diff --git a/city_metrix/layers/__init__.py b/city_metrix/layers/__init__.py
index bef56c3..1ca7443 100644
--- a/city_metrix/layers/__init__.py
+++ b/city_metrix/layers/__init__.py
@@ -3,7 +3,7 @@
 from .land_surface_temperature import LandSurfaceTemperature
 from .tree_cover import TreeCover
 from .high_land_surface_temperature import HighLandSurfaceTemperature
-from .smart_cities_lulc import SmartCitiesLULC
+from .smart_surface_lulc import SmartSurfaceLULC
 from .open_street_map import OpenStreetMap, OpenStreetMapClass
 from .urban_land_use import UrbanLandUse
 from .natural_areas import NaturalAreas
@@ -14,5 +14,5 @@
 from .built_up_height import BuiltUpHeight
 from .average_net_building_height import AverageNetBuildingHeight
 from .open_buildings import OpenBuildings
-from .tree_canopy_hight import TreeCanopyHeight
+from .tree_canopy_height import TreeCanopyHeight
 from .alos_dsm import AlosDSM
diff --git a/city_metrix/layers/building_classifier/V2-building-class-data.geojson b/city_metrix/layers/building_classifier/V2-building-class-data.geojson
new file mode 100644
index 0000000..50fa155
--- /dev/null
+++ b/city_metrix/layers/building_classifier/V2-building-class-data.geojson
@@ -0,0 +1,303 @@
+{
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+]
+}
diff --git a/city_metrix/layers/building_classifier/building_classifier.pkl b/city_metrix/layers/building_classifier/building_classifier.pkl
new file mode 100644
index 0000000..d1cee21
Binary files /dev/null and b/city_metrix/layers/building_classifier/building_classifier.pkl differ
diff --git a/city_metrix/layers/building_classifier/building_classifier.py b/city_metrix/layers/building_classifier/building_classifier.py
new file mode 100644
index 0000000..5865a0f
--- /dev/null
+++ b/city_metrix/layers/building_classifier/building_classifier.py
@@ -0,0 +1,197 @@
+import xarray as xr
+import ee
+import numpy as np
+from rasterio.enums import Resampling
+from geocube.api.core import make_geocube
+import pandas as pd
+import geopandas as gpd
+from sklearn.tree import DecisionTreeClassifier, plot_tree
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+from xrspatial.classify import reclassify
+from exactextract import exact_extract
+import pickle
+
+from ..layer import Layer, get_utm_zone_epsg
+from ..esa_world_cover import EsaWorldCover, EsaWorldCoverClass
+from ..urban_land_use import UrbanLandUse
+from ..average_net_building_height import AverageNetBuildingHeight
+from ..open_street_map import OpenStreetMap, OpenStreetMapClass
+from ..open_buildings import OpenBuildings
+
+
+class BuildingClassifier(Layer):
+    def __init__(self, geo_file='V2-building-class-data.geojson', **kwargs):
+        super().__init__(**kwargs)
+        self.geo_file = geo_file
+
+    def get_data_geo(self):
+        buildings_sample = gpd.read_file(self.geo_file)
+        buildings_sample.to_crs(epsg=4326, inplace=True)
+
+        return buildings_sample
+
+    def get_data_esa_reclass(self, bbox, crs):
+        # ESA reclass and upsample
+        esa_world_cover = EsaWorldCover(year=2021).get_data(bbox)
+
+        reclass_map = {
+            EsaWorldCoverClass.TREE_COVER.value: 1,
+            EsaWorldCoverClass.SHRUBLAND.value: 1,
+            EsaWorldCoverClass.GRASSLAND.value: 1,
+            EsaWorldCoverClass.CROPLAND.value: 1,
+            EsaWorldCoverClass.BUILT_UP.value: 2,
+            EsaWorldCoverClass.BARE_OR_SPARSE_VEGETATION.value: 3,
+            EsaWorldCoverClass.SNOW_AND_ICE.value: 20,
+            EsaWorldCoverClass.PERMANENT_WATER_BODIES.value: 20,
+            EsaWorldCoverClass.HERBACEOUS_WET_LAND.value: 20,
+            EsaWorldCoverClass.MANGROVES.value: 20,
+            EsaWorldCoverClass.MOSS_AND_LICHEN.value: 3
+        }
+
+        # 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})
+
+        reclassified_esa = reclassified_esa.rio.write_crs(esa_world_cover.rio.crs, inplace=True)
+
+        esa_1m = reclassified_esa.rio.reproject(
+            dst_crs=crs,
+            resolution=1,
+            resampling=Resampling.nearest
+        )
+
+        return esa_1m
+
+    def get_data_ulu(self, bbox, crs, snap_to):
+        # Read ULU land cover, filter to city, select lulc band
+        ulu_lulc = UrbanLandUse(band='lulc').get_data(bbox)
+        ###### ulu_roads = UrbanLandUse(band='road').get_data(bbox)
+        ####### Create road mask of 50
+        ####### Typical threshold for creating road mask
+        ####### road_mask = ulu_roads >= 50
+        ####### ulu_lulc = ulu_lulc.where(~road_mask, 6)
+        # 0-Unclassified: 0 (open space)
+        # 1-Non-residential: 0 (open space), 1 (non-res)
+        # 2-Residential: 2 (Atomistic), 3 (Informal), 4 (Formal), 5 (Housing project)
+        ####### 3-Roads: 6 (Roads)
+        mapping = {0: 0, 1: 1, 2: 2, 3: 2, 4: 2, 5: 2}
+        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})
+
+        ####### 1-Non-residential as default
+        # 0-Unclassified as nodata 
+        ulu_lulc_1m = ulu_lulc.rio.reproject(
+            dst_crs=crs,
+            shape=snap_to.shape,
+            resampling=Resampling.nearest,
+            nodata=0
+        )
+
+        return ulu_lulc_1m
+
+    def get_data_anbh(self, bbox, snap_to):
+        # Load ANBH layer
+        anbh_data = AverageNetBuildingHeight().get_data(bbox)
