keep sentinel. Apply changes

app.py

@@ -1,48 +1,19 @@
 import os
 import ee
 import json
+import geojson
 import geemap
 import geemap.foliumap as gee_folium
 import leafmap.foliumap as leaf_folium
 import streamlit as st
-from pandas import to_datetime, read_csv, merge, date_range, DateOffset
-from geopandas import read_file
+import pandas as pd
+import geopandas as gpd
 from shapely.ops import transform
 from functools import reduce
 import plotly.express as px
 
 st.set_page_config(layout="wide")
 
-############################################
-# One time setup
-############################################
-
-
-def initialize_ee():
-    credentials_path = os.path.expanduser("~/.config/earthengine/credentials")
-    if os.path.exists(credentials_path):
-        pass  # Earth Engine credentials already exist
-    elif "EE" in os.environ:  # write the credentials to the file
-        ee_credentials = os.environ.get("EE")
-        os.makedirs(os.path.dirname(credentials_path), exist_ok=True)
-        with open(credentials_path, "w") as f:
-            f.write(ee_credentials)
-    else:
-        raise ValueError(
-            f"Earth Engine credentials not found at {credentials_path} or in the environment variable 'EE'"
-        )
-
-    ee.Initialize()
-
-
-if "ee_initialized" not in st.session_state:
-    initialize_ee()
-    st.session_state.ee_initialized = True
-    
-if "wayback_mapping" not in st.session_state:
-    with open("wayback_imagery.json") as f:
-        st.session_state.wayback_mapping = json.load(f)
-
 
 ############################################
 # Functions
@@ -53,34 +24,60 @@ def shape_3d_to_2d(shape):
     else:
         return shape
 
-
 def preprocess_gdf(gdf):
     gdf = gdf.to_crs(epsg=4326)
-    gdf = gdf[['Name', 'geometry']]
+    gdf = gdf[["Name", "geometry"]]
     gdf["geometry"] = gdf["geometry"].apply(shape_3d_to_2d)
     return gdf
 
-
 def calculate_ndvi(image, nir_band, red_band):
     nir = image.select(nir_band)
     red = image.select(red_band)
     ndvi = (nir.subtract(red)).divide(nir.add(red)).rename("NDVI")
     return image.addBands(ndvi)
 
+def process_date(start_date, end_date, satellite):
+    try:
+        attrs = satellites[satellite]
+        collection = attrs["collection"]
+        collection = collection.filterBounds(ee_geometry)
+        collection = collection.filterDate(start_date, end_date)
+        mosaic = collection.qualityMosaic("NDVI")
+        fc = geemap.zonal_stats(
+            mosaic, ee_feature_collection, scale=attrs["scale"], return_fc=True
+        ).getInfo()
+        mean_ndvi = fc["features"][0]["properties"]["NDVI"]
+        if satellite == "COPERNICUS/S2_SR_HARMONIZED":
+            cloud_mask_probability = fc["features"][0]["properties"]["MSK_CLDPRB"] / 100
+        else:
+            cloud_mask_probability = None
+    except Exception as e:
+        print(e)
+        mosaic = None
+        mean_ndvi = None
+        cloud_mask_probability = None
+    return mosaic, mean_ndvi, cloud_mask_probability
+
 
 def postprocess_df(df, name):
     df = df.T
     df = df.reset_index()
     ndvi_df = df[df["index"].str.contains("NDVI")]
-    ndvi_df["index"] = to_datetime(ndvi_df["index"], format="%Y-%m_NDVI")
+    ndvi_df["index"] = pd.to_datetime(ndvi_df["index"], format="%Y-%m_NDVI")
     ndvi_df = ndvi_df.rename(columns={"index": "Date", 0: name})
-    
+
     cloud_mask_probability = df[df["index"].str.contains("MSK_CLDPRB")]
-    cloud_mask_probability["index"] = to_datetime(cloud_mask_probability["index"], format="%Y-%m_MSK_CLDPRB")
-    cloud_mask_probability = cloud_mask_probability.rename(columns={"index": "Date", 0: f"{name}_cloud_proba"})
+    cloud_mask_probability["index"] = pd.to_datetime(
+        cloud_mask_probability["index"], format="%Y-%m_MSK_CLDPRB"
+    )
+    cloud_mask_probability = cloud_mask_probability.rename(
+        columns={"index": "Date", 0: f"{name}_cloud_proba"}
+    )
     # normalize
-    cloud_mask_probability[f"{name}_cloud_proba"] = cloud_mask_probability[f"{name}_cloud_proba"] / 100
-    df = merge(ndvi_df, cloud_mask_probability, on="Date", how="outer")
+    cloud_mask_probability[f"{name}_cloud_proba"] = (
+        cloud_mask_probability[f"{name}_cloud_proba"] / 100
+    )
+    df = pd.merge(ndvi_df, cloud_mask_probability, on="Date", how="outer")
     return df
 
