Added multipage app

Home.py

@@ -0,0 +1,50 @@
+import streamlit as st
+import leafmap.foliumap as leafmap
+
+st.set_page_config(layout="wide")
+
+st.sidebar.title("About")
+st.sidebar.info(
+    """
+    Web App URL: <https://geospatial.streamlitapp.com>
+    GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
+    """
+)
+
+st.sidebar.title("Contact")
+st.sidebar.info(
+    """
+    Qiusheng Wu: <https://wetlands.io>
+    [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
+    """
+)
+
+st.title("Streamlit for Geospatial Applications")
+
+st.markdown(
+    """
+    This multi-page web app demonstrates various interactive web apps created using [streamlit](https://streamlit.io) and open-source mapping libraries, 
+    such as [leafmap](https://leafmap.org), [geemap](https://geemap.org), [pydeck](https://deckgl.readthedocs.io), and [kepler.gl](https://docs.kepler.gl/docs/keplergl-jupyter).
+    This is an open-source project and you are very welcome to contribute your comments, questions, resources, and apps as [issues](https://github.com/giswqs/streamlit-geospatial/issues) or 
+    [pull requests](https://github.com/giswqs/streamlit-geospatial/pulls) to the [GitHub repository](https://github.com/giswqs/streamlit-geospatial).
+
+    """
+)
+
+st.info("Click on the left sidebar menu to navigate to the different apps.")
+
+st.subheader("Timelapse of Satellite Imagery")
+st.markdown(
+    """
+    The following timelapse animations were created using the Timelapse web app. Click `Timelapse` on the left sidebar menu to create your own timelapse for any location around the globe.
+"""
+)
+
+row1_col1, row1_col2 = st.columns(2)
+with row1_col1:
+    st.image("https://github.com/giswqs/data/raw/main/timelapse/spain.gif")
+    st.image("https://github.com/giswqs/data/raw/main/timelapse/las_vegas.gif")
+
+with row1_col2:
+    st.image("https://github.com/giswqs/data/raw/main/timelapse/goes.gif")
+    st.image("https://github.com/giswqs/data/raw/main/timelapse/fire.gif")

Procfile

@@ -1 +1 @@
-web: sh setup.sh && streamlit run app.py
+web: sh setup.sh && streamlit run Home.py

apps/hurricane.py

@@ -3,8 +3,9 @@ import tropycal.tracks as tracks
 
 
 @st.cache
-def read_data(basin='north_atlantic',source='hurdat',include_btk=False):
-    return tracks.TrackDataset(basin=basin,source=source,include_btk=include_btk)
+def read_data(basin='north_atlantic', source='hurdat', include_btk=False):
+    return tracks.TrackDataset(basin=basin, source=source, include_btk=include_btk)
+
 
 def app():
 
@@ -23,14 +24,16 @@ def app():
             if st.session_state.get('hurricane') is None:
                 st.session_state['hurricane'] = read_data()
 
-            years = st.slider('Select a year', min_value=1950, max_value=2022, value=(2000, 2010))
+            years = st.slider(
+                'Select a year', min_value=1950, max_value=2022, value=(2000, 2010)
+            )
             storms = st.session_state['hurricane'].filter_storms(year_range=years)
             selected = st.selectbox('Select a storm', storms)
             storm = st.session_state['hurricane'].get_storm(selected)
             ax = storm.plot()
             fig = ax.get_figure()
-            empty.pyplot(fig) 
-        else:           
+            empty.pyplot(fig)
+        else:
 
             name = st.text_input("Or enter a storm Name", "michael")
             if name:
@@ -40,12 +43,10 @@ def app():
                 years = basin.search_name(name)
                 if len(years) > 0:
                     year = st.selectbox("Select a year", years)
-                    storm = basin.get_storm((name,year))
+                    storm = basin.get_storm((name, year))
                     ax = storm.plot()
                     fig = ax.get_figure()
                     empty.pyplot(fig)
                 else:
                     empty.text("No storms found")
                     st.write("No storms found")
-
-    

apps/timelapse.py

@@ -368,14 +368,10 @@ def app():
                 # st.info(
                 #     "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click Submit button"
                 # )
-                if (
-                    collection
-                    in [
-                        "Geostationary Operational Environmental Satellites (GOES)",
-                        "USDA National Agriculture Imagery Program (NAIP)",
-                    ]
-                    and (not keyword)
-                ):
+                if collection in [
+                    "Geostationary Operational Environmental Satellites (GOES)",
+                    "USDA National Agriculture Imagery Program (NAIP)",
+                ] and (not keyword):
                     m.set_center(-100, 40, 3)
                 # else:
                 #     m.set_center(4.20, 18.63, zoom=2)

environment-bk.yml

@@ -2,7 +2,7 @@ name: geo
 channels:
   - conda-forge
 dependencies:
-  - gdal=3.2.2
+  - gdal=3.4.3
   - pip
   - pip:
       - geopandas

environment-bk2.yml

@@ -1,20 +0,0 @@
-name: geo-env
-channels:
-    - conda-forge
-dependencies:
-    # - gdal=3.4.3
-    - cartopy
-    - pip
-    - pip:
-      - geopandas
-      - keplergl
-      - palettable
-      - localtileserver
-      - owslib
-      - plotly
-      - streamlit
-      - streamlit-folium
-      - streamlit-bokeh-events
-      - tropycal
-      - geemap
-      - leafmap

