(np.int8).chunk({'x': 512, 'y': 512})
+        # Chunk the data for Dask processing
+        reclassified_esa = reclassified_esa.chunk({'x': 512, 'y': 512})
 
         reclassified_esa = reclassified_esa.rio.write_crs(esa_world_cover.rio.crs, inplace=True)
 
@@ -81,8 +79,8 @@ def get_data_ulu(self, bbox, crs, snap_to):
         for from_val, to_val in mapping.items():
             ulu_lulc = ulu_lulc.where(ulu_lulc != from_val, to_val)
 
-        # Convert to int8 and chunk the data for Dask processing
-        ulu_lulc = ulu_lulc.astype(np.int8).chunk({'x': 512, 'y': 512})
+        # Chunk the data for Dask processing
+        ulu_lulc = ulu_lulc.chunk({'x': 512, 'y': 512})
 
         ####### 1-Non-residential as default
         # 0-Unclassified as nodata 
@@ -111,28 +109,10 @@ def get_data_anbh(self, bbox, snap_to):
 
         return anbh_1m
 
-    def get_data_buildings(self, bbox, crs):
-        # OSM buildings
-        building_osm = OpenStreetMap(osm_class=OpenStreetMapClass.BUILDING).get_data(bbox).to_crs(crs).reset_index(drop=True)
-        # Google-Microsoft Open Buildings Dataset buildings
-        openbuilds = OpenBuildings(country='USA').get_data(bbox).to_crs(crs).reset_index(drop=True)
-
-        # Intersect buildings and keep the open buildings that don't intersect OSM buildings
-        intersect_buildings = gpd.sjoin(building_osm, openbuilds, how='inner', predicate='intersects')
-        openbuilds_non_intersect = openbuilds.loc[~openbuilds.index.isin(intersect_buildings.index)]
-
-        buildings = pd.concat([building_osm['geometry'], openbuilds_non_intersect['geometry']], ignore_index=True).reset_index()
-        # Get rid of any 3d geometries that cause a problem
-        buildings = buildings[~buildings['geometry'].apply(lambda geom: 'Z' in geom.geom_type)]
-
-        # Value not start with 0
-        buildings['Value'] = buildings['index'] + 1
-
-        return buildings
 
     def rasterize_polygon(self, gdf, snap_to):
         if gdf.empty:
-            raster = np.full(snap_to.shape, 0, dtype=np.int8)
+            raster = np.full(snap_to.shape, 0)
             raster = xr.DataArray(raster, dims=snap_to.dims, coords=snap_to.coords)
 
             return raster.rio.write_crs(snap_to.rio.crs, inplace=True)
@@ -141,7 +121,7 @@ def rasterize_polygon(self, gdf, snap_to):
             vector_data=gdf,
             measurements=["Value"],
             like=snap_to,
-            fill=np.int8(0)
+            fill=0
         ).Value
 
         return raster.rio.reproject_match(snap_to)
@@ -169,7 +149,7 @@ def building_classifier_tree(self):
         # set classifier parameters
         clf = DecisionTreeClassifier(max_depth=5)
         # encode labels
-        buildings_sample['Slope_encoded'] = buildings_sample['Slope'].map({'low': np.int8(42), 'high': np.int8(40)})
+        buildings_sample['Slope_encoded'] = buildings_sample['Slope'].map({'low': 42, 'high': 40})
 
         # Select these rows for the training set
         build_train = buildings_sample[buildings_sample['Model']=='training']

environment.yml

@@ -21,7 +21,7 @@ dependencies:
   - geemap=0.32.0
   - pip=23.3.1
   - boto3=1.34.124
-  - scikit-learn=1.5.0
+  - scikit-learn=1.5.1
   - exactextract=0.2.0.dev252
   - pip:
       - overturemaps==0.6.0

setup.py

@@ -30,6 +30,6 @@
         "boto3",
         "exactextract<=0.2.0.dev252",
         "overturemaps",
-        "scikit-learn>=1.5.0",
+        "scikit-learn>=1.5.1",
     ],
 )