+
+        # Chunk the data for Dask processing
+        anbh_data = anbh_data.chunk({'x': 512, 'y': 512})
+
+        # Use reproject_match, because reproject would raise OneDimensionalRaster with shape (1,1)
+        anbh_1m = anbh_data.rio.reproject_match(
+            match_data_array=snap_to,
+            resampling=Resampling.nearest,
+            nodata=0
+        )
+
+        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 = xr.DataArray(raster, dims=snap_to.dims, coords=snap_to.coords)
+
+            return raster.rio.write_crs(snap_to.rio.crs, inplace=True)
+
+        raster = make_geocube(
+            vector_data=gdf,
+            measurements=["Value"],
+            like=snap_to,
+            fill=np.int8(0)
+        ).Value
+
+        return raster.rio.reproject_match(snap_to)
+    
+
+    def building_classifier_tree(self):
+        buildings_sample = self.get_data_geo()
+        
+        # # Building sample 'V2-building-class-data.geojson' has extracted data and saved in geojson,
+        # # so no need for steps below
+        # # Step 1: Get raster data
+        # bbox = buildings_sample.reset_index().total_bounds
+        # crs = get_utm_zone_epsg(bbox)
+        # esa_1m = BuildingClassifier().get_data_esa_reclass(bbox, crs)
+        # ulu_lulc_1m = self.get_data_ulu(bbox, crs, esa_1m)
+        # anbh_1m = self.get_data_anbh(bbox, esa_1m)
+        # # Step 2: Extract 3 features for buildings:
+        # # 2.1 majority of Urban Land Use(ULU) class
+        # # 2.2 mean Average Net Building Height(ANBH)
+        # # 2.3 area of the building
+        # buildings_sample['ULU'] = exact_extract(ulu_lulc_1m, buildings_sample, ["majority"], output='pandas')['majority']
+        # buildings_sample['ANBH'] = exact_extract(anbh_1m, buildings_sample, ["mean"], output='pandas')['mean']
+        # buildings_sample['Area_m'] = buildings_sample.geometry.area
+
+        # TODO: 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)})
+
+        # Select these rows for the training set
+        build_train = buildings_sample[buildings_sample['Model']=='training']
+
+        clf.fit(build_train[['ULU', 'ANBH', 'Area_m']], build_train['Slope_encoded'])
+
+        # save decision tree classifier
+        with open('building_classifier.pkl','wb') as f:
+            pickle.dump(clf, f)
+
+        # # Check decision tree and accuracy
+        # # Select the remaining rows for the testing set
+        # build_test = buildings_sample[buildings_sample['Model']=='testing']
+        # plt.figure(figsize=(20, 10))
+        # plot_tree(clf, feature_names=['ULU', 'ANBH', 'Area_m'], class_names=['low','high'], filled=True)
+        # plt.show()
+        # # Predict and evaluate
+        # y_pred = clf.predict(build_train[['ULU', 'ANBH', 'Area_m']])
+        # accuracy = accuracy_score(build_train['Slope_encoded'], y_pred)
+        # print(f"Training Accuracy: {accuracy}")
+        # len(build_train[build_train['Slope']==build_train['pred']])/len(build_train)
+        # y_pred = clf.predict(build_test[['ULU', 'ANBH', 'Area_m']])
+        # accuracy = accuracy_score(build_test['Slope_encoded'], y_pred)
+        # print(f"Test Accuracy: {accuracy}")
+        # len(build_test[build_test['Slope']==build_test['pred']])/len(build_test)
diff --git a/city_metrix/layers/smart_cities_lulc.py b/city_metrix/layers/smart_cities_lulc.py
deleted file mode 100644
index 425cee5..0000000
--- a/city_metrix/layers/smart_cities_lulc.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from pystac_client import Client
-import rioxarray
-import xarray as xr
-
-from .layer import Layer
-
-
-class SmartCitiesLULC(Layer):
-    def __init__(self, land_cover_class=None, **kwargs):
-        super().__init__(**kwargs)
-        self.land_cover_class = land_cover_class
-
-    def get_data(self, bbox):
-       raise NotImplementedError()
-
-
-
-
-
-
-
diff --git a/city_metrix/layers/smart_surface_lulc.py b/city_metrix/layers/smart_surface_lulc.py
new file mode 100644
index 0000000..4e35976
--- /dev/null
+++ b/city_metrix/layers/smart_surface_lulc.py
@@ -0,0 +1,130 @@
+import xarray as xr
+import numpy as np
+import pandas as pd
+import geopandas as gpd
+from shapely.geometry import CAP_STYLE, JOIN_STYLE
+from shapely.geometry import box
+import psutil
+from exactextract import exact_extract
+import pickle
+import warnings
+warnings.filterwarnings('ignore',category=UserWarning)
+
+from .layer import Layer, get_utm_zone_epsg, create_fishnet_grid, MAX_TILE_SIZE
+from .open_street_map import OpenStreetMap, OpenStreetMapClass
+from .building_classifier.building_classifier import BuildingClassifier
+
+
+class SmartSurfaceLULC(Layer):
+    def __init__(self, land_cover_class=None, **kwargs):
+        super().__init__(**kwargs)
+        self.land_cover_class = land_cover_class
+
+    def get_data(self, bbox):
+        crs = get_utm_zone_epsg(bbox)
+
+        # load building roof slope classifier
+        with open('city_metrix/layers/building_classifier/building_classifier.pkl', 'rb') as f:
+            clf = pickle.load(f)
+
+        # ESA world cover
+        esa_1m = BuildingClassifier().get_data_esa_reclass(bbox, crs)
+
+        # 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)
+
+
+        # Water