pages/1_📷_Timelapse.py

@@ -0,0 +1,1505 @@
+from sys import modules
+import ee
+import os
+import datetime
+import geopandas as gpd
+import folium
+import streamlit as st
+import geemap.colormaps as cm
+import geemap.foliumap as geemap
+from datetime import date
+from shapely.geometry import Polygon
+
+st.set_page_config(layout="wide")
+
+st.sidebar.title("About")
+st.sidebar.info(
+    """
+    Web App URL: <https://geospatial.streamlitapp.com>
+    GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
+    """
+)
+
+st.sidebar.title("Contact")
+st.sidebar.info(
+    """
+    Qiusheng Wu: <https://wetlands.io>
+    [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
+    """
+)
+
+goes_rois = {
+    "Creek Fire, CA (2020-09-05)": {
+        "region": Polygon(
+            [
+                [-121.003418, 36.848857],
+                [-121.003418, 39.049052],
+                [-117.905273, 39.049052],
+                [-117.905273, 36.848857],
+                [-121.003418, 36.848857],
+            ]
+        ),
+        "start_time": "2020-09-05T15:00:00",
+        "end_time": "2020-09-06T02:00:00",
+    },
+    "Bomb Cyclone (2021-10-24)": {
+        "region": Polygon(
+            [
+                [-159.5954, 60.4088],
+                [-159.5954, 24.5178],
+                [-114.2438, 24.5178],
+                [-114.2438, 60.4088],
+            ]
+        ),
+        "start_time": "2021-10-24T14:00:00",
+        "end_time": "2021-10-25T01:00:00",
+    },
+    "Hunga Tonga Volcanic Eruption (2022-01-15)": {
+        "region": Polygon(
+            [
+                [-192.480469, -32.546813],
+                [-192.480469, -8.754795],
+                [-157.587891, -8.754795],
+                [-157.587891, -32.546813],
+                [-192.480469, -32.546813],
+            ]
+        ),
+        "start_time": "2022-01-15T03:00:00",
+        "end_time": "2022-01-15T07:00:00",
+    },
+    "Hunga Tonga Volcanic Eruption Closer Look (2022-01-15)": {
+        "region": Polygon(
+            [
+                [-178.901367, -22.958393],
+                [-178.901367, -17.85329],
+                [-171.452637, -17.85329],
+                [-171.452637, -22.958393],
+                [-178.901367, -22.958393],
+            ]
+        ),
+        "start_time": "2022-01-15T03:00:00",
+        "end_time": "2022-01-15T07:00:00",
+    },
+}
+
+
+landsat_rois = {
+    "Aral Sea": Polygon(
+        [
+            [57.667236, 43.834527],
+            [57.667236, 45.996962],
+            [61.12793, 45.996962],
+            [61.12793, 43.834527],
+            [57.667236, 43.834527],
+        ]
+    ),
+    "Dubai": Polygon(
+        [
+            [54.541626, 24.763044],
+            [54.541626, 25.427152],
+            [55.632019, 25.427152],
+            [55.632019, 24.763044],
+            [54.541626, 24.763044],
+        ]
+    ),
+    "Hong Kong International Airport": Polygon(
+        [
+            [113.825226, 22.198849],
+            [113.825226, 22.349758],
+            [114.085121, 22.349758],
+            [114.085121, 22.198849],
+            [113.825226, 22.198849],
+        ]
+    ),
+    "Las Vegas, NV": Polygon(
+        [
+            [-115.554199, 35.804449],
+            [-115.554199, 36.558188],
+            [-113.903503, 36.558188],
+            [-113.903503, 35.804449],
+            [-115.554199, 35.804449],
+        ]
+    ),
+    "Pucallpa, Peru": Polygon(
+        [
+            [-74.672699, -8.600032],
+            [-74.672699, -8.254983],
+            [-74.279938, -8.254983],
+            [-74.279938, -8.600032],
+        ]
+    ),
+    "Sierra Gorda, Chile": Polygon(
+        [
+            [-69.315491, -22.837104],
+            [-69.315491, -22.751488],
+            [-69.190006, -22.751488],
+            [-69.190006, -22.837104],
+            [-69.315491, -22.837104],
+        ]
+    ),
+}
+
+modis_rois = {
+    "World": Polygon(
+        [
+            [-171.210938, -57.136239],
+            [-171.210938, 79.997168],
+            [177.539063, 79.997168],
+            [177.539063, -57.136239],
+            [-171.210938, -57.136239],
+        ]
+    ),
+    "Africa": Polygon(
+        [
+            [-18.6983, 38.1446],
+            [-18.6983, -36.1630],
+            [52.2293, -36.1630],
+            [52.2293, 38.1446],
+        ]
+    ),
+    "USA": Polygon(
+        [
+            [-127.177734, 23.725012],
+            [-127.177734, 50.792047],
+            [-66.269531, 50.792047],
+            [-66.269531, 23.725012],
+            [-127.177734, 23.725012],
+        ]
+    ),
+}
+
+ocean_rois = {
+    "Gulf of Mexico": Polygon(
+        [
+            [-101.206055, 15.496032],
+            [-101.206055, 32.361403],
+            [-75.673828, 32.361403],
+            [-75.673828, 15.496032],
+            [-101.206055, 15.496032],
+        ]
+    ),
+    "North Atlantic Ocean": Polygon(
+        [
+            [-85.341797, 24.046464],
+            [-85.341797, 45.02695],
+            [-55.810547, 45.02695],
+            [-55.810547, 24.046464],
+            [-85.341797, 24.046464],
+        ]
+    ),
+    "World": Polygon(
+        [
+            [-171.210938, -57.136239],
+            [-171.210938, 79.997168],
+            [177.539063, 79.997168],
+            [177.539063, -57.136239],
+            [-171.210938, -57.136239],
+        ]
+    ),
+}
+
+
+@st.cache
+def uploaded_file_to_gdf(data):
+    import tempfile
+    import os
+    import uuid
+
+    _, file_extension = os.path.splitext(data.name)
+    file_id = str(uuid.uuid4())
+    file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{file_extension}")
+
+    with open(file_path, "wb") as file:
+        file.write(data.getbuffer())
+
+    if file_path.lower().endswith(".kml"):
+        gpd.io.file.fiona.drvsupport.supported_drivers["KML"] = "rw"
+        gdf = gpd.read_file(file_path, driver="KML")
+    else:
+        gdf = gpd.read_file(file_path)
+
+    return gdf
+
+
+def app():
+
+    today = date.today()
+
+    st.title("Create Satellite Timelapse")
+
+    st.markdown(
+        """
+        An interactive web app for creating [Landsat](https://developers.google.com/earth-engine/datasets/catalog/landsat)/[GOES](https://jstnbraaten.medium.com/goes-in-earth-engine-53fbc8783c16) timelapse for any location around the globe. 
+        The app was built using [streamlit](https://streamlit.io), [geemap](https://geemap.org), and [Google Earth Engine](https://earthengine.google.com). For more info, check out my streamlit [blog post](https://blog.streamlit.io/creating-satellite-timelapse-with-streamlit-and-earth-engine). 
+    """
+    )
+
+    row1_col1, row1_col2 = st.columns([2, 1])
+
+    if st.session_state.get("zoom_level") is None:
+        st.session_state["zoom_level"] = 4
+
+    st.session_state["ee_asset_id"] = None
+    st.session_state["bands"] = None
+    st.session_state["palette"] = None
+    st.session_state["vis_params"] = None
+
+    with row1_col1:
+        m = geemap.Map(
+            basemap="HYBRID",
+            plugin_Draw=True,
+            Draw_export=True,
+            locate_control=True,
+            plugin_LatLngPopup=False,
+        )
+        m.add_basemap("ROADMAP")
+
+    with row1_col2:
+
+        keyword = st.text_input("Search for a location:", "")
+        if keyword:
+            locations = geemap.geocode(keyword)
+            if locations is not None and len(locations) > 0:
+                str_locations = [str(g)[1:-1] for g in locations]
+                location = st.selectbox("Select a location:", str_locations)
+                loc_index = str_locations.index(location)
+                selected_loc = locations[loc_index]
+                lat, lng = selected_loc.lat, selected_loc.lng
+                folium.Marker(location=[lat, lng], popup=location).add_to(m)
+                m.set_center(lng, lat, 12)
+                st.session_state["zoom_level"] = 12
+
+        collection = st.selectbox(
+            "Select a satellite image collection: ",
+            [
+                "Any Earth Engine ImageCollection",
+                "Landsat TM-ETM-OLI Surface Reflectance",
+                "Sentinel-2 MSI Surface Reflectance",
+                "Geostationary Operational Environmental Satellites (GOES)",
+                "MODIS Vegetation Indices (NDVI/EVI) 16-Day Global 1km",
+                "MODIS Gap filled Land Surface Temperature Daily",
+                "MODIS Ocean Color SMI",
+                "USDA National Agriculture Imagery Program (NAIP)",
+            ],
+            index=1,
+        )
+
+        if collection in [
+            "Landsat TM-ETM-OLI Surface Reflectance",
+            "Sentinel-2 MSI Surface Reflectance",
+        ]:
+            roi_options = ["Uploaded GeoJSON"] + list(landsat_rois.keys())
+
+        elif collection == "Geostationary Operational Environmental Satellites (GOES)":
+            roi_options = ["Uploaded GeoJSON"] + list(goes_rois.keys())
+
+        elif collection in [
+            "MODIS Vegetation Indices (NDVI/EVI) 16-Day Global 1km",
+            "MODIS Gap filled Land Surface Temperature Daily",
+        ]:
+            roi_options = ["Uploaded GeoJSON"] + list(modis_rois.keys())
+        elif collection == "MODIS Ocean Color SMI":
+            roi_options = ["Uploaded GeoJSON"] + list(ocean_rois.keys())
+        else:
+            roi_options = ["Uploaded GeoJSON"]
+
+        if collection == "Any Earth Engine ImageCollection":
+            keyword = st.text_input("Enter a keyword to search (e.g., MODIS):", "")
+            if keyword:
+
+                assets = geemap.search_ee_data(keyword)
+                ee_assets = []
+                for asset in assets:
+                    if asset["ee_id_snippet"].startswith("ee.ImageCollection"):
+                        ee_assets.append(asset)
+
+                asset_titles = [x["title"] for x in ee_assets]
+                dataset = st.selectbox("Select a dataset:", asset_titles)
+                if len(ee_assets) > 0:
+                    st.session_state["ee_assets"] = ee_assets
+                    st.session_state["asset_titles"] = asset_titles
+                    index = asset_titles.index(dataset)
+                    ee_id = ee_assets[index]["id"]
+                else:
+                    ee_id = ""
+
+                if dataset is not None:
+                    with st.expander("Show dataset details", False):
+                        index = asset_titles.index(dataset)
+                        html = geemap.ee_data_html(st.session_state["ee_assets"][index])
+                        st.markdown(html, True)
+            # elif collection == "MODIS Gap filled Land Surface Temperature Daily":
+            #     ee_id = ""
+            else:
+                ee_id = ""
+
+            asset_id = st.text_input("Enter an ee.ImageCollection asset ID:", ee_id)
+
+            if asset_id:
+                with st.expander("Customize band combination and color palette", True):
+                    try:
+                        col = ee.ImageCollection.load(asset_id)
+                        st.session_state["ee_asset_id"] = asset_id
+                    except:
+                        st.error("Invalid Earth Engine asset ID.")
+                        st.session_state["ee_asset_id"] = None
+                        return
+
+                    img_bands = col.first().bandNames().getInfo()
+                    if len(img_bands) >= 3:
+                        default_bands = img_bands[:3][::-1]
+                    else:
+                        default_bands = img_bands[:]
+                    bands = st.multiselect(
+                        "Select one or three bands (RGB):", img_bands, default_bands
+                    )
+                    st.session_state["bands"] = bands
+
+                    if len(bands) == 1:
+                        palette_options = st.selectbox(
+                            "Color palette",
+                            cm.list_colormaps(),
+                            index=2,
+                        )
+                        palette_values = cm.get_palette(palette_options, 15)
+                        palette = st.text_area(
+                            "Enter a custom palette:",
+                            palette_values,
+                        )
+                        st.write(
+                            cm.plot_colormap(cmap=palette_options, return_fig=True)
+                        )
+                        st.session_state["palette"] = eval(palette)
+
+                    if bands:
+                        vis_params = st.text_area(
+                            "Enter visualization parameters",
+                            "{'bands': ["
+                            + ", ".join([f"'{band}'" for band in bands])
+                            + "]}",
+                        )
+                    else:
+                        vis_params = st.text_area(
+                            "Enter visualization parameters",
+                            "{}",
+                        )
+                    try:
+                        st.session_state["vis_params"] = eval(vis_params)
+                        st.session_state["vis_params"]["palette"] = st.session_state[
+                            "palette"
+                        ]
+                    except Exception as e:
+                        st.session_state["vis_params"] = None
+                        st.error(
+                            f"Invalid visualization parameters. It must be a dictionary."
+                        )
+
+        elif collection == "MODIS Gap filled Land Surface Temperature Daily":
+            with st.expander("Show dataset details", False):
+                st.markdown(
+                    """
+                See the [Awesome GEE Community Datasets](https://samapriya.github.io/awesome-gee-community-datasets/projects/daily_lst/).
+                """
+                )
+
+            MODIS_options = ["Daytime (1:30 pm)", "Nighttime (1:30 am)"]
+            MODIS_option = st.selectbox("Select a MODIS dataset:", MODIS_options)
+            if MODIS_option == "Daytime (1:30 pm)":
+                st.session_state[
+                    "ee_asset_id"
+                ] = "projects/sat-io/open-datasets/gap-filled-lst/gf_day_1km"
+            else:
+                st.session_state[
+                    "ee_asset_id"
+                ] = "projects/sat-io/open-datasets/gap-filled-lst/gf_night_1km"
+
+            palette_options = st.selectbox(
+                "Color palette",
+                cm.list_colormaps(),
+                index=90,
+            )
+            palette_values = cm.get_palette(palette_options, 15)
+            palette = st.text_area(
+                "Enter a custom palette:",
+                palette_values,
+            )
+            st.write(cm.plot_colormap(cmap=palette_options, return_fig=True))
+            st.session_state["palette"] = eval(palette)
+        elif collection == "MODIS Ocean Color SMI":
+            with st.expander("Show dataset details", False):
+                st.markdown(
+                    """
+                See the [Earth Engine Data Catalog](https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI).
+                """
+                )
+
+            MODIS_options = ["Aqua", "Terra"]
+            MODIS_option = st.selectbox("Select a satellite:", MODIS_options)
+            st.session_state["ee_asset_id"] = MODIS_option
+            # if MODIS_option == "Daytime (1:30 pm)":
+            #     st.session_state[
+            #         "ee_asset_id"
+            #     ] = "projects/sat-io/open-datasets/gap-filled-lst/gf_day_1km"
+            # else:
+            #     st.session_state[
+            #         "ee_asset_id"
+            #     ] = "projects/sat-io/open-datasets/gap-filled-lst/gf_night_1km"
+
+            band_dict = {
+                "Chlorophyll a concentration": "chlor_a",
+                "Normalized fluorescence line height": "nflh",
+                "Particulate organic carbon": "poc",
+                "Sea surface temperature": "sst",
+                "Remote sensing reflectance at band 412nm": "Rrs_412",
+                "Remote sensing reflectance at band 443nm": "Rrs_443",
+                "Remote sensing reflectance at band 469nm": "Rrs_469",
+                "Remote sensing reflectance at band 488nm": "Rrs_488",
+                "Remote sensing reflectance at band 531nm": "Rrs_531",
+                "Remote sensing reflectance at band 547nm": "Rrs_547",
+                "Remote sensing reflectance at band 555nm": "Rrs_555",
+                "Remote sensing reflectance at band 645nm": "Rrs_645",
+                "Remote sensing reflectance at band 667nm": "Rrs_667",
+                "Remote sensing reflectance at band 678nm": "Rrs_678",
+            }
+
+            band_options = list(band_dict.keys())
+            band = st.selectbox(
+                "Select a band",
+                band_options,
+                band_options.index("Sea surface temperature"),
+            )
+            st.session_state["band"] = band_dict[band]
+
+            colors = cm.list_colormaps()
+            palette_options = st.selectbox(
+                "Color palette",
+                colors,
+                index=colors.index("coolwarm"),
+            )
+            palette_values = cm.get_palette(palette_options, 15)
+            palette = st.text_area(
+                "Enter a custom palette:",
+                palette_values,
+            )
+            st.write(cm.plot_colormap(cmap=palette_options, return_fig=True))
+            st.session_state["palette"] = eval(palette)
+
+        sample_roi = st.selectbox(
+            "Select a sample ROI or upload a GeoJSON file:",
+            roi_options,
+            index=0,
+        )
+
+        add_outline = st.checkbox(
+            "Overlay an administrative boundary on timelapse", False
+        )
+
+        if add_outline:
+
+            with st.expander("Customize administrative boundary", True):
+
+                overlay_options = {
+                    "User-defined": None,
+                    "Continents": "continents",
+                    "Countries": "countries",
+                    "US States": "us_states",
+                    "China": "china",
+                }
+
+                overlay = st.selectbox(
+                    "Select an administrative boundary:",
+                    list(overlay_options.keys()),
+                    index=2,
+                )
+
+                overlay_data = overlay_options[overlay]
+
+                if overlay_data is None:
+                    overlay_data = st.text_input(
+                        "Enter an HTTP URL to a GeoJSON file or an ee.FeatureCollection asset id:",
+                        "https://raw.githubusercontent.com/giswqs/geemap/master/examples/data/countries.geojson",
+                    )
+
+                overlay_color = st.color_picker(
+                    "Select a color for the administrative boundary:", "#000000"
+                )
+                overlay_width = st.slider(
+                    "Select a line width for the administrative boundary:", 1, 20, 1
+                )
+                overlay_opacity = st.slider(
+                    "Select an opacity for the administrative boundary:",
+                    0.0,
+                    1.0,
+                    1.0,
+                    0.05,
+                )
+        else:
+            overlay_data = None
+            overlay_color = "black"
+            overlay_width = 1
+            overlay_opacity = 1
+
+    with row1_col1:
+
+        with st.expander(
+            "Steps: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Expand this tab to see a demo 👉"
+        ):
+            video_empty = st.empty()
+
+        data = st.file_uploader(
+            "Upload a GeoJSON file to use as an ROI. Customize timelapse parameters and then click the Submit button 😇👇",
+            type=["geojson", "kml", "zip"],
+        )
+
+        crs = "epsg:4326"
+        if sample_roi == "Uploaded GeoJSON":
+            if data is None:
+                # st.info(
+                #     "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click Submit button"
+                # )
+                if collection in [
+                    "Geostationary Operational Environmental Satellites (GOES)",
+                    "USDA National Agriculture Imagery Program (NAIP)",
+                ] and (not keyword):
+                    m.set_center(-100, 40, 3)
+                # else:
+                #     m.set_center(4.20, 18.63, zoom=2)
+        else:
+            if collection in [
+                "Landsat TM-ETM-OLI Surface Reflectance",
+                "Sentinel-2 MSI Surface Reflectance",
+            ]:
+                gdf = gpd.GeoDataFrame(
+                    index=[0], crs=crs, geometry=[landsat_rois[sample_roi]]
+                )
+            elif (
+                collection
+                == "Geostationary Operational Environmental Satellites (GOES)"
+            ):
+                gdf = gpd.GeoDataFrame(
+                    index=[0], crs=crs, geometry=[goes_rois[sample_roi]["region"]]
+                )
+            elif collection == "MODIS Vegetation Indices (NDVI/EVI) 16-Day Global 1km":
+                gdf = gpd.GeoDataFrame(
+                    index=[0], crs=crs, geometry=[modis_rois[sample_roi]]
+                )
+
+        if sample_roi != "Uploaded GeoJSON":
+
+            if collection in [
+                "Landsat TM-ETM-OLI Surface Reflectance",
+                "Sentinel-2 MSI Surface Reflectance",
+            ]:
+                gdf = gpd.GeoDataFrame(
+                    index=[0], crs=crs, geometry=[landsat_rois[sample_roi]]