 
@@ -88,6 +85,75 @@ def write_info(info):
     st.write(f"<span style='color:#00FF00;'>{info}</span>", unsafe_allow_html=True)
 
 
+############################################
+# One time setup
+############################################
+
+
+def one_time_setup():
+    credentials_path = os.path.expanduser("~/.config/earthengine/credentials")
+    if os.path.exists(credentials_path):
+        pass  # Earth Engine credentials already exist
+    elif "EE" in os.environ:  # write the credentials to the file
+        ee_credentials = os.environ.get("EE")
+        os.makedirs(os.path.dirname(credentials_path), exist_ok=True)
+        with open(credentials_path, "w") as f:
+            f.write(ee_credentials)
+    else:
+        raise ValueError(
+            f"Earth Engine credentials not found at {credentials_path} or in the environment variable 'EE'"
+        )
+
+    ee.Initialize()
+
+    satellites = {
+        # "LANDSAT/LC08/C02/T1_TOA": {
+        #     "scale": 30,
+        #     "collection": ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
+        #     .select(["B2", "B3", "B4", "B5"], ["B", "G", "R", "NIR"])
+        #     .map(lambda image: calculate_ndvi(image, nir_band="NIR", red_band="R")),
+        # },
+        "COPERNICUS/S2_SR_HARMONIZED": {
+            "scale": 10,
+            "collection": ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
+            .select(
+                ["B4", "B8", "MSK_CLDPRB", "TCI_R", "TCI_G", "TCI_B"],
+                ["Red", "NIR", "MSK_CLDPRB", "R", "G", "B"],
+            )
+            .map(lambda image: calculate_ndvi(image, nir_band="NIR", red_band="Red")),
+        },
+        # "LANDSAT/LC09/C02/T1_L2": {
+        #     "scale": 30,
+        #     "collection": ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")
+        #     .select(["SR_B2", "SR_B3", "SR_B4", "SR_B5"], ["B", "G", "R", "NIR"])
+        #     .map(lambda image: calculate_ndvi(image, nir_band="NIR", red_band="R")),
+        # },
+        # "LANDSAT/LC08/C02/T1_L2": {
+        #     "scale": 30,
+        #     "collection": ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")
+        #     .select(["SR_B2", "SR_B3", "SR_B4", "SR_B5"], ["B", "G", "R", "NIR"])
+        #     .map(lambda image: calculate_ndvi(image, nir_band="NIR", red_band="R")),
+        # },
+        # "LANDSAT/LE07/C02/T1_L2": {
+        #     "scale": 30,
+        #     "collection": ee.ImageCollection("LANDSAT/LE07/C02/T1_L2")
+        #     .select(["SR_B2", "SR_B3", "SR_B4", "SR_B5"], ["B", "G", "R", "NIR"])
+        #     .map(lambda image: calculate_ndvi(image, nir_band="NIR", red_band="R")),
+        # },
+    }
+    st.session_state.satellites = satellites
+    with open("wayback_imagery.json") as f:
+        st.session_state.wayback_mapping = json.load(f)
+
+
+if "one_time_setup_done" not in st.session_state:
+    one_time_setup()
+    st.session_state.one_time_setup_done = True
+else:
+    satellites = st.session_state.satellites
+    wayback_mapping = st.session_state.wayback_mapping
+
+
 ############################################
 # App
 ############################################
@@ -96,166 +162,162 @@ def write_info(info):
 # make title in center
 st.markdown(
     f"""
-    <h1 style="text-align: center;">Mean NDVI Calculator</h1>
+    <h1 style="text-align: center;">NDVI Explorer</h1>
     """,
     unsafe_allow_html=True,
 )
 