+        water_osm = OpenStreetMap(osm_class=OpenStreetMapClass.WATER).get_data(bbox).to_crs(crs).reset_index()
+        water_osm['Value'] = np.int8(20)
+
+
+        # Roads
+        roads_osm = OpenStreetMap(osm_class=OpenStreetMapClass.ROAD).get_data(bbox).to_crs(crs).reset_index()
+        if len(roads_osm) > 0:
+            roads_osm['lanes'] = pd.to_numeric(roads_osm['lanes'], errors='coerce')
+            # Get the average number of lanes per highway class
+            lanes = (roads_osm.drop(columns='geometry')
+                    .groupby('highway')
+                    # Calculate average and round up
+                    .agg(avg_lanes=('lanes', lambda x: np.ceil(np.nanmean(x)) if not np.isnan(x).all() else np.NaN))
+                    )
+            # Handle NaN values in avg_lanes
+            lanes['avg_lanes'] = lanes['avg_lanes'].fillna(2)
+
+            # Fill lanes with avg lane value when missing
+            roads_osm = roads_osm.merge(lanes, on='highway', how='left')
+            roads_osm['lanes'] = roads_osm['lanes'].fillna(roads_osm['avg_lanes'])
+
+            # Add value field (30)
+            roads_osm['Value'] = np.int8(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
+            # cap is flat to the terminus of the road
+            # join style is mitred so intersections are squared
+            roads_osm['geometry'] = roads_osm.apply(lambda row: row['geometry'].buffer(
+                row['lanes'] * 3.048,
+                cap_style=CAP_STYLE.flat,
+                join_style=JOIN_STYLE.mitre),
+                axis=1
+            )
+        else:
+            # Add value field (30)
+            roads_osm['Value'] = np.int8(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)
+        # 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']
+        buildings['Area_m'] = buildings.geometry.area
+        # classify buildings
+        unclassed_buildings = buildings[buildings['ULU'] == 0]
+        classed_buildings = buildings[buildings['ULU'] != 0]
+        
+        if len(classed_buildings) > 0:
+            classed_buildings['Value'] = clf.predict(classed_buildings[['ULU', 'ANBH', 'Area_m']])
+            # Define conditions and choices
+            case_when_class = [
+                # "residential" & "high"
+                (classed_buildings['Value'] == 40) & (classed_buildings['ULU'] == 2),
+                # "non-residential" & "high"
+                (classed_buildings['Value'] == 40) & (classed_buildings['ULU'] == 1),
+                # "residential" & "low"
+                (classed_buildings['Value'] == 42) & (classed_buildings['ULU'] == 2),
+                # "non-residential" & "low"
+                (classed_buildings['Value'] == 42) & (classed_buildings['ULU'] == 1)
+            ]
+            case_when_value = [40, 41, 42, 43]
+            classed_buildings['Value'] = np.select(case_when_class, case_when_value, default=44)
+            unclassed_buildings['Value'] = 44
+            buildings = pd.concat([classed_buildings, unclassed_buildings])
+        else:
+            buildings['Value'] = 44
+
+
+        # Parking
+        parking_osm = OpenStreetMap(osm_class=OpenStreetMapClass.PARKING).get_data(bbox).to_crs(crs).reset_index()
+        parking_osm['Value'] = np.int8(50)
+
+
+        # combine features: open space, water, road, building, parking
+        feature_df = pd.concat([open_space_osm[['geometry','Value']], water_osm[['geometry','Value']], roads_osm[['geometry','Value']], buildings[['geometry','Value']], parking_osm[['geometry','Value']]], axis=0)
+        feature_1m = BuildingClassifier().rasterize_polygon(feature_df, esa_1m)
+
+        # Combine rasters
+        datasets = [esa_1m, feature_1m]
+        # not all raster has 'time', concatenate without 'time' dimension
+        aligned_datasets = [ds.drop_vars('time', errors='ignore') for ds in datasets]
+        # use chunk 512x512
+        aligned_datasets = [ds.chunk({'x': 512, 'y': 512}) for ds in aligned_datasets]
+        lulc = xr.concat(aligned_datasets, dim='Value').max(dim='Value')
+        lulc = lulc.compute()
+
+        return lulc
diff --git a/city_metrix/layers/tree_canopy_hight.py b/city_metrix/layers/tree_canopy_height.py
similarity index 89%
rename from city_metrix/layers/tree_canopy_hight.py
rename to city_metrix/layers/tree_canopy_height.py
index 464c7cf..2785272 100644
--- a/city_metrix/layers/tree_canopy_hight.py
+++ b/city_metrix/layers/tree_canopy_height.py
@@ -7,9 +7,7 @@
 
 
 class TreeCanopyHeight(Layer):
-    
-    name = "tree_canopy_hight"
-
+    name = "tree_canopy_height"
     NO_DATA_VALUE = 0
 
     def __init__(self, **kwargs):
@@ -20,10 +18,6 @@ def get_data(self, bbox):
         # aggregate time series into a single image
         canopy_ht = canopy_ht.reduce(ee.Reducer.mean()).rename("cover_code")
 
-
-
-
-        data = get_image_collection(ee.ImageCollection(canopy_ht), bbox, 1, "tree canopy hight")
+        data = get_image_collection(ee.ImageCollection(canopy_ht), bbox, 1, "tree canopy height")
 
         return data.cover_code
-
diff --git a/environment.yml b/environment.yml
index 4f00317..21b5e16 100644
--- a/environment.yml
+++ b/environment.yml
@@ -21,5 +21,7 @@ dependencies:
   - geemap=0.32.0
   - pip=23.3.1
   - boto3=1.34.124
+  - scikit-learn=1.5.0
   - pip:
     - cartoframes==1.2.5