+                )
+            elif (
+                collection
+                == "Geostationary Operational Environmental Satellites (GOES)"
+            ):
+                gdf = gpd.GeoDataFrame(
+                    index=[0], crs=crs, geometry=[goes_rois[sample_roi]["region"]]
+                )
+            elif collection in [
+                "MODIS Vegetation Indices (NDVI/EVI) 16-Day Global 1km",
+                "MODIS Gap filled Land Surface Temperature Daily",
+            ]:
+                gdf = gpd.GeoDataFrame(
+                    index=[0], crs=crs, geometry=[modis_rois[sample_roi]]
+                )
+            elif collection == "MODIS Ocean Color SMI":
+                gdf = gpd.GeoDataFrame(
+                    index=[0], crs=crs, geometry=[ocean_rois[sample_roi]]
+                )
+            st.session_state["roi"] = geemap.geopandas_to_ee(gdf, geodesic=False)
+            m.add_gdf(gdf, "ROI")
+
+        elif data:
+            gdf = uploaded_file_to_gdf(data)
+            st.session_state["roi"] = geemap.geopandas_to_ee(gdf, geodesic=False)
+            m.add_gdf(gdf, "ROI")
+
+        m.to_streamlit(height=600)
+
+    with row1_col2:
+
+        if collection in [
+            "Landsat TM-ETM-OLI Surface Reflectance",
+            "Sentinel-2 MSI Surface Reflectance",
+        ]:
+
+            if collection == "Landsat TM-ETM-OLI Surface Reflectance":
+                sensor_start_year = 1984
+                timelapse_title = "Landsat Timelapse"
+                timelapse_speed = 5
+            elif collection == "Sentinel-2 MSI Surface Reflectance":
+                sensor_start_year = 2015
+                timelapse_title = "Sentinel-2 Timelapse"
+                timelapse_speed = 5
+            video_empty.video("https://youtu.be/VVRK_-dEjR4")
+
+            with st.form("submit_landsat_form"):
+
+                roi = None
+                if st.session_state.get("roi") is not None:
+                    roi = st.session_state.get("roi")
+                out_gif = geemap.temp_file_path(".gif")
+
+                title = st.text_input(
+                    "Enter a title to show on the timelapse: ", timelapse_title
+                )
+                RGB = st.selectbox(
+                    "Select an RGB band combination:",
+                    [
+                        "Red/Green/Blue",
+                        "NIR/Red/Green",
+                        "SWIR2/SWIR1/NIR",
+                        "NIR/SWIR1/Red",
+                        "SWIR2/NIR/Red",
+                        "SWIR2/SWIR1/Red",
+                        "SWIR1/NIR/Blue",
+                        "NIR/SWIR1/Blue",
+                        "SWIR2/NIR/Green",
+                        "SWIR1/NIR/Red",
+                        "SWIR2/NIR/SWIR1",
+                        "SWIR1/NIR/SWIR2",
+                    ],
+                    index=9,
+                )
+
+                frequency = st.selectbox(
+                    "Select a temporal frequency:",
+                    ["year", "quarter", "month"],
+                    index=0,
+                )
+
+                with st.expander("Customize timelapse"):
+
+                    speed = st.slider("Frames per second:", 1, 30, timelapse_speed)
+                    dimensions = st.slider(
+                        "Maximum dimensions (Width*Height) in pixels", 768, 2000, 768
+                    )
+                    progress_bar_color = st.color_picker(
+                        "Progress bar color:", "#0000ff"
+                    )
+                    years = st.slider(
+                        "Start and end year:",
+                        sensor_start_year,
+                        today.year,
+                        (sensor_start_year, today.year),
+                    )
+                    months = st.slider("Start and end month:", 1, 12, (1, 12))
+                    font_size = st.slider("Font size:", 10, 50, 30)
+                    font_color = st.color_picker("Font color:", "#ffffff")
+                    apply_fmask = st.checkbox(
+                        "Apply fmask (remove clouds, shadows, snow)", True
+                    )
+                    font_type = st.selectbox(
+                        "Select the font type for the title:",
+                        ["arial.ttf", "alibaba.otf"],
+                        index=0,
+                    )
+                    fading = st.slider(
+                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
+                    )
+                    mp4 = st.checkbox("Save timelapse as MP4", True)
+
+                empty_text = st.empty()
+                empty_image = st.empty()
+                empty_fire_image = st.empty()
+                empty_video = st.container()
+                submitted = st.form_submit_button("Submit")
+                if submitted:
+
+                    if sample_roi == "Uploaded GeoJSON" and data is None:
+                        empty_text.warning(
+                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
+                        )
+                    else:
+
+                        empty_text.text("Computing... Please wait...")
+
+                        start_year = years[0]
+                        end_year = years[1]
+                        start_date = str(months[0]).zfill(2) + "-01"
+                        end_date = str(months[1]).zfill(2) + "-30"
+                        bands = RGB.split("/")
+
+                        try:
+                            if collection == "Landsat TM-ETM-OLI Surface Reflectance":
+                                out_gif = geemap.landsat_timelapse(
+                                    roi=roi,
+                                    out_gif=out_gif,
+                                    start_year=start_year,
+                                    end_year=end_year,
+                                    start_date=start_date,
+                                    end_date=end_date,
+                                    bands=bands,
+                                    apply_fmask=apply_fmask,
+                                    frames_per_second=speed,
+                                    dimensions=dimensions,
+                                    overlay_data=overlay_data,
+                                    overlay_color=overlay_color,
+                                    overlay_width=overlay_width,
+                                    overlay_opacity=overlay_opacity,
+                                    frequency=frequency,
+                                    date_format=None,
+                                    title=title,
+                                    title_xy=("2%", "90%"),
+                                    add_text=True,
+                                    text_xy=("2%", "2%"),
+                                    text_sequence=None,
+                                    font_type=font_type,
+                                    font_size=font_size,
+                                    font_color=font_color,
+                                    add_progress_bar=True,
+                                    progress_bar_color=progress_bar_color,
+                                    progress_bar_height=5,
+                                    loop=0,
+                                    mp4=mp4,
+                                    fading=fading,
+                                )
+                            elif collection == "Sentinel-2 MSI Surface Reflectance":
+                                out_gif = geemap.sentinel2_timelapse(
+                                    roi=roi,
+                                    out_gif=out_gif,
+                                    start_year=start_year,
+                                    end_year=end_year,
+                                    start_date=start_date,
+                                    end_date=end_date,
+                                    bands=bands,
+                                    apply_fmask=apply_fmask,
+                                    frames_per_second=speed,
+                                    dimensions=dimensions,
+                                    overlay_data=overlay_data,
+                                    overlay_color=overlay_color,
+                                    overlay_width=overlay_width,
+                                    overlay_opacity=overlay_opacity,
+                                    frequency=frequency,
+                                    date_format=None,
+                                    title=title,
+                                    title_xy=("2%", "90%"),
+                                    add_text=True,
+                                    text_xy=("2%", "2%"),
+                                    text_sequence=None,
+                                    font_type=font_type,
+                                    font_size=font_size,
+                                    font_color=font_color,
+                                    add_progress_bar=True,
+                                    progress_bar_color=progress_bar_color,
+                                    progress_bar_height=5,
+                                    loop=0,
+                                    mp4=mp4,
+                                    fading=fading,
+                                )
+                        except:
+                            empty_text.error(
+                                "An error occurred while computing the timelapse. Your probably requested too much data. Try reducing the ROI or timespan."
+                            )
+                            st.stop()
+
+                        if out_gif is not None and os.path.exists(out_gif):
+
+                            empty_text.text(
+                                "Right click the GIF to save it to your computer👇"
+                            )
+                            empty_image.image(out_gif)
+
+                            out_mp4 = out_gif.replace(".gif", ".mp4")
+                            if mp4 and os.path.exists(out_mp4):
+                                with empty_video:
+                                    st.text(
+                                        "Right click the MP4 to save it to your computer👇"
+                                    )
+                                    st.video(out_gif.replace(".gif", ".mp4"))
+
+                        else:
+                            empty_text.error(
+                                "Something went wrong. You probably requested too much data. Try reducing the ROI or timespan."
+                            )
+
+        elif collection == "Geostationary Operational Environmental Satellites (GOES)":
+
+            video_empty.video("https://youtu.be/16fA2QORG4A")
+
+            with st.form("submit_goes_form"):
+
+                roi = None
