first commit

Bhankhara Df 11 he 5 2020-21.kml

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+<Document>
+	<name>Bhankhara Df 11 he 5 2020-21</name>
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Bhankhara_Df_11_he_5_2020-21.geojson

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+}

Modhari_he_10_2024-25_1.geojson

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+{
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+"name": "Modhari he 10 2024-25",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
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+]
+}

README.md

@@ -1,13 +1,13 @@
 ---
-title: Mean NDVI V2
-emoji: 🔥
-colorFrom: pink
-colorTo: yellow
+title: Mean NDVI
+emoji: 📊
+colorFrom: gray
+colorTo: blue
 sdk: streamlit
 sdk_version: 1.39.0
 app_file: app.py
 pinned: false
-license: cc-by-4.0
+license: cc-by-nc-4.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,259 @@
+import os
+import ee
+import json
+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
+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
+############################################
+def shape_3d_to_2d(shape):
+    if shape.has_z:
+        return transform(lambda x, y, z: (x, y), shape)
+    else:
+        return shape
+
+
+def preprocess_gdf(gdf):
+    gdf = gdf.to_crs(epsg=4326)
+    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 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 = 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"})
+    # 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")
+    return df
+
+
+def write_info(info):
+    st.write(f"<span style='color:#00FF00;'>{info}</span>", unsafe_allow_html=True)
+
+
+############################################
+# App
+############################################
+
+# Title
+# make title in center
+st.markdown(
+    f"""
+    <h1 style="text-align: center;">Mean NDVI Calculator</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)
+
+# 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)
+
+selected_shape = st.selectbox("Select the geometry", gdf.Name.values)
+if selected_shape is None:
+    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]}")
+
+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,
+    },
+}
+
+submit = st.button("Submit", use_container_width=True)
+if submit:
+    if not any(satellites.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()
+    write_info(
+        f"Start Date (inclusive): {start_date}, End Date (exclusive): {end_date}"
+    )
+
+    df_list = []
+    collections = {}
+    for satellite, attrs in satellites.items():
+        if not attrs["selected"]:
+            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"],
+        )
+        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")
+    
+    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])
+    st.plotly_chart(fig)
+    
+    st.subheader("Visual Inspection")
+    
+    cols = st.columns(2)
+    with cols[0]:
+        start_date = st.selectbox("Start Date", df.Date, index=0)
+        start_date_index = df[df.Date == start_date].index[0].item()
+    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]        
+        m_list = []
+        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}")
+
+            with col:
+                m = leaf_folium.Map()
+                m.add_layer(layer)
+                m.add_gdf(selected_shape, layer_name="Selected Geometry")
+                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))
+        with col:
+            m = leaf_folium.Map()
+            m.add_tile_layer(st.session_state.wayback_mapping[esri_date], name=f"Esri Wayback Imagery - {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()

credentials

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dezkvf.geojson

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+"features": [
+{ "type": "Feature", "properties": { "Name": "kelava 25 he", "Description": "<div><b>kelava irrigation he 25&nbsp;</b><br></div><div><b>Year 2022-23</b><br></div>" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 73.197914709471448, 24.019776408586271, 0.0 ], [ 73.195116560943902, 24.017540941492609, 0.0 ], [ 73.195520536704294, 24.017383406835211, 0.0 ], [ 73.194800334002622, 24.015236193127709, 0.0 ], [ 73.194636950732615, 24.014403118717031, 0.0 ], [ 73.194542860108086, 24.0139003560814, 0.0 ], [ 73.194244339216496, 24.0126807914708, 0.0 ], [ 73.194234194479606, 24.0126863557728, 0.0 ], [ 73.196535112752471, 24.012520238854709, 0.0 ], [ 73.196484322109839, 24.01217871972386, 0.0 ], [ 73.197648612812372, 24.011999301879619, 0.0 ], [ 73.198172869309701, 24.014332547414551, 0.0 ], [ 73.198681981415604, 24.01611200638915, 0.0 ], [ 73.199597451332266, 24.01895266153662, 0.0 ], [ 73.197914709471448, 24.019776408586271, 0.0 ] ] ] } },
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+{ "type": "Feature", "properties": { "Name": "Golvada he 10", "Description": "" }, "geometry": { "type": "Point", "coordinates": [ 73.262362465970142, 23.947286317747501, 348.096590272880121 ] } }
+]
+}