 # Input: Date and Cloud Cover
-col = st.columns(2)
-start_date = col[0].date_input("Start Date", value=to_datetime("2021-01-01"))
-end_date = col[1].date_input("End Date", value=to_datetime("2021-07-31"))
-start_date = start_date.strftime("%Y-%m")
-end_date = end_date.strftime("%Y-%m")
-# max_cloud_cover = st.number_input("Max Cloud Cover (in percentage)", value=5)
+col = st.columns(4)
+start_year = col[0].selectbox("Start Year", list(range(2014, 2024)), index=8)
+start_month = col[1].selectbox("Start Month", list(range(1, 13)), index=0)
+end_year = col[2].selectbox("End Year", list(range(2014, 2024)), index=8)
+end_month = col[3].selectbox("End Month", list(range(1, 13)), index=2)
+start_date = f"{start_year}-{start_month:02d}"
+end_date = f"{end_year}-{end_month:02d}"
 
 # Input: GeoJSON/KML file
 uploaded_file = st.file_uploader("Upload KML/GeoJSON file", type=["geojson", "kml"])
 if uploaded_file is None:
     st.stop()
 
-file_name = uploaded_file.name
-gdf = read_file(uploaded_file)
-gdf = preprocess_gdf(gdf)
+gdf = preprocess_gdf(gpd.read_file(uploaded_file))
+
+# Input: Geometry
+selected_geometry = st.selectbox("Select the geometry", gdf.Name.values)
 
-selected_shape = st.selectbox("Select the geometry", gdf.Name.values)
-if selected_shape is None:
+selected_geometry = gdf[gdf.Name == selected_geometry].iloc[0].geometry
+if selected_geometry.type != "Polygon":
+    st.error(
+        f"Selected geometry is of type {selected_geometry.type}. Please provide a polygon geometry."
+    )
     st.stop()
 
-selected_shape = gdf[gdf.Name == selected_shape]
-ee_object = geemap.gdf_to_ee(selected_shape)
-write_info(f"Type of Geometry: {selected_shape.geometry.type.values[0]}")
+# Derived Inputs
+selected_geometry = selected_geometry.__geo_interface__
+ee_geometry = ee.Geometry(selected_geometry)
+ee_feature_collection = ee.FeatureCollection(ee_geometry)
+feature_collection = geojson.FeatureCollection([{"type": "Feature", "geometry": selected_geometry, "properties": {}}])
 
+# Input: Satellite Sources
 st.write("Select the satellite sources:")
-satellites = {
-    "LANDSAT/LC08/C02/T1_TOA": {
-        "selected": st.checkbox("LANDSAT/LC08/C02/T1_TOA", value=True),
-        "nir_band": "B5",
-        "red_band": "B4",
-        "scale": 30,
-    },
-    "COPERNICUS/S2_SR_HARMONIZED": {
-        "selected": st.checkbox("COPERNICUS/S2_SR_HARMONIZED", value=True),
-        "nir_band": "B8",
-        "red_band": "B4",
-        "scale": 10,
-    },
-}
+satellite_selected = {}
+for satellite in satellites:
+    satellite_selected[satellite] = st.checkbox(satellite, value=True)
 
+# Submit
 submit = st.button("Submit", use_container_width=True)
 if submit:
-    if not any(satellites.values()):
+    if not any(satellite_selected.values()):
         st.error("Please select at least one satellite source")
         st.stop()
 