+                if st.session_state.get("roi") is not None:
+                    roi = st.session_state.get("roi")
+                out_gif = geemap.temp_file_path(".gif")
+
+                satellite = st.selectbox("Select a satellite:", ["GOES-17", "GOES-16"])
+                earliest_date = datetime.date(2017, 7, 10)
+                latest_date = datetime.date.today()
+
+                if sample_roi == "Uploaded GeoJSON":
+                    roi_start_date = today - datetime.timedelta(days=2)
+                    roi_end_date = today - datetime.timedelta(days=1)
+                    roi_start_time = datetime.time(14, 00)
+                    roi_end_time = datetime.time(1, 00)
+                else:
+                    roi_start = goes_rois[sample_roi]["start_time"]
+                    roi_end = goes_rois[sample_roi]["end_time"]
+                    roi_start_date = datetime.datetime.strptime(
+                        roi_start[:10], "%Y-%m-%d"
+                    )
+                    roi_end_date = datetime.datetime.strptime(roi_end[:10], "%Y-%m-%d")
+                    roi_start_time = datetime.time(
+                        int(roi_start[11:13]), int(roi_start[14:16])
+                    )
+                    roi_end_time = datetime.time(
+                        int(roi_end[11:13]), int(roi_end[14:16])
+                    )
+
+                start_date = st.date_input("Select the start date:", roi_start_date)
+                end_date = st.date_input("Select the end date:", roi_end_date)
+
+                with st.expander("Customize timelapse"):
+
+                    add_fire = st.checkbox("Add Fire/Hotspot Characterization", False)
+
+                    scan_type = st.selectbox(
+                        "Select a scan type:", ["Full Disk", "CONUS", "Mesoscale"]
+                    )
+
+                    start_time = st.time_input(
+                        "Select the start time of the start date:", roi_start_time
+                    )
+
+                    end_time = st.time_input(
+                        "Select the end time of the end date:", roi_end_time
+                    )
+
+                    start = (
+                        start_date.strftime("%Y-%m-%d")
+                        + "T"
+                        + start_time.strftime("%H:%M:%S")
+                    )
+                    end = (
+                        end_date.strftime("%Y-%m-%d")
+                        + "T"
+                        + end_time.strftime("%H:%M:%S")
+                    )
+
+                    speed = st.slider("Frames per second:", 1, 30, 5)
+                    add_progress_bar = st.checkbox("Add a progress bar", True)
+                    progress_bar_color = st.color_picker(
+                        "Progress bar color:", "#0000ff"
+                    )
+                    font_size = st.slider("Font size:", 10, 50, 20)
+                    font_color = st.color_picker("Font color:", "#ffffff")
+                    fading = st.slider(
+                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
+                    )
+                    mp4 = st.checkbox("Save timelapse as MP4", True)
+
+                empty_text = st.empty()
+                empty_image = st.empty()
+                empty_video = st.container()
+                empty_fire_text = st.empty()
+                empty_fire_image = st.empty()
+
+                submitted = st.form_submit_button("Submit")
+                if submitted:
+                    if sample_roi == "Uploaded GeoJSON" and data is None:
+                        empty_text.warning(
+                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
+                        )
+                    else:
+                        empty_text.text("Computing... Please wait...")
+
+                        geemap.goes_timelapse(
+                            out_gif,
+                            start_date=start,
+                            end_date=end,
+                            data=satellite,
+                            scan=scan_type.replace(" ", "_").lower(),
+                            region=roi,
+                            dimensions=768,
+                            framesPerSecond=speed,
+                            date_format="YYYY-MM-dd HH:mm",
+                            xy=("3%", "3%"),
+                            text_sequence=None,
+                            font_type="arial.ttf",
+                            font_size=font_size,
+                            font_color=font_color,
+                            add_progress_bar=add_progress_bar,
+                            progress_bar_color=progress_bar_color,
+                            progress_bar_height=5,
+                            loop=0,
+                            overlay_data=overlay_data,
+                            overlay_color=overlay_color,
+                            overlay_width=overlay_width,
+                            overlay_opacity=overlay_opacity,
+                            mp4=mp4,
+                            fading=fading,
+                        )
+
+                        if out_gif is not None and os.path.exists(out_gif):
+                            empty_text.text(
+                                "Right click the GIF to save it to your computer👇"
+                            )
+                            empty_image.image(out_gif)
+
+                            out_mp4 = out_gif.replace(".gif", ".mp4")
+                            if mp4 and os.path.exists(out_mp4):
+                                with empty_video:
+                                    st.text(
+                                        "Right click the MP4 to save it to your computer👇"
+                                    )
+                                    st.video(out_gif.replace(".gif", ".mp4"))
+
+                            if add_fire:
+                                out_fire_gif = geemap.temp_file_path(".gif")
+                                empty_fire_text.text(
+                                    "Delineating Fire Hotspot... Please wait..."
+                                )
+                                geemap.goes_fire_timelapse(
+                                    out_fire_gif,
+                                    start_date=start,
+                                    end_date=end,
+                                    data=satellite,
+                                    scan=scan_type.replace(" ", "_").lower(),
+                                    region=roi,
+                                    dimensions=768,
+                                    framesPerSecond=speed,
+                                    date_format="YYYY-MM-dd HH:mm",
+                                    xy=("3%", "3%"),
+                                    text_sequence=None,
+                                    font_type="arial.ttf",
+                                    font_size=font_size,
+                                    font_color=font_color,
+                                    add_progress_bar=add_progress_bar,
+                                    progress_bar_color=progress_bar_color,
+                                    progress_bar_height=5,
+                                    loop=0,
+                                )
+                                if os.path.exists(out_fire_gif):
+                                    empty_fire_image.image(out_fire_gif)
+                        else:
+                            empty_text.text(
+                                "Something went wrong, either the ROI is too big or there are no data available for the specified date range. Please try a smaller ROI or different date range."
+                            )
+
+        elif collection == "MODIS Vegetation Indices (NDVI/EVI) 16-Day Global 1km":
+
+            video_empty.video("https://youtu.be/16fA2QORG4A")
+
+            satellite = st.selectbox("Select a satellite:", ["Terra", "Aqua"])
+            band = st.selectbox("Select a band:", ["NDVI", "EVI"])
+
+            with st.form("submit_modis_form"):
+
+                roi = None
+                if st.session_state.get("roi") is not None:
+                    roi = st.session_state.get("roi")
+                out_gif = geemap.temp_file_path(".gif")
+
+                with st.expander("Customize timelapse"):
+
+                    start = st.date_input(
+                        "Select a start date:", datetime.date(2000, 2, 8)
+                    )
+                    end = st.date_input("Select an end date:", datetime.date.today())
+
+                    start_date = start.strftime("%Y-%m-%d")
+                    end_date = end.strftime("%Y-%m-%d")
+
+                    speed = st.slider("Frames per second:", 1, 30, 5)
+                    add_progress_bar = st.checkbox("Add a progress bar", True)
+                    progress_bar_color = st.color_picker(
+                        "Progress bar color:", "#0000ff"
+                    )
+                    font_size = st.slider("Font size:", 10, 50, 20)
+                    font_color = st.color_picker("Font color:", "#ffffff")
+
+                    font_type = st.selectbox(
+                        "Select the font type for the title:",
+                        ["arial.ttf", "alibaba.otf"],
+                        index=0,
+                    )
+                    fading = st.slider(
+                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
+                    )
+                    mp4 = st.checkbox("Save timelapse as MP4", True)
+
+                empty_text = st.empty()
+                empty_image = st.empty()
+                empty_video = st.container()
+
+                submitted = st.form_submit_button("Submit")
+                if submitted:
+                    if sample_roi == "Uploaded GeoJSON" and data is None:
+                        empty_text.warning(
+                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
+                        )
+                    else:
+
+                        empty_text.text("Computing... Please wait...")
+
+                        geemap.modis_ndvi_timelapse(
+                            out_gif,
+                            satellite,
+                            band,
+                            start_date,
+                            end_date,
+                            roi,
+                            768,
+                            speed,
+                            overlay_data=overlay_data,
+                            overlay_color=overlay_color,
+                            overlay_width=overlay_width,
+                            overlay_opacity=overlay_opacity,
+                            mp4=mp4,
+                            fading=fading,
+                        )
+
+                        geemap.reduce_gif_size(out_gif)
+
+                        empty_text.text(