+    - git+https://github.com/isciences/exactextract
diff --git a/notebooks/tutorial/get layers.ipynb b/notebooks/tutorial/get layers.ipynb
index e67ce8a..67107f8 100644
--- a/notebooks/tutorial/get layers.ipynb	
+++ b/notebooks/tutorial/get layers.ipynb	
@@ -26,7 +26,7 @@
     "| Layer name | class name | Parameter defaults | Layer metadata |\n",
     "| ---- | ---- | ---- | ---- |\n",
     "| Tropical Tree Cover | `TreeCover()` | `min_tree_cover=None`: a threshold to use to filter the minimum percent of tree cover| |\n",
-    "| EsaWorldCover | `EsaWorldCover()` | `land_cover_class=None`; `EsaWorldCoverClass`: a specific class of land cover| |\n",
+    "| EsaWorldCover | `EsaWorldCover()` | `land_cover_class=None`; `EsaWorldCoverClass`: a specific class of land cover; `year=2020`| |\n",
     "| Land Surface Temeprature | `LandSurfaceTemperature()` | `start_date=\"2013-01-01\", end_date=\"2023-01-01\"` | |\n",
     "| Albedo | `Albedo()` | `start_date=\"2021-01-01\", end_date=\"2022-01-01\"` | |\n",
     "| Natural Areas | `NaturalAreas()` | `none` | |\n",
@@ -34,7 +34,8 @@
     "| Building Hight | `AverageNetBuildingHeight()` | `none` | |\n",
     "| Building Footprints | `OpenBuildings()` | `country='USA'` | |\n",
     "| 1m Global Tree Canopy Height  | `TreeCanopyHeight()` | | |\n",
-    "| ALOS DSM | `AlosDSM()`| | |"
+    "| ALOS DSM | `AlosDSM()`| | |\n",
+    "| Smart Surface LULC| `SmartSurfaceLULC()`| | |"
    ]
   },
   {
@@ -86,24 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c506fca",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "286103d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import boto3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d4f01f2f-5164-4a05-9997-f70b2abe6b37",
+   "id": "6280fc2f",
    "metadata": {},
    "source": [
     "In most cases you shouldn't need to set these. GEE will open your browser to authenticate. "
@@ -257,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "5eb883fd-c533-47b7-8735-65393afca89d",
    "metadata": {},
    "outputs": [],
@@ -267,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "0703cfe0-201d-4cae-ab43-7a4d0e5082cf",
    "metadata": {},
    "outputs": [
@@ -275,8 +259,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 9.24 ss\n"
+      "Extracting tree cover layer:\n",
+      "[########################################] | 100% Completed | 19.58 s\n"
      ]
     },
     {
@@ -645,40 +629,40 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ttc&#x27; (y: 2878, x: 3722)&gt;\n",
-       "array([[ nan,  nan,  nan, ..., 100.,  90.,  90.],\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;ttc&#x27; (y: 2878, x: 3721)&gt;\n",
+       "array([[ nan,  nan,  nan, ..., 100., 100.,  90.],\n",
+       "       [ nan,  nan,  nan, ...,  90.,  90.,  90.],\n",
        "       [ nan,  nan,  nan, ...,  90.,  90.,  90.],\n",
-       "       [ nan,  nan,  nan, ...,  90.,  90.,  80.],\n",
        "       ...,\n",
        "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
        "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
        "       [ nan,  nan,  nan, ...,  nan,  nan,  nan]])\n",
        "Coordinates:\n",
-       "    time     int64 0\n",
+       "    time     int32 0\n",
        "  * x        (x) float32 -38.65 -38.65 -38.65 -38.65 ... -38.3 -38.3 -38.3 -38.3\n",
        "  * y        (y) float32 -12.76 -12.76 -12.76 -12.76 ... -13.02 -13.02 -13.02\n",
        "Attributes:\n",
        "    id:             ttc\n",
        "    data_type:      {&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;m...\n",
        "    crs:            EPSG:4326\n",
-       "    crs_transform:  [1, 0, 0, 0, 1, 0]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'ttc'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 2878</li><li><span class='xr-has-index'>x</span>: 3722</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-38f696e7-db41-490f-8daa-0aa72704f94b' class='xr-array-in' type='checkbox' checked><label for='section-38f696e7-db41-490f-8daa-0aa72704f94b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[ nan,  nan,  nan, ..., 100.,  90.,  90.],\n",
+       "    crs_transform:  [1, 0, 0, 0, 1, 0]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'ttc'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 2878</li><li><span class='xr-has-index'>x</span>: 3721</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1e6e2a0c-c2b1-42a0-b6bc-049609492f9c' class='xr-array-in' type='checkbox' checked><label for='section-1e6e2a0c-c2b1-42a0-b6bc-049609492f9c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[ nan,  nan,  nan, ..., 100., 100.,  90.],\n",
+       "       [ nan,  nan,  nan, ...,  90.,  90.,  90.],\n",
        "       [ nan,  nan,  nan, ...,  90.,  90.,  90.],\n",
-       "       [ nan,  nan,  nan, ...,  90.,  90.,  80.],\n",
        "       ...,\n",
        "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
        "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