     # Create month range
-    dates = date_range(start_date, end_date, freq="MS").strftime("%Y-%m-%d").tolist()
+    dates = pd.date_range(start_date, end_date, freq="MS").strftime("%Y-%m").tolist()
     write_info(
         f"Start Date (inclusive): {start_date}, End Date (exclusive): {end_date}"
     )
-
-    df_list = []
-    collections = {}
+    result = {key: {} for key in satellites}
     for satellite, attrs in satellites.items():
-        if not attrs["selected"]:
+        if not satellite_selected[satellite]:
             continue
-        collection = ee.ImageCollection(satellite)
-        collection = collection.filterBounds(ee_object)
-        if satellite == "COPERNICUS/S2_SR_HARMONIZED":
-            collection = collection.select([attrs["red_band"], attrs["nir_band"], "MSK_CLDPRB"])
-        else:
-            collection = collection.select([attrs["red_band"], attrs["nir_band"]])
-        # collection = collection.filter(ee.Filter.lt(attrs["cloud_cover_var"], max_cloud_cover))
-        collection = collection.filterDate(start_date, end_date)
-        collection = collection.map(
-            lambda image: calculate_ndvi(
-                image, nir_band=attrs["nir_band"], red_band=attrs["red_band"]
-            )
-        )
-        write_info(f"Number of images in {satellite}: {collection.size().getInfo()}")
-        
-        progress_bar = st.progress(0)
-
-        def monthly_quality_mosaic(start, end, i):
-            progress_bar.progress((i + 1) / (len(dates) - 1))
-            collection_filtered = collection.filterDate(start, end)
-            size = collection_filtered.size().getInfo()
-            if size == 0:
-                return None
-            mosaic = collection_filtered.qualityMosaic("NDVI")
-            month = to_datetime(start).strftime("%Y-%m")
-            print(f"Processing {month} with {size} images")
-            return mosaic.set("system:index", f"{month}")
-            
-        collection = [monthly_quality_mosaic(start, end, i) for i, (start, end) in enumerate(zip(dates[:-1], dates[1:]))]
-        collection = list(filter(None, collection))
-        collection = ee.ImageCollection(collection)
-        collections[satellite] = collection
-
-        save_name = satellite.replace("/", "_")
-        geemap.zonal_stats(
-            collection,#.select(["NDVI"]),
-            ee_object,
-            f"/tmp/{save_name}.csv",
-            stat_type="mean",
-            scale=attrs["scale"],
+
+        with st.spinner(f"Processing {satellite} ..."):
+            progress_bar = st.progress(0)
+            for i, (start, end) in enumerate(zip(dates[:-1], dates[1:])):
+                mosaic, mean_ndvi, cloud_proba = process_date(start, end, satellite)
+                result[satellite][start] = {
+                    "mosaic": mosaic,
+                    "mean_ndvi": mean_ndvi,
+                    "cloud_mask_probability": cloud_proba,
+                }
+                progress_bar.progress((i + 1) / (len(dates) - 1))
+
+    st.session_state.result = result
+
+if "result" in st.session_state:
+    result = st.session_state.result
+
+    df_list = []
+    for satellite, satellite_result in result.items():
+        satellite_df = pd.DataFrame(satellite_result).T
+        satellite_df.rename(
+            columns={
+                "mean_ndvi": f"NDVI_{satellite}",
+                "mosaic": f"Mosaic_{satellite}",
+                "cloud_mask_probability": f"Cloud_{satellite}",
+            },
+            inplace=True,
         )
-        df = read_csv(f"/tmp/{save_name}.csv")
-        df = postprocess_df(df, name=satellite)
-        df_list.append(df)
-
-    df = reduce(lambda left, right: merge(left, right, on="Date", how="outer"), df_list)
-    df = df.sort_values("Date")
-    
-    # drop rows with all NaN values
-    df = df.dropna(how="all")
-    # drop columns with all NaN values
-    df = df.dropna(axis=1, how="all")
-    df = df.reset_index(drop=True)
-    
-    st.session_state.df = df
-    st.session_state.collections = collections
-
-if "df" in st.session_state:
-    df = st.session_state.df
-    collections = st.session_state.collections
-    st.write(df.applymap(lambda x: f"{x:.2f}" if isinstance(x, float) else x))
-
-    fig = px.line(df, x="Date", y=df.columns[1:], title='Mean NDVI', markers=True)
-    fig.update_yaxes(range=[0, 1])
+        # drop rows with all NaN values
+        satellite_df = satellite_df.dropna(how="all")
+        # drop columns with all NaN values
+        satellite_df = satellite_df.dropna(axis=1, how="all")
+        df_list.append(satellite_df)
+
+    # merge outer on index of the dataframes
+    df = reduce(
+        lambda left, right: pd.merge(
+            left, right, left_index=True, right_index=True, how="outer"
+        ),
+        df_list,
+    )
+    df.reset_index(inplace=True)
+    df.index = pd.to_datetime(df["index"], format="%Y-%m")
+    for column in df.columns:
+        df[column] = pd.to_numeric(df[column], errors="ignore")
+
+    df_numeric = df.select_dtypes(include=["float64"])
+
+    fig = px.line(df, y=df_numeric.columns, title="Mean NDVI", markers=True)
+    fig.update_yaxes(range=[-1, 1])
     st.plotly_chart(fig)
-    
+
     st.subheader("Visual Inspection")
-    write_info(f"Centroid of the selected geometry: {selected_shape.geometry.centroid.values[0]}")
+    _, lonlat = ee_geometry.centroid().getInfo().values()
+    lon, lat = lonlat
+    write_info(f"Centroid of the selected geometry (lat, lon): ({lat}, {lon})")
     cols = st.columns(2)
+    df_dates = df.index.strftime("%Y-%m").tolist()
     with cols[0]:
-        start_date = st.selectbox("Start Date", df.Date, index=0)
-        start_date_index = df[df.Date == start_date].index[0].item()
+        date_1 = st.selectbox("Month 1", df_dates, index=0)
     with cols[1]:
-        end_date = st.selectbox("End Date", df.Date, index=len(df.Date) - 1)
-        end_date_index = df[df.Date == end_date].index[0].item()
-    
-    for imagery in satellites:
-        collection = collections[imagery]
-        for col, date in zip(cols, [start_date, end_date]):
-            date_index = df[df.Date == date].index[0].item()
-            image = ee.Image(collections[imagery].toList(collection.size()).get(date_index))
-            layer = gee_folium.ee_tile_layer(image, {"bands": ["NDVI"], "min": 0, "max": 1}, f"{imagery}_{date}")
+        date_2 = st.selectbox("Month 2", df_dates, index=len(df.index) - 1)
 