+                            "Right click the GIF to save it to your computer👇"
+                        )
+                        empty_image.image(out_gif)
+
+                        out_mp4 = out_gif.replace(".gif", ".mp4")
+                        if mp4 and os.path.exists(out_mp4):
+                            with empty_video:
+                                st.text(
+                                    "Right click the MP4 to save it to your computer👇"
+                                )
+                                st.video(out_gif.replace(".gif", ".mp4"))
+
+        elif collection == "Any Earth Engine ImageCollection":
+
+            with st.form("submit_ts_form"):
+                with st.expander("Customize timelapse"):
+
+                    title = st.text_input(
+                        "Enter a title to show on the timelapse: ", "Timelapse"
+                    )
+                    start_date = st.date_input(
+                        "Select the start date:", datetime.date(2020, 1, 1)
+                    )
+                    end_date = st.date_input(
+                        "Select the end date:", datetime.date.today()
+                    )
+                    frequency = st.selectbox(
+                        "Select a temporal frequency:",
+                        ["year", "quarter", "month", "day", "hour", "minute", "second"],
+                        index=0,
+                    )
+                    reducer = st.selectbox(
+                        "Select a reducer for aggregating data:",
+                        ["median", "mean", "min", "max", "sum", "variance", "stdDev"],
+                        index=0,
+                    )
+                    data_format = st.selectbox(
+                        "Select a date format to show on the timelapse:",
+                        [
+                            "YYYY-MM-dd",
+                            "YYYY",
+                            "YYMM-MM",
+                            "YYYY-MM-dd HH:mm",
+                            "YYYY-MM-dd HH:mm:ss",
+                            "HH:mm",
+                            "HH:mm:ss",
+                            "w",
+                            "M",
+                            "d",
+                            "D",
+                        ],
+                        index=0,
+                    )
+
+                    speed = st.slider("Frames per second:", 1, 30, 5)
+                    add_progress_bar = st.checkbox("Add a progress bar", True)
+                    progress_bar_color = st.color_picker(
+                        "Progress bar color:", "#0000ff"
+                    )
+                    font_size = st.slider("Font size:", 10, 50, 30)
+                    font_color = st.color_picker("Font color:", "#ffffff")
+                    font_type = st.selectbox(
+                        "Select the font type for the title:",
+                        ["arial.ttf", "alibaba.otf"],
+                        index=0,
+                    )
+                    fading = st.slider(
+                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
+                    )
+                    mp4 = st.checkbox("Save timelapse as MP4", True)
+
+                empty_text = st.empty()
+                empty_image = st.empty()
+                empty_video = st.container()
+                empty_fire_image = st.empty()
+
+                roi = None
+                if st.session_state.get("roi") is not None:
+                    roi = st.session_state.get("roi")
+                out_gif = geemap.temp_file_path(".gif")
+
+                submitted = st.form_submit_button("Submit")
+                if submitted:
+
+                    if sample_roi == "Uploaded GeoJSON" and data is None:
+                        empty_text.warning(
+                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
+                        )
+                    else:
+
+                        empty_text.text("Computing... Please wait...")
+                        try:
+                            geemap.create_timelapse(
+                                st.session_state.get("ee_asset_id"),
+                                start_date=start_date.strftime("%Y-%m-%d"),
+                                end_date=end_date.strftime("%Y-%m-%d"),
+                                region=roi,
+                                frequency=frequency,
+                                reducer=reducer,
+                                date_format=data_format,
+                                out_gif=out_gif,
+                                bands=st.session_state.get("bands"),
+                                palette=st.session_state.get("palette"),
+                                vis_params=st.session_state.get("vis_params"),
+                                dimensions=768,
+                                frames_per_second=speed,
+                                crs="EPSG:3857",
+                                overlay_data=overlay_data,
+                                overlay_color=overlay_color,
+                                overlay_width=overlay_width,
+                                overlay_opacity=overlay_opacity,
+                                title=title,
+                                title_xy=("2%", "90%"),
+                                add_text=True,
+                                text_xy=("2%", "2%"),
+                                text_sequence=None,
+                                font_type=font_type,
+                                font_size=font_size,
+                                font_color=font_color,
+                                add_progress_bar=add_progress_bar,
+                                progress_bar_color=progress_bar_color,
+                                progress_bar_height=5,
+                                loop=0,
+                                mp4=mp4,
+                                fading=fading,
+                            )
+                        except:
+                            empty_text.error(
+                                "An error occurred while computing the timelapse. You probably requested too much data. Try reducing the ROI or timespan."
+                            )
+
+                        empty_text.text(
+                            "Right click the GIF to save it to your computer👇"
+                        )
+                        empty_image.image(out_gif)
+
+                        out_mp4 = out_gif.replace(".gif", ".mp4")
+                        if mp4 and os.path.exists(out_mp4):
+                            with empty_video:
+                                st.text(
+                                    "Right click the MP4 to save it to your computer👇"
+                                )
+                                st.video(out_gif.replace(".gif", ".mp4"))
+
+        elif collection in [
+            "MODIS Gap filled Land Surface Temperature Daily",
+            "MODIS Ocean Color SMI",
+        ]:
+
+            with st.form("submit_ts_form"):
+                with st.expander("Customize timelapse"):
+
+                    title = st.text_input(
+                        "Enter a title to show on the timelapse: ",
+                        "Surface Temperature",
+                    )
+                    start_date = st.date_input(
+                        "Select the start date:", datetime.date(2018, 1, 1)
+                    )
+                    end_date = st.date_input(
+                        "Select the end date:", datetime.date(2020, 12, 31)
+                    )
+                    frequency = st.selectbox(
+                        "Select a temporal frequency:",
+                        ["year", "quarter", "month", "week", "day"],
+                        index=2,
+                    )
+                    reducer = st.selectbox(
+                        "Select a reducer for aggregating data:",
+                        ["median", "mean", "min", "max", "sum", "variance", "stdDev"],
+                        index=0,
+                    )
+
+                    vis_params = st.text_area(
+                        "Enter visualization parameters",
+                        "",
+                        help="Enter a string in the format of a dictionary, such as '{'min': 23, 'max': 32}'",
+                    )
+
+                    speed = st.slider("Frames per second:", 1, 30, 5)
+                    add_progress_bar = st.checkbox("Add a progress bar", True)
+                    progress_bar_color = st.color_picker(
+                        "Progress bar color:", "#0000ff"
+                    )
+                    font_size = st.slider("Font size:", 10, 50, 30)
+                    font_color = st.color_picker("Font color:", "#ffffff")
+                    font_type = st.selectbox(
+                        "Select the font type for the title:",
+                        ["arial.ttf", "alibaba.otf"],
+                        index=0,
+                    )
+                    add_colorbar = st.checkbox("Add a colorbar", True)
+                    colorbar_label = st.text_input(
+                        "Enter the colorbar label:", "Surface Temperature (°C)"
+                    )
+                    fading = st.slider(
+                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
+                    )
+                    mp4 = st.checkbox("Save timelapse as MP4", True)
+
+                empty_text = st.empty()
+                empty_image = st.empty()
+                empty_video = st.container()
+
+                roi = None
+                if st.session_state.get("roi") is not None:
+                    roi = st.session_state.get("roi")
+                out_gif = geemap.temp_file_path(".gif")
+
+                submitted = st.form_submit_button("Submit")
+                if submitted:
+
+                    if sample_roi == "Uploaded GeoJSON" and data is None:
+                        empty_text.warning(
+                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
+                        )
+                    else:
+
+                        empty_text.text("Computing... Please wait...")
+                        try:
+                            if (
+                                collection
+                                == "MODIS Gap filled Land Surface Temperature Daily"
+                            ):
+                                out_gif = geemap.create_timelapse(
+                                    st.session_state.get("ee_asset_id"),
+                                    start_date=start_date.strftime("%Y-%m-%d"),
+                                    end_date=end_date.strftime("%Y-%m-%d"),
+                                    region=roi,
+                                    bands=None,
+                                    frequency=frequency,
+                                    reducer=reducer,