-       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-69bef0c7-02b8-4e83-9b44-a6eec65d5155' class='xr-section-summary-in' type='checkbox'  checked><label for='section-69bef0c7-02b8-4e83-9b44-a6eec65d5155' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-eca31e52-545d-41c6-ba3d-0f71b08959f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eca31e52-545d-41c6-ba3d-0f71b08959f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d3344e1-ce17-4adf-a215-f1a621545e1c' class='xr-var-data-in' type='checkbox'><label for='data-1d3344e1-ce17-4adf-a215-f1a621545e1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-38.65 -38.65 ... -38.3 -38.3</div><input id='attrs-cac7b2f2-5dfd-4b14-aa29-66f7f7eebbcb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cac7b2f2-5dfd-4b14-aa29-66f7f7eebbcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fffbb33-41a4-4eaf-9fe7-b14d63fee9d8' class='xr-var-data-in' type='checkbox'><label for='data-9fffbb33-41a4-4eaf-9fe7-b14d63fee9d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-38.647305, -38.647213, -38.64712 , ..., -38.304703, -38.304607,\n",
-       "       -38.304516], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-12.76 -12.76 ... -13.02 -13.02</div><input id='attrs-956a749a-fb1f-4005-ab43-e4eeba229688' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-956a749a-fb1f-4005-ab43-e4eeba229688' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74eacf19-1955-49ae-9bf0-638cb6d4d9d3' class='xr-var-data-in' type='checkbox'><label for='data-74eacf19-1955-49ae-9bf0-638cb6d4d9d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-12.757013, -12.757104, -12.757195, ..., -13.016989, -13.017079,\n",
-       "       -13.01717 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e80803bd-d478-4682-aa3a-d84718bae4b7' class='xr-section-summary-in' type='checkbox'  ><label for='section-e80803bd-d478-4682-aa3a-d84718bae4b7' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1ad339cf-e301-4c92-b390-6ea590228366' class='xr-index-data-in' type='checkbox'/><label for='index-1ad339cf-e301-4c92-b390-6ea590228366' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -38.64730453491211, -38.647212982177734,  -38.64712142944336,\n",
+       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan]])</pre></div></div></li><li class='xr-section-item'><input id='section-f358c2db-0292-4686-994f-a9c9183ab5a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f358c2db-0292-4686-994f-a9c9183ab5a3' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-c7cb1c5a-e5b5-4f14-8c01-2df5c7ca2c0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7cb1c5a-e5b5-4f14-8c01-2df5c7ca2c0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f3c6ed4-8d60-4bf0-9c54-9581ce19e523' class='xr-var-data-in' type='checkbox'><label for='data-8f3c6ed4-8d60-4bf0-9c54-9581ce19e523' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-38.65 -38.65 ... -38.3 -38.3</div><input id='attrs-e2995cdd-ca1e-466f-92bf-f931e8ae492c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2995cdd-ca1e-466f-92bf-f931e8ae492c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a0a8272-ea74-400a-98ce-8026565ba73f' class='xr-var-data-in' type='checkbox'><label for='data-5a0a8272-ea74-400a-98ce-8026565ba73f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-38.647305, -38.647213, -38.64712 , ..., -38.304794, -38.304703,\n",
+       "       -38.304607], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-12.76 -12.76 ... -13.02 -13.02</div><input id='attrs-aff4615c-0a57-4f10-bc16-9ee14401ac68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aff4615c-0a57-4f10-bc16-9ee14401ac68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c40bcc99-5257-4e98-97f6-aa1b1f6b44d3' class='xr-var-data-in' type='checkbox'><label for='data-c40bcc99-5257-4e98-97f6-aa1b1f6b44d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-12.757013, -12.757104, -12.757195, ..., -13.016989, -13.017079,\n",
+       "       -13.01717 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b369bb2-2370-436a-b13c-837661322b90' class='xr-section-summary-in' type='checkbox'  ><label for='section-9b369bb2-2370-436a-b13c-837661322b90' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6f0a6036-d9f9-48d9-b7e1-270fe032c5e2' class='xr-index-data-in' type='checkbox'/><label for='index-6f0a6036-d9f9-48d9-b7e1-270fe032c5e2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -38.64730453491211, -38.647212982177734,  -38.64712142944336,\n",
        "       -38.647029876708984,  -38.64693832397461, -38.646846771240234,\n",
        "        -38.64675521850586,  -38.64665985107422, -38.646568298339844,\n",
        "        -38.64647674560547,\n",
        "       ...\n",
-       "        -38.30534744262695,  -38.30525588989258,  -38.30516052246094,\n",
-       "        -38.30506896972656,  -38.30497741699219,  -38.30488586425781,\n",
-       "        -38.30479431152344,  -38.30470275878906,  -38.30460739135742,\n",
-       "        -38.30451583862305],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;x&#x27;, length=3722))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ce3acb1-be1c-4cf0-90d2-2b9a72efff19' class='xr-index-data-in' type='checkbox'/><label for='index-6ce3acb1-be1c-4cf0-90d2-2b9a72efff19' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12.757013320922852,  -12.75710391998291, -12.757194519042969,\n",
+       "        -38.30543899536133,  -38.30534744262695,  -38.30525588989258,\n",
+       "        -38.30516052246094,  -38.30506896972656,  -38.30497741699219,\n",
+       "        -38.30488586425781,  -38.30479431152344,  -38.30470275878906,\n",