+    for satellite in satellites:
+        for col, date in zip(cols, [date_1, date_2]):
+            if f"Mosaic_{satellite}" not in df.columns:
+                continue
+            mosaic = df.loc[pd.to_datetime(date), f"Mosaic_{satellite}"]
             with col:
-                m = leaf_folium.Map()
-                m.add_layer(layer)
-                m.add_gdf(selected_shape, layer_name="Selected Geometry")
-                colors = ["#000000", "#FFFFFF"]
-                m.add_colorbar(colors, vmin=0, vmax=1)
-                st.write(f"{imagery} - {date}")
-                m.to_streamlit()
-    
-    for col, date in zip(cols, [start_date, end_date]):
-        esri_date = min(st.session_state.wayback_mapping.keys(), key=lambda x: abs(to_datetime(x) - date))
+                maps = [leaf_folium.Map(), leaf_folium.Map()]
+                ndvi_layer = gee_folium.ee_tile_layer(mosaic, {"bands": ["NDVI"], "min": 0, "max": 1})
+                
+                if satellite == "COPERNICUS/S2_SR_HARMONIZED":
+                    min_all = 0
+                    max_all = 255
+                else:
+                    raise ValueError(f"Unknown satellite: {satellite}")
+                visual_layer = gee_folium.ee_tile_layer(mosaic, {"bands": ["R", "G", "B"], "min": min_all, "max": max_all})
+
+                maps[0].add_layer(
+                    ndvi_layer,
+                )
+                    
+                maps[1].add_layer(
+                    visual_layer,
+                )
+
+                for m, name in zip(maps, ["NDVI", "Visual"]):
+                    m.add_geojson(feature_collection)
+                    write_info(f"{name}: {satellite} - {date}")
+                    m.to_streamlit()
+
+    for col, date in zip(cols, [date_1, date_2]):
+        esri_date = min(wayback_mapping.keys(), key=lambda x: abs(pd.to_datetime(x) - pd.to_datetime(date)))
         with col:
             m = leaf_folium.Map()
-            m.add_tile_layer(st.session_state.wayback_mapping[esri_date], name=f"Esri Wayback Imagery - {esri_date}", attribution="Esri")
-            m.add_gdf(selected_shape, layer_name="Selected Geometry")
-            st.write(f"Esri Wayback Imagery - {esri_date} (Closest to {date})")
-            m.to_streamlit()
+            m.add_tile_layer(wayback_mapping[esri_date], name=f"Esri Wayback Imagery - {esri_date}", attribution="Esri")
+            m.add_geojson(feature_collection)
+            write_info(f"Visual Esri Wayback Basemap - {esri_date} (Closest to {date})")
+            m.to_streamlit()