+                                    date_format=None,
+                                    out_gif=out_gif,
+                                    palette=st.session_state.get("palette"),
+                                    vis_params=None,
+                                    dimensions=768,
+                                    frames_per_second=speed,
+                                    crs="EPSG:3857",
+                                    overlay_data=overlay_data,
+                                    overlay_color=overlay_color,
+                                    overlay_width=overlay_width,
+                                    overlay_opacity=overlay_opacity,
+                                    title=title,
+                                    title_xy=("2%", "90%"),
+                                    add_text=True,
+                                    text_xy=("2%", "2%"),
+                                    text_sequence=None,
+                                    font_type=font_type,
+                                    font_size=font_size,
+                                    font_color=font_color,
+                                    add_progress_bar=add_progress_bar,
+                                    progress_bar_color=progress_bar_color,
+                                    progress_bar_height=5,
+                                    add_colorbar=add_colorbar,
+                                    colorbar_label=colorbar_label,
+                                    loop=0,
+                                    mp4=mp4,
+                                    fading=fading,
+                                )
+                            elif collection == "MODIS Ocean Color SMI":
+                                if vis_params.startswith("{") and vis_params.endswith(
+                                    "}"
+                                ):
+                                    vis_params = eval(vis_params)
+                                else:
+                                    vis_params = None
+                                out_gif = geemap.modis_ocean_color_timelapse(
+                                    st.session_state.get("ee_asset_id"),
+                                    start_date=start_date.strftime("%Y-%m-%d"),
+                                    end_date=end_date.strftime("%Y-%m-%d"),
+                                    region=roi,
+                                    bands=st.session_state["band"],
+                                    frequency=frequency,
+                                    reducer=reducer,
+                                    date_format=None,
+                                    out_gif=out_gif,
+                                    palette=st.session_state.get("palette"),
+                                    vis_params=vis_params,
+                                    dimensions=768,
+                                    frames_per_second=speed,
+                                    crs="EPSG:3857",
+                                    overlay_data=overlay_data,
+                                    overlay_color=overlay_color,
+                                    overlay_width=overlay_width,
+                                    overlay_opacity=overlay_opacity,
+                                    title=title,
+                                    title_xy=("2%", "90%"),
+                                    add_text=True,
+                                    text_xy=("2%", "2%"),
+                                    text_sequence=None,
+                                    font_type=font_type,
+                                    font_size=font_size,
+                                    font_color=font_color,
+                                    add_progress_bar=add_progress_bar,
+                                    progress_bar_color=progress_bar_color,
+                                    progress_bar_height=5,
+                                    add_colorbar=add_colorbar,
+                                    colorbar_label=colorbar_label,
+                                    loop=0,
+                                    mp4=mp4,
+                                    fading=fading,
+                                )
+                        except:
+                            empty_text.error(
+                                "Something went wrong. You probably requested too much data. Try reducing the ROI or timespan."
+                            )
+
+                        if out_gif is not None and os.path.exists(out_gif):
+
+                            geemap.reduce_gif_size(out_gif)
+
+                            empty_text.text(
+                                "Right click the GIF to save it to your computer👇"
+                            )
+                            empty_image.image(out_gif)
+
+                            out_mp4 = out_gif.replace(".gif", ".mp4")
+                            if mp4 and os.path.exists(out_mp4):
+                                with empty_video:
+                                    st.text(
+                                        "Right click the MP4 to save it to your computer👇"
+                                    )
+                                    st.video(out_gif.replace(".gif", ".mp4"))
+
+                        else:
+                            st.error(
+                                "Something went wrong. You probably requested too much data. Try reducing the ROI or timespan."
+                            )
+
+        elif collection == "USDA National Agriculture Imagery Program (NAIP)":
+
+            with st.form("submit_naip_form"):
+                with st.expander("Customize timelapse"):
+
+                    title = st.text_input(
+                        "Enter a title to show on the timelapse: ", "NAIP Timelapse"
+                    )
+
+                    years = st.slider(
+                        "Start and end year:",
+                        2003,
+                        today.year,
+                        (2003, today.year),
+                    )
+
+                    bands = st.selectbox(
+                        "Select a band combination:", ["N/R/G", "R/G/B"], index=0
+                    )
+
+                    speed = st.slider("Frames per second:", 1, 30, 3)
+                    add_progress_bar = st.checkbox("Add a progress bar", True)
+                    progress_bar_color = st.color_picker(
+                        "Progress bar color:", "#0000ff"
+                    )
+                    font_size = st.slider("Font size:", 10, 50, 30)
+                    font_color = st.color_picker("Font color:", "#ffffff")
+                    font_type = st.selectbox(
+                        "Select the font type for the title:",
+                        ["arial.ttf", "alibaba.otf"],
+                        index=0,
+                    )
+                    fading = st.slider(
+                        "Fading duration (seconds) for each frame:", 0.0, 3.0, 0.0
+                    )
+                    mp4 = st.checkbox("Save timelapse as MP4", True)
+
+                empty_text = st.empty()
+                empty_image = st.empty()
+                empty_video = st.container()
+                empty_fire_image = st.empty()
+
+                roi = None
+                if st.session_state.get("roi") is not None:
+                    roi = st.session_state.get("roi")
+                out_gif = geemap.temp_file_path(".gif")
+
+                submitted = st.form_submit_button("Submit")
+                if submitted:
+
+                    if sample_roi == "Uploaded GeoJSON" and data is None:
+                        empty_text.warning(
+                            "Steps to create a timelapse: Draw a rectangle on the map -> Export it as a GeoJSON -> Upload it back to the app -> Click the Submit button. Alternatively, you can select a sample ROI from the dropdown list."
+                        )
+                    else:
+
+                        empty_text.text("Computing... Please wait...")
+                        try:
+                            geemap.naip_timelapse(
+                                roi,
+                                years[0],
+                                years[1],
+                                out_gif,
+                                bands=bands.split("/"),
+                                palette=st.session_state.get("palette"),
+                                vis_params=None,
+                                dimensions=768,
+                                frames_per_second=speed,
+                                crs="EPSG:3857",
+                                overlay_data=overlay_data,
+                                overlay_color=overlay_color,
+                                overlay_width=overlay_width,
+                                overlay_opacity=overlay_opacity,
+                                title=title,
+                                title_xy=("2%", "90%"),
+                                add_text=True,
+                                text_xy=("2%", "2%"),
+                                text_sequence=None,
+                                font_type=font_type,
+                                font_size=font_size,
+                                font_color=font_color,
+                                add_progress_bar=add_progress_bar,
+                                progress_bar_color=progress_bar_color,
+                                progress_bar_height=5,
+                                loop=0,
+                                mp4=mp4,
+                                fading=fading,
+                            )
+                        except:
+                            empty_text.error(
+                                "Something went wrong. You either requested too much data or the ROI is outside the U.S."
+                            )
+
+                        if out_gif is not None and os.path.exists(out_gif):
+
+                            empty_text.text(
+                                "Right click the GIF to save it to your computer👇"
+                            )
+                            empty_image.image(out_gif)
+
+                            out_mp4 = out_gif.replace(".gif", ".mp4")
+                            if mp4 and os.path.exists(out_mp4):
+                                with empty_video:
+                                    st.text(
+                                        "Right click the MP4 to save it to your computer👇"
+                                    )
+                                    st.video(out_gif.replace(".gif", ".mp4"))
+
+                        else:
+                            st.error(
+                                "Something went wrong. You either requested too much data or the ROI is outside the U.S."
+                            )
+
+
+app()