+       "        -38.30460739135742],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;x&#x27;, length=3721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9f4fd264-321f-4bb9-af22-6c16613d218c' class='xr-index-data-in' type='checkbox'/><label for='index-9f4fd264-321f-4bb9-af22-6c16613d218c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-12.757013320922852,  -12.75710391998291, -12.757194519042969,\n",
        "       -12.757284164428711,  -12.75737476348877, -12.757465362548828,\n",
        "       -12.757555961608887, -12.757646560668945, -12.757737159729004,\n",
        "       -12.757826805114746,\n",
@@ -687,19 +671,19 @@
        "       -13.016627311706543, -13.016716957092285, -13.016807556152344,\n",
        "       -13.016898155212402, -13.016988754272461,  -13.01707935333252,\n",
        "       -13.017169952392578],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;y&#x27;, length=2878))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6fb40a7-e95b-4f46-b913-0e3213fa01bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6fb40a7-e95b-4f46-b913-0e3213fa01bc' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>ttc</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 255}</dd><dt><span>crs :</span></dt><dd>EPSG:4326</dd><dt><span>crs_transform :</span></dt><dd>[1, 0, 0, 0, 1, 0]</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float32&#x27;, name=&#x27;y&#x27;, length=2878))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-29fa65ff-9127-4d9c-a15a-e15533c469f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-29fa65ff-9127-4d9c-a15a-e15533c469f9' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>id :</span></dt><dd>ttc</dd><dt><span>data_type :</span></dt><dd>{&#x27;type&#x27;: &#x27;PixelType&#x27;, &#x27;precision&#x27;: &#x27;double&#x27;, &#x27;min&#x27;: 0, &#x27;max&#x27;: 255}</dd><dt><span>crs :</span></dt><dd>EPSG:4326</dd><dt><span>crs_transform :</span></dt><dd>[1, 0, 0, 0, 1, 0]</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'ttc' (y: 2878, x: 3722)>\n",
-       "array([[ nan,  nan,  nan, ..., 100.,  90.,  90.],\n",
+       "<xarray.DataArray 'ttc' (y: 2878, x: 3721)>\n",
+       "array([[ nan,  nan,  nan, ..., 100., 100.,  90.],\n",
+       "       [ nan,  nan,  nan, ...,  90.,  90.,  90.],\n",
        "       [ nan,  nan,  nan, ...,  90.,  90.,  90.],\n",
-       "       [ nan,  nan,  nan, ...,  90.,  90.,  80.],\n",
        "       ...,\n",
        "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
        "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
        "       [ nan,  nan,  nan, ...,  nan,  nan,  nan]])\n",
        "Coordinates:\n",
-       "    time     int64 0\n",
+       "    time     int32 0\n",
        "  * x        (x) float32 -38.65 -38.65 -38.65 -38.65 ... -38.3 -38.3 -38.3 -38.3\n",
        "  * y        (y) float32 -12.76 -12.76 -12.76 -12.76 ... -13.02 -13.02 -13.02\n",
        "Attributes:\n",
@@ -709,7 +693,7 @@
        "    crs_transform:  [1, 0, 0, 0, 1, 0]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -720,75 +704,6 @@
     "city_TreeCover"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8c2477dc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 8.91 ss\n"
-     ]
-    }
-   ],
-   "source": [
-    "city_TreeCover = TreeCover().write(city_gdf.total_bounds, \"output.tif\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "cb58b256",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.63 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.52 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.53 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.38 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.35 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 2.47 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.53 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.45 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.46 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.54 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.69 sms\n",
-      "Extracting layer tree cover from Google Earth Engine:\n",
-      "[########################################] | 100% Completed | 1.59 sms\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/anaconda3/envs/cities-cif/lib/python3.10/site-packages/osgeo/gdal.py:287: FutureWarning: Neither gdal.UseExceptions() nor gdal.DontUseExceptions() has been explicitly called. In GDAL 4.0, exceptions will be enabled by default.\n",
-      "  warnings.warn(\n",
-      "ERROR 4: Failed to open output to write.\n"
-     ]
-    }
-   ],
-   "source": [
-    "city_TreeCover = TreeCover().write(city_gdf.total_bounds, \"output\", tile_degrees=0.1)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -4559,7 +4474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 6,
    "id": "0a9ee177",
    "metadata": {},
    "outputs": [
@@ -5415,6 +5330,628 @@
     "city_AlosDSM.plot()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "496ac032",
+   "metadata": {},
+   "source": [
+    "# Smart Surface LULC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6db8e4f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Authenticating to GEE with configured credentials file.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from city_metrix.layers import SmartSurfaceLULC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2ada0845",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting layer ESA world cover from Google Earth Engine for bbox [-38.43996975 -12.92755175 -38.41996975 -12.90755175]:\n",