sandbox.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -45,12 +45,299 @@
     "import geemap\n",
     "import geopandas as gpd\n",
     "from shapely.ops import transform\n",
-    "ee.Initialize()"
+    "ee.Initialize()\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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"
+    }
+   ],
+   "source": [
+    "import geemap.foliumap as gee_folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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"
+    }
+   ],
+   "source": [
+    "m = gee_folium.Map()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2.75e-05"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2.75e-05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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": [
+      "\u001b[0;31mSignature:\u001b[0m\n",
+      "\u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddLayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mee_object\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mvis_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Layer untitled'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mshown\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mopacity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mSource:\u001b[0m   \n",
+      "    \u001b[0;32mdef\u001b[0m \u001b[0madd_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mee_object\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mvis_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Layer untitled\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mshown\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mopacity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Adds a given EE object to the map as a layer.\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m        Args:\u001b[0m\n",
+      "\u001b[0;34m            ee_object (Collection|Feature|Image|MapId): The object to add to the map.\u001b[0m\n",
+      "\u001b[0;34m            vis_params (dict, optional): The visualization parameters. Defaults to {}.\u001b[0m\n",
+      "\u001b[0;34m            name (str, optional): The name of the layer. Defaults to 'Layer untitled'.\u001b[0m\n",
+      "\u001b[0;34m            shown (bool, optional): A flag indicating whether the layer should be on by default. Defaults to True.\u001b[0m\n",
+      "\u001b[0;34m            opacity (float, optional): The layer's opacity represented as a number between 0 and 1. Defaults to 1.\u001b[0m\n",
+      "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mlayer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEEFoliumTileLayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mee_object\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvis_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshown\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopacity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m        \u001b[0marc_add_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl_format\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshown\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopacity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFile:\u001b[0m      /opt/anaconda3/envs/zeel_py310/lib/python3.10/site-packages/geemap/foliumap.py\n",
+      "\u001b[0;31mType:\u001b[0m      method"
+     ]
+    }
+   ],
+   "source": [
+    "m.addLayer??"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Index(['2023-01', '2023-02', '2023-03', '2023-04', '2023-05', '2023-06',\n",
+       "       '2023-07', '2023-08', '2023-09', '2023-10', '2023-11', '2023-12',\n",
+       "       '2024-01'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "start = \"2023-01\"\n",
+    "end = \"2024-01\"\n",
+    "pd.date_range(start, end, freq='MS').strftime(\"%Y-%m\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -86,6 +373,128 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'type': 'Polygon', 'coordinates': (((73.19791470947145, 24.01977640858627), (73.1951165609439, 24.01754094149261), (73.1955205367043, 24.01738340683521), (73.19480033400262, 24.01523619312771), (73.19463695073262, 24.01440311871703), (73.19454286010809, 24.0139003560814), (73.1942443392165, 24.0126807914708), (73.1942341944796, 24.0126863557728), (73.19653511275247, 24.01252023885471), (73.19648432210984, 24.01217871972386), (73.19764861281237, 24.01199930187962), (73.1981728693097, 24.01433254741455), (73.1986819814156, 24.01611200638915), (73.19959745133227, 24.01895266153662), (73.19791470947145, 24.01977640858627)),)}\n",