pages/2_🏠_U.S._Housing.py

@@ -0,0 +1,484 @@
+import datetime
+import os
+import pathlib
+import requests
+import zipfile
+import pandas as pd
+import pydeck as pdk
+import geopandas as gpd
+import streamlit as st
+import leafmap.colormaps as cm
+from leafmap.common import hex_to_rgb
+
+st.set_page_config(layout="wide")
+
+st.sidebar.title("About")
+st.sidebar.info(
+    """
+    Web App URL: <https://geospatial.streamlitapp.com>
+    GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
+    """
+)
+
+st.sidebar.title("Contact")
+st.sidebar.info(
+    """
+    Qiusheng Wu: <https://wetlands.io>
+    [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
+    """
+)
+
+STREAMLIT_STATIC_PATH = pathlib.Path(st.__path__[0]) / "static"
+# We create a downloads directory within the streamlit static asset directory
+# and we write output files to it
+DOWNLOADS_PATH = STREAMLIT_STATIC_PATH / "downloads"
+if not DOWNLOADS_PATH.is_dir():
+    DOWNLOADS_PATH.mkdir()
+
+# Data source: https://www.realtor.com/research/data/
+# link_prefix = "https://econdata.s3-us-west-2.amazonaws.com/Reports/"
+link_prefix = "https://raw.githubusercontent.com/giswqs/data/main/housing/"
+
+data_links = {
+    "weekly": {
+        "national": link_prefix + "Core/listing_weekly_core_aggregate_by_country.csv",
+        "metro": link_prefix + "Core/listing_weekly_core_aggregate_by_metro.csv",
+    },
+    "monthly_current": {
+        "national": link_prefix + "Core/RDC_Inventory_Core_Metrics_Country.csv",
+        "state": link_prefix + "Core/RDC_Inventory_Core_Metrics_State.csv",
+        "metro": link_prefix + "Core/RDC_Inventory_Core_Metrics_Metro.csv",
+        "county": link_prefix + "Core/RDC_Inventory_Core_Metrics_County.csv",
+        "zip": link_prefix + "Core/RDC_Inventory_Core_Metrics_Zip.csv",
+    },
+    "monthly_historical": {
+        "national": link_prefix + "Core/RDC_Inventory_Core_Metrics_Country_History.csv",
+        "state": link_prefix + "Core/RDC_Inventory_Core_Metrics_State_History.csv",
+        "metro": link_prefix + "Core/RDC_Inventory_Core_Metrics_Metro_History.csv",
+        "county": link_prefix + "Core/RDC_Inventory_Core_Metrics_County_History.csv",
+        "zip": link_prefix + "Core/RDC_Inventory_Core_Metrics_Zip_History.csv",
+    },
+    "hotness": {
+        "metro": link_prefix
+        + "Hotness/RDC_Inventory_Hotness_Metrics_Metro_History.csv",
+        "county": link_prefix
+        + "Hotness/RDC_Inventory_Hotness_Metrics_County_History.csv",
+        "zip": link_prefix + "Hotness/RDC_Inventory_Hotness_Metrics_Zip_History.csv",
+    },
+}
+
+
+def get_data_columns(df, category, frequency="monthly"):
+    if frequency == "monthly":
+        if category.lower() == "county":
+            del_cols = ["month_date_yyyymm", "county_fips", "county_name"]
+        elif category.lower() == "state":
+            del_cols = ["month_date_yyyymm", "state", "state_id"]
+        elif category.lower() == "national":
+            del_cols = ["month_date_yyyymm", "country"]
+        elif category.lower() == "metro":
+            del_cols = ["month_date_yyyymm", "cbsa_code", "cbsa_title", "HouseholdRank"]
+        elif category.lower() == "zip":
+            del_cols = ["month_date_yyyymm", "postal_code", "zip_name", "flag"]
+    elif frequency == "weekly":
+        if category.lower() == "national":
+            del_cols = ["week_end_date", "geo_country"]
+        elif category.lower() == "metro":
+            del_cols = ["week_end_date", "cbsa_code", "cbsa_title", "hh_rank"]
+
+    cols = df.columns.values.tolist()
+
+    for col in cols:
+        if col.strip() in del_cols:
+            cols.remove(col)
+    if category.lower() == "metro":
+        return cols[2:]
+    else:
+        return cols[1:]
+
+
+@st.cache
+def get_inventory_data(url):
+    df = pd.read_csv(url)
+    url = url.lower()
+    if "county" in url:
+        df["county_fips"] = df["county_fips"].map(str)
+        df["county_fips"] = df["county_fips"].str.zfill(5)
+    elif "state" in url:
+        df["STUSPS"] = df["state_id"].str.upper()
+    elif "metro" in url:
+        df["cbsa_code"] = df["cbsa_code"].map(str)
+    elif "zip" in url:
+        df["postal_code"] = df["postal_code"].map(str)
+        df["postal_code"] = df["postal_code"].str.zfill(5)
+
+    if "listing_weekly_core_aggregate_by_country" in url:
+        columns = get_data_columns(df, "national", "weekly")
+        for column in columns:
+            if column != "median_days_on_market_by_day_yy":
+                df[column] = df[column].str.rstrip("%").astype(float) / 100
+    if "listing_weekly_core_aggregate_by_metro" in url:
+        columns = get_data_columns(df, "metro", "weekly")
+        for column in columns:
+            if column != "median_days_on_market_by_day_yy":
+                df[column] = df[column].str.rstrip("%").astype(float) / 100
+        df["cbsa_code"] = df["cbsa_code"].str[:5]
+    return df
+
+
+def filter_weekly_inventory(df, week):
+    df = df[df["week_end_date"] == week]
+    return df
+
+
+def get_start_end_year(df):
+    start_year = int(str(df["month_date_yyyymm"].min())[:4])
+    end_year = int(str(df["month_date_yyyymm"].max())[:4])
+    return start_year, end_year
+
+
+def get_periods(df):
+    return [str(d) for d in list(set(df["month_date_yyyymm"].tolist()))]
+
+
+@st.cache
+def get_geom_data(category):
+
+    prefix = (
+        "https://raw.githubusercontent.com/giswqs/streamlit-geospatial/master/data/"
+    )
+    links = {
+        "national": prefix + "us_nation.geojson",
+        "state": prefix + "us_states.geojson",
+        "county": prefix + "us_counties.geojson",
+        "metro": prefix + "us_metro_areas.geojson",
+        "zip": "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_zcta510_500k.zip",
+    }
+
+    if category.lower() == "zip":
+        r = requests.get(links[category])
+        out_zip = os.path.join(DOWNLOADS_PATH, "cb_2018_us_zcta510_500k.zip")
+        with open(out_zip, "wb") as code:
+            code.write(r.content)
+        zip_ref = zipfile.ZipFile(out_zip, "r")
+        zip_ref.extractall(DOWNLOADS_PATH)
+        gdf = gpd.read_file(out_zip.replace("zip", "shp"))
+    else:
+        gdf = gpd.read_file(links[category])
+    return gdf
+
+
+def join_attributes(gdf, df, category):
+
+    new_gdf = None
+    if category == "county":
+        new_gdf = gdf.merge(df, left_on="GEOID", right_on="county_fips", how="outer")
+    elif category == "state":
+        new_gdf = gdf.merge(df, left_on="STUSPS", right_on="STUSPS", how="outer")
+    elif category == "national":
+        if "geo_country" in df.columns.values.tolist():
+            df["country"] = None
+            df.loc[0, "country"] = "United States"
+        new_gdf = gdf.merge(df, left_on="NAME", right_on="country", how="outer")
+    elif category == "metro":
+        new_gdf = gdf.merge(df, left_on="CBSAFP", right_on="cbsa_code", how="outer")
+    elif category == "zip":
+        new_gdf = gdf.merge(df, left_on="GEOID10", right_on="postal_code", how="outer")
+    return new_gdf
+
+
+def select_non_null(gdf, col_name):
+    new_gdf = gdf[~gdf[col_name].isna()]
+    return new_gdf
+
+
+def select_null(gdf, col_name):
+    new_gdf = gdf[gdf[col_name].isna()]
+    return new_gdf
+
+
+def get_data_dict(name):
+    in_csv = os.path.join(os.getcwd(), "data/realtor_data_dict.csv")
+    df = pd.read_csv(in_csv)
+    label = list(df[df["Name"] == name]["Label"])[0]
+    desc = list(df[df["Name"] == name]["Description"])[0]
+    return label, desc
+
+
+def get_weeks(df):
+    seq = list(set(df[~df["week_end_date"].isnull()]["week_end_date"].tolist()))
+    weeks = [
+        datetime.date(int(d.split("/")[2]), int(d.split("/")[0]), int(d.split("/")[1]))
+        for d in seq
+    ]
+    weeks.sort()
+    return weeks
+
+
+def get_saturday(in_date):
+    idx = (in_date.weekday() + 1) % 7
+    sat = in_date + datetime.timedelta(6 - idx)
+    return sat
+
+
+def app():
+
+    st.title("U.S. Real Estate Data and Market Trends")
+    st.markdown(
+        """**Introduction:** This interactive dashboard is designed for visualizing U.S. real estate data and market trends at multiple levels (i.e., national,
+         state, county, and metro). The data sources include [Real Estate Data](https://www.realtor.com/research/data) from realtor.com and 
+         [Cartographic Boundary Files](https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html) from U.S. Census Bureau.
+         Several open-source packages are used to process the data and generate the visualizations, e.g., [streamlit](https://streamlit.io),
+          [geopandas](https://geopandas.org), [leafmap](https://leafmap.org), and [pydeck](https://deckgl.readthedocs.io).
+    """
+    )
+
+    with st.expander("See a demo"):
+        st.image("https://i.imgur.com/Z3dk6Tr.gif")
+
+    row1_col1, row1_col2, row1_col3, row1_col4, row1_col5 = st.columns(
+        [0.6, 0.8, 0.6, 1.4, 2]
+    )
+    with row1_col1:
+        frequency = st.selectbox("Monthly/weekly data", ["Monthly", "Weekly"])
+    with row1_col2:
+        types = ["Current month data", "Historical data"]
+        if frequency == "Weekly":
+            types.remove("Current month data")
+        cur_hist = st.selectbox(
+            "Current/historical data",
+            types,
+        )
+    with row1_col3:
+        if frequency == "Monthly":
+            scale = st.selectbox(
+                "Scale", ["National", "State", "Metro", "County"], index=3
+            )
+        else:
+            scale = st.selectbox("Scale", ["National", "Metro"], index=1)
+
+    gdf = get_geom_data(scale.lower())
+
+    if frequency == "Weekly":
+        inventory_df = get_inventory_data(data_links["weekly"][scale.lower()])
+        weeks = get_weeks(inventory_df)
+        with row1_col1:
+            selected_date = st.date_input("Select a date", value=weeks[-1])
+            saturday = get_saturday(selected_date)
+            selected_period = saturday.strftime("%-m/%-d/%Y")
+            if saturday not in weeks:
+                st.error(
+                    "The selected date is not available in the data. Please select a date between {} and {}".format(
+                        weeks[0], weeks[-1]
+                    )
+                )
+                selected_period = weeks[-1].strftime("%-m/%-d/%Y")
+        inventory_df = get_inventory_data(data_links["weekly"][scale.lower()])
+        inventory_df = filter_weekly_inventory(inventory_df, selected_period)
+
+    if frequency == "Monthly":
+        if cur_hist == "Current month data":
+            inventory_df = get_inventory_data(
+                data_links["monthly_current"][scale.lower()]
+            )
+            selected_period = get_periods(inventory_df)[0]
+        else:
+            with row1_col2:
+                inventory_df = get_inventory_data(
+                    data_links["monthly_historical"][scale.lower()]
+                )
+                start_year, end_year = get_start_end_year(inventory_df)
+                periods = get_periods(inventory_df)
+                with st.expander("Select year and month", True):
+                    selected_year = st.slider(
+                        "Year",
+                        start_year,
+                        end_year,
+                        value=start_year,
+                        step=1,
+                    )
+                    selected_month = st.slider(
+                        "Month",
+                        min_value=1,
+                        max_value=12,
+                        value=int(periods[0][-2:]),
+                        step=1,
+                    )
+                selected_period = str(selected_year) + str(selected_month).zfill(2)
+                if selected_period not in periods:
+                    st.error("Data not available for selected year and month")
+                    selected_period = periods[0]
+                inventory_df = inventory_df[
+                    inventory_df["month_date_yyyymm"] == int(selected_period)
+                ]
+
+    data_cols = get_data_columns(inventory_df, scale.lower(), frequency.lower())
+
+    with row1_col4:
+        selected_col = st.selectbox("Attribute", data_cols)
+    with row1_col5:
+        show_desc = st.checkbox("Show attribute description")
+        if show_desc:
+            try:
+                label, desc = get_data_dict(selected_col.strip())
+                markdown = f"""
+                **{label}**: {desc}
+                """
+                st.markdown(markdown)
+            except:
+                st.warning("No description available for selected attribute")
+
+    row2_col1, row2_col2, row2_col3, row2_col4, row2_col5, row2_col6 = st.columns(
+        [0.6, 0.68, 0.7, 0.7, 1.5, 0.8]
+    )
+
+    palettes = cm.list_colormaps()
+    with row2_col1:
+        palette = st.selectbox("Color palette", palettes, index=palettes.index("Blues"))
+    with row2_col2:
+        n_colors = st.slider("Number of colors", min_value=2, max_value=20, value=8)
+    with row2_col3:
+        show_nodata = st.checkbox("Show nodata areas", value=True)
+    with row2_col4:
+        show_3d = st.checkbox("Show 3D view", value=False)
+    with row2_col5:
+        if show_3d:
+            elev_scale = st.slider(
+                "Elevation scale", min_value=1, max_value=1000000, value=1, step=10
+            )
+            with row2_col6:
+                st.info("Press Ctrl and move the left mouse button.")
+        else:
+            elev_scale = 1
+
+    gdf = join_attributes(gdf, inventory_df, scale.lower())
+    gdf_null = select_null(gdf, selected_col)
+    gdf = select_non_null(gdf, selected_col)
+    gdf = gdf.sort_values(by=selected_col, ascending=True)
+
+    colors = cm.get_palette(palette, n_colors)
+    colors = [hex_to_rgb(c) for c in colors]
+
+    for i, ind in enumerate(gdf.index