+      "[########################################] | 100% Completed | 813.65 ms\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/weiqi_tori/anaconda3/envs/fenv/lib/python3.10/site-packages/xee/ext.py:683: UserWarning: Unable to retrieve 'system:time_start' values from an ImageCollection due to: No 'system:time_start' values found in the 'ImageCollection'.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting layer urban land use from Google Earth Engine for bbox [-38.43996975 -12.92755175 -38.41996975 -12.90755175]:\n",
+      "[########################################] | 100% Completed | 1.13 ss\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/weiqi_tori/anaconda3/envs/fenv/lib/python3.10/site-packages/xee/ext.py:683: UserWarning: Unable to retrieve 'system:time_start' values from an ImageCollection due to: No 'system:time_start' values found in the 'ImageCollection'.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting layer average net building height from Google Earth Engine for bbox [-38.43996975 -12.92755175 -38.41996975 -12.90755175]:\n",
+      "[########################################] | 100% Completed | 814.53 ms\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/weiqi_tori/anaconda3/envs/fenv/lib/python3.10/site-packages/geopandas/geodataframe.py:1528: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  super().__setitem__(key, value)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;reclassify&#x27; (y: 2210, x: 2170)&gt; Size: 38MB\n",
+       "array([[ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       ...,\n",
+       "       [ 2,  2,  2, ..., 30, 30,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2]])\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 17kB 5.607e+05 5.608e+05 ... 5.629e+05 5.629e+05\n",
+       "  * y            (y) float64 18kB 8.573e+06 8.573e+06 ... 8.571e+06 8.571e+06\n",
+       "    spatial_ref  int64 8B 0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'reclassify'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 2210</li><li><span class='xr-has-index'>x</span>: 2170</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a1b80306-804e-4060-a3f9-9fa721ee2c77' class='xr-array-in' type='checkbox' checked><label for='section-a1b80306-804e-4060-a3f9-9fa721ee2c77' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ... 30 30 30 30 30 30 30 2 2 2 2 2 2 2 2</span></div><div class='xr-array-data'><pre>array([[ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       ...,\n",
+       "       [ 2,  2,  2, ..., 30, 30,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2]])</pre></div></div></li><li class='xr-section-item'><input id='section-9ffc2d1c-095c-46a0-968b-bd72024a4b8e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9ffc2d1c-095c-46a0-968b-bd72024a4b8e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.607e+05 5.608e+05 ... 5.629e+05</div><input id='attrs-31728bb3-20c0-4e8e-948a-4039f977b800' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31728bb3-20c0-4e8e-948a-4039f977b800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-633addbd-0c1c-4594-a6f1-09f908c7a208' class='xr-var-data-in' type='checkbox'><label for='data-633addbd-0c1c-4594-a6f1-09f908c7a208' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([560749.386974, 560750.386974, 560751.386974, ..., 562916.386974,\n",
+       "       562917.386974, 562918.386974])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.573e+06 8.573e+06 ... 8.571e+06</div><input id='attrs-67f33059-80a3-491e-82b3-418a190ab788' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67f33059-80a3-491e-82b3-418a190ab788' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3115c28-40e3-4a9c-96ce-91290cef6fe1' class='xr-var-data-in' type='checkbox'><label for='data-f3115c28-40e3-4a9c-96ce-91290cef6fe1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div><div class='xr-var-data'><pre>array([8573019.092218, 8573018.092218, 8573017.092218, ..., 8570812.092218,\n",
+       "       8570811.092218, 8570810.092218])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-161435da-6884-4edc-92c9-06f9a3e7482e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-161435da-6884-4edc-92c9-06f9a3e7482e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57315f56-c47a-4733-a3a9-5c6156643aa9' class='xr-var-data-in' type='checkbox'><label for='data-57315f56-c47a-4733-a3a9-5c6156643aa9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 24S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-39],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32724&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 24S</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-39.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>10000000.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 24S&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-39],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,10000000],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32724&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>560748.8869744056 1.0 0.0 8573019.592217797 0.0 -1.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c52d17aa-87e4-4bc3-b322-9239614aff8a' class='xr-section-summary-in' type='checkbox'  ><label for='section-c52d17aa-87e4-4bc3-b322-9239614aff8a' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6c5313f3-667b-49ae-a800-8302d06dda60' class='xr-index-data-in' type='checkbox'/><label for='index-6c5313f3-667b-49ae-a800-8302d06dda60' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([560749.3869744056, 560750.3869744056, 560751.3869744056,\n",