+      "<class 'dict'>\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>:root {\n",
+       "  --font-color-primary: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --font-color-secondary: var(--jp-content-font-color2, rgba(0, 0, 0, 0.6));\n",
+       "  --font-color-accent: rgba(123, 31, 162, 1);\n",
+       "  --border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --background-color: var(--jp-layout-color0, white);\n",
+       "  --background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --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",
+       "  --font-color-primary: rgba(255, 255, 255, 1);\n",
+       "  --font-color-secondary: rgba(255, 255, 255, 0.6);\n",
+       "  --font-color-accent: rgb(173, 132, 190);\n",
+       "  --border-color: #2e2e2e;\n",
+       "  --background-color: #111111;\n",
+       "  --background-color-row-even: #111111;\n",
+       "  --background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".ee {\n",
+       "  padding: 1em;\n",
+       "  line-height: 1.5em;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 1200px;\n",
+       "  overflow-y: scroll;\n",
+       "  max-height: 600px;\n",
+       "  border: 1px solid var(--border-color);\n",
+       "  font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".ee li {\n",
+       "  list-style-type: none;\n",
+       "}\n",
+       "\n",
+       ".ee ul {\n",
+       "  padding-left: 1.5em !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".ee > ul {\n",
+       "  padding-left: 0 !important;\n",
+       "}\n",
+       "\n",
+       ".ee-open,\n",
+       ".ee-shut {\n",
+       "  color: var(--font-color-secondary);\n",
+       "  cursor: pointer;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".ee-open:hover,\n",
+       ".ee-shut:hover {\n",
+       "  color: var(--font-color-primary);\n",
+       "}\n",
+       "\n",
+       ".ee-k {\n",
+       "  color: var(--font-color-accent);\n",
+       "  margin-right: 6px;\n",
+       "}\n",
+       "\n",
+       ".ee-v {\n",
+       "  color: var(--font-color-primary);\n",
+       "}\n",
+       "\n",
+       ".ee-toggle {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".ee-shut + ul {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".ee-open + ul {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".ee-shut::before {\n",
+       "  display: inline-block;\n",
+       "  content: \"▼\";\n",
+       "  margin-right: 6px;\n",
+       "  transform: rotate(-90deg);\n",
+       "  transition: transform 0.2s;\n",
+       "}\n",
+       "\n",
+       ".ee-open::before {\n",
+       "  transform: rotate(0deg);\n",
+       "  display: inline-block;\n",
+       "  content: \"▼\";\n",
+       "  margin-right: 6px;\n",
+       "  transition: transform 0.2s;\n",
+       "}\n",
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+       "    const parent = this.parentElement;\n",
+       "    parent.className = parent.className === \"ee-open\" ? \"ee-shut\" : \"ee-open\";\n",
+       "}\n",
+       "\n",
+       "for (let c of document.getElementsByClassName(\"ee-toggle\")) {\n",
+       "    c.onclick = toggleHeader;\n",
+       "}</script></div>"
+      ],
+      "text/plain": [
+       "<ee.featurecollection.FeatureCollection at 0x7faa000c3160>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -98,13 +507,17 @@
     "        return shape\n",
     "gdf['geometry'] = gdf['geometry'].apply(poly_3d_to_2d)\n",
     "gdf = gdf.to_crs(epsg=4326)\n",
-    "first_item = gdf.iloc[0:1]\n",
-    "ee_object = geemap.gdf_to_ee(first_item)"
+    "first_item = gdf.iloc[0].geometry.__geo_interface__\n",
+    "print(first_item)\n",
+    "print(type(first_item))\n",
+    "ee_geometry = ee.Geometry(first_item)\n",
+    "ee_feature_collection = ee.FeatureCollection(ee_geometry)\n",
+    "ee_feature_collection\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -145,14 +558,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of images in the collection:  2\n"
+      "['LANDSAT/LC08/C02/T1_TOA/LC08_148043_20230405', 'LANDSAT/LC08/C02/T1_TOA/LC08_148043_20230421', 'LANDSAT/LC08/C02/T1_TOA/LC08_148043_20230507', 'LANDSAT/LC08/C02/T1_TOA/LC08_148043_20230523', 'LANDSAT/LC08/C02/T1_TOA/LC08_148043_20230608', 'LANDSAT/LC08/C02/T1_TOA/LC08_148043_20230624', 'LANDSAT/LC08/C02/T1_TOA/LC08_148043_20230811', 'LANDSAT/LC08/C02/T1_TOA/LC08_148043_20230827', 'LANDSAT/LC08/C02/T1_TOA/LC08_148044_20230405', 'LANDSAT/LC08/C02/T1_TOA/LC08_148044_20230421', 'LANDSAT/LC08/C02/T1_TOA/LC08_148044_20230507', 'LANDSAT/LC08/C02/T1_TOA/LC08_148044_20230523', 'LANDSAT/LC08/C02/T1_TOA/LC08_148044_20230608', 'LANDSAT/LC08/C02/T1_TOA/LC08_148044_20230811', 'LANDSAT/LC08/C02/T1_TOA/LC08_148044_20230827']\n"