golvada he 10 2023-24.kml

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+<?xml version="1.0" encoding="UTF-8"?>
+<kml xmlns="http://www.opengis.net/kml/2.2" xmlns:gx="http://www.google.com/kml/ext/2.2" xmlns:kml="http://www.opengis.net/kml/2.2" xmlns:atom="http://www.w3.org/2005/Atom">
+<Document>
+	<name>golvada he 10 2023-24</name>
+	<StyleMap id="__managed_style_689C019E91332BD1EC88">
+		<Pair>
+			<key>normal</key>
+			<styleUrl>#__managed_style_79BDFF2A5D332BD1EC88</styleUrl>
+		</Pair>
+		<Pair>
+			<key>highlight</key>
+			<styleUrl>#__managed_style_8DE467F478332BD1EC88</styleUrl>
+		</Pair>
+	</StyleMap>
+	<gx:CascadingStyle kml:id="__managed_style_550D7CA50D332BD1EC88">
+		<Style>
+			<IconStyle>
+				<scale>1.2</scale>
+				<Icon>
+					<href>https://earth.google.com/earth/rpc/cc/icon?color=1976d2&amp;id=2000&amp;scale=4</href>
+				</Icon>
+				<hotSpot x="64" y="128" xunits="pixels" yunits="insetPixels"/>
+			</IconStyle>
+			<LabelStyle>
+			</LabelStyle>
+			<LineStyle>
+				<color>ff2dc0fb</color>
+				<width>4.8772</width>
+			</LineStyle>
+			<PolyStyle>
+				<color>00ffffff</color>
+			</PolyStyle>
+			<BalloonStyle>
+				<displayMode>hide</displayMode>
+			</BalloonStyle>
+		</Style>
+	</gx:CascadingStyle>
+	<gx:CascadingStyle kml:id="__managed_style_490CBAF8C4332BD1EC88">
+		<Style>
+			<IconStyle>
+				<Icon>
+					<href>https://earth.google.com/earth/rpc/cc/icon?color=1976d2&amp;id=2000&amp;scale=4</href>
+				</Icon>
+				<hotSpot x="64" y="128" xunits="pixels" yunits="insetPixels"/>
+			</IconStyle>
+			<LabelStyle>
+			</LabelStyle>
+			<LineStyle>
+				<color>ff2dc0fb</color>
+				<width>3.25146</width>
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+	<gx:CascadingStyle kml:id="__managed_style_19AFB589B7332BD1EC88">
+		<styleUrl>https://earth.google.com/balloon_components/base/1.0.26.0/card_template.kml#main</styleUrl>
+		<Style>
+			<IconStyle>
+				<Icon>
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+				</Icon>
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+		<styleUrl>https://earth.google.com/balloon_components/base/1.0.26.0/card_template.kml#main</styleUrl>
+		<Style>
+			<IconStyle>
+				<scale>1.2</scale>
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+				</Icon>
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+			<LabelStyle>
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+			<BalloonStyle>
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requirements.txt

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+fastkml
+geopandas
+geemap
+pandas
+geojson

small_app.py

@@ -0,0 +1,19 @@
+import streamlit as st
+# full width display
+st.set_page_config(layout="wide")
+
+import leafmap.foliumap as leafmap
+
+layers = ["Esri.WorldTopoMap", "OpenTopoMap"]
+m1 = leafmap.Map()
+m1.add_basemap("Esri.WorldTopoMap")
+
+m2 = leafmap.Map()
+m2.add_basemap("OpenTopoMap")
+
+cols = st.columns(2)
+with cols[0]:
+    m1.to_streamlit()
+with cols[1]:
+    m2.to_streamlit()
+# leafmap.linked_maps(rows=1, cols=2, height="400px", layers=layers)

tmp.csv

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+20210112T054211_20210112T054208_T43QCG_NDVI,20210117T054149_20210117T054151_T43QCG_NDVI,20210122T054131_20210122T054132_T43QCG_NDVI,20210127T054109_20210127T055157_T43QCG_NDVI,system:index,Name
+0.5412110887471273,0.49817145677100677,0.5045362449588447,0.4920719918276227,0,bhankhara Df 

tmp.geojson

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+]
+}

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+  "2016-01-13": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/3515/{z}/{y}/{x}",
+  "2015-12-16": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/28163/{z}/{y}/{x}",
+  "2015-11-18": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/8781/{z}/{y}/{x}",
+  "2015-10-28": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/11092/{z}/{y}/{x}",
+  "2015-10-14": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/10850/{z}/{y}/{x}",
+  "2015-09-30": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/3630/{z}/{y}/{x}",
+  "2015-09-16": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/1431/{z}/{y}/{x}",
+  "2015-09-02": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/30584/{z}/{y}/{x}",
+  "2015-08-19": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/28219/{z}/{y}/{x}",
+  "2015-07-08": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/24007/{z}/{y}/{x}",
+  "2015-06-24": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/11952/{z}/{y}/{x}",
+  "2015-05-13": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/19930/{z}/{y}/{x}",
+  "2015-04-30": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/23880/{z}/{y}/{x}",
+  "2015-04-15": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/9203/{z}/{y}/{x}",
+  "2015-03-25": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/2730/{z}/{y}/{x}",
+  "2015-03-18": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/15084/{z}/{y}/{x}",
+  "2015-02-18": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/10443/{z}/{y}/{x}",
+  "2015-01-21": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/20222/{z}/{y}/{x}",
+  "2014-12-30": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/5844/{z}/{y}/{x}",
+  "2014-12-18": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/14720/{z}/{y}/{x}",
+  "2014-12-03": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/23383/{z}/{y}/{x}",
+  "2014-11-12": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/30195/{z}/{y}/{x}",
+  "2014-10-29": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/11019/{z}/{y}/{x}",
+  "2014-10-01": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/22692/{z}/{y}/{x}",
+  "2014-09-17": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/25586/{z}/{y}/{x}",
+  "2014-07-30": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/5232/{z}/{y}/{x}",
+  "2014-07-02": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/3026/{z}/{y}/{x}",
+  "2014-06-25": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/11033/{z}/{y}/{x}",
+  "2014-06-11": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/31144/{z}/{y}/{x}",
+  "2014-05-14": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/16513/{z}/{y}/{x}",
+  "2014-04-30": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/19819/{z}/{y}/{x}",
+  "2014-03-26": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/4230/{z}/{y}/{x}",
+  "2014-02-20": "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/10/{z}/{y}/{x}"
+}

zonal_stats.csv

@@ -0,0 +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