+       "       560752.3869744056, 560753.3869744056, 560754.3869744056,\n",
+       "       560755.3869744056, 560756.3869744056, 560757.3869744056,\n",
+       "       560758.3869744056,\n",
+       "       ...\n",
+       "       562909.3869744056, 562910.3869744056, 562911.3869744056,\n",
+       "       562912.3869744056, 562913.3869744056, 562914.3869744056,\n",
+       "       562915.3869744056, 562916.3869744056, 562917.3869744056,\n",
+       "       562918.3869744056],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=2170))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-574c28b2-a3c9-4f54-9a6c-b2bace315e1e' class='xr-index-data-in' type='checkbox'/><label for='index-574c28b2-a3c9-4f54-9a6c-b2bace315e1e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([8573019.092217797, 8573018.092217797, 8573017.092217797,\n",
+       "       8573016.092217797, 8573015.092217797, 8573014.092217797,\n",
+       "       8573013.092217797, 8573012.092217797, 8573011.092217797,\n",
+       "       8573010.092217797,\n",
+       "       ...\n",
+       "       8570819.092217797, 8570818.092217797, 8570817.092217797,\n",
+       "       8570816.092217797, 8570815.092217797, 8570814.092217797,\n",
+       "       8570813.092217797, 8570812.092217797, 8570811.092217797,\n",
+       "       8570810.092217797],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=2210))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c51d1f37-c3dc-4bca-af2f-f2f075e5f6e8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c51d1f37-c3dc-4bca-af2f-f2f075e5f6e8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'reclassify' (y: 2210, x: 2170)> Size: 38MB\n",
+       "array([[ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       [ 1,  1,  1, ...,  1,  1,  1],\n",
+       "       ...,\n",
+       "       [ 2,  2,  2, ..., 30, 30,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2],\n",
+       "       [ 2,  2,  2, ...,  2,  2,  2]])\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 17kB 5.607e+05 5.608e+05 ... 5.629e+05 5.629e+05\n",
+       "  * y            (y) float64 18kB 8.573e+06 8.573e+06 ... 8.571e+06 8.571e+06\n",
+       "    spatial_ref  int64 8B 0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load Smart Surface LULC layer\n",
+    "city_SmartSurfaceLULC = SmartSurfaceLULC().get_data(city_centroid_buffer.total_bounds)\n",
+    "city_SmartSurfaceLULC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0935811e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <style>\n",
+       "                .geemap-dark {\n",
+       "                    --jp-widgets-color: white;\n",
+       "                    --jp-widgets-label-color: white;\n",
+       "                    --jp-ui-font-color1: white;\n",
+       "                    --jp-layout-color2: #454545;\n",
+       "                    background-color: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-dark .jupyter-button {\n",
+       "                    --jp-layout-color3: #383838;\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab {\n",
+       "                    background-color: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "\n",
+       "                .geemap-colab .jupyter-button {\n",
+       "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
+       "                }\n",
+       "            </style>\n",
+       "            "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/weiqi_tori/anaconda3/envs/fenv/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f1277690d90>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "city_SmartSurfaceLULC.plot()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1276883d",
@@ -5459,4 +5996,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/tests/layers.py b/tests/layers.py
index adf01ea..417dd35 100644
--- a/tests/layers.py
+++ b/tests/layers.py
@@ -1,6 +1,6 @@
 import ee
 
-from city_metrix.layers import LandsatCollection2, Albedo, LandSurfaceTemperature, EsaWorldCover, EsaWorldCoverClass, TreeCover, AverageNetBuildingHeight, OpenStreetMap, OpenStreetMapClass, UrbanLandUse, OpenBuildings, TreeCanopyHeight, AlosDSM
+from city_metrix.layers import LandsatCollection2, Albedo, LandSurfaceTemperature, EsaWorldCover, EsaWorldCoverClass, TreeCover, AverageNetBuildingHeight, OpenStreetMap, OpenStreetMapClass, UrbanLandUse, OpenBuildings, TreeCanopyHeight, AlosDSM, SmartSurfaceLULC
 from city_metrix.layers.layer import get_image_collection
 from .conftest import MockLayer, MockMaskLayer, ZONES, LARGE_ZONES, MockLargeLayer, MockGroupByLayer, \
     MockLargeGroupByLayer
@@ -105,6 +105,11 @@ def test_tree_canopy_hight():
     count = TreeCanopyHeight().get_data(SAMPLE_BBOX).count()
     assert count
     
-def test_AlosDSM():
+def test_alos_dsm():
     mean = AlosDSM().get_data(SAMPLE_BBOX).mean()
     assert mean
+
+
+def test_smart_surface_lulc():
+    count = SmartSurfaceLULC().get_data(SAMPLE_BBOX).count()
+    assert count