      ]
     }
    ],
    "source": [
-    "dataset = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA').filterBounds(ee_object).filterDate('2023-01-01', '2023-01-31')\n",
-    "true_color_432 = dataset.select(['B4', 'B3', 'B2'])\n",
-    "print(\"Number of images in the collection: \", true_color_432.size().getInfo())"
+    "dataset = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA').filterBounds(ee_geometry).filterDate('2023-04', '2023-09')\n",
+    "# print image IDs\n",
+    "image_ids = dataset.aggregate_array('system:id').getInfo()\n",
+    "print(image_ids)\n",
+    "\n",
+    "# true_color_432 = dataset.select(['B4', 'B3', 'B2'])\n",
+    "# print(\"Number of images in the collection: \", true_color_432.size().getInfo())"
    ]
   },
   {
@@ -217,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -258,16 +675,114 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computing statistics ...\n",
-      "Generating URL ...\n",
-      "Downloading data from https://earthengine.googleapis.com/v1/projects/815228341823/tables/8a81a210a87d3b3a27187b606616f13c-f5c9d2fc785b6f388b63270382c578e4:getFeatures\n",
-      "Please wait ...\n",
-      "Data downloaded to /home/patel_zeel/Mean_NDVI/zonal_stats.csv\n"
+      "Computing statistics ...\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'type': 'FeatureCollection',\n",
+       " 'columns': {'mean': 'Float', 'system:index': 'String'},\n",
+       " 'features': [{'type': 'Feature',\n",
+       "   'geometry': {'type': 'Polygon',\n",
+       "    'coordinates': [[[73.19791470947145, 24.01977640858627],\n",
+       "      [73.1951165609439, 24.01754094149261],\n",
+       "      [73.1955205367043, 24.01738340683521],\n",
+       "      [73.19480033400262, 24.01523619312771],\n",
+       "      [73.19463695073262, 24.01440311871703],\n",
+       "      [73.19454286010809, 24.0139003560814],\n",
+       "      [73.1942443392165, 24.0126807914708],\n",
+       "      [73.1942341944796, 24.0126863557728],\n",
+       "      [73.19653511275247, 24.01252023885471],\n",
+       "      [73.19648432210984, 24.01217871972386],\n",
+       "      [73.19764861281237, 24.01199930187962],\n",
+       "      [73.1981728693097, 24.01433254741455],\n",
+       "      [73.1986819814156, 24.01611200638915],\n",
+       "      [73.19959745133227, 24.01895266153662],\n",
+       "      [73.19791470947145, 24.01977640858627]]]},\n",
+       "   'id': '0',\n",
+       "   'properties': {'mean': 0.7695880858277654}}]}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "geemap.zonal_stats(dataset.qualityMosaic(\"B4\").select([\"B4\"]), ee_feature_collection, statistics_type='mean', scale=10, return_fc=True).getInfo()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "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"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'type': 'Feature',\n",
+       "  'geometry': {'type': 'Polygon',\n",
+       "   'coordinates': [[[73.19791470947145, 24.01977640858627],\n",
+       "     [73.1951165609439, 24.01754094149261],\n",
+       "     [73.1955205367043, 24.01738340683521],\n",
+       "     [73.19480033400262, 24.01523619312771],\n",
+       "     [73.19463695073262, 24.01440311871703],\n",
+       "     [73.19454286010809, 24.0139003560814],\n",
+       "     [73.1942443392165, 24.0126807914708],\n",
+       "     [73.1942341944796, 24.0126863557728],\n",
+       "     [73.19653511275247, 24.01252023885471],\n",
+       "     [73.19648432210984, 24.01217871972386],\n",
+       "     [73.19764861281237, 24.01199930187962],\n",
+       "     [73.1981728693097, 24.01433254741455],\n",
+       "     [73.1986819814156, 24.01611200638915],\n",
+       "     [73.19959745133227, 24.01895266153662],\n",
+       "     [73.19791470947145, 24.01977640858627]]]},\n",
+       "  'id': '0',\n",
+       "  'properties': {'mean': 0.7695880858277654}}]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "features = geemap.zonal_stats(dataset.qualityMosaic(\"B4\").select(\"B4\"), ee_object, statistics_type='mean', scale=10)"
+    "features.getInfo()['features']"
    ]
   },
   {

zonal_stats.csv

@@ -1,2 +1,2 @@
-mean,system:index,Description,Name
-0.08817322105067514,0,<div><b>kelava irrigation he 25&nbsp;</b><br></div><div><b>Year 2022-23</b><br></div>,kelava 25 he
+mean,system:index
+0.7695880858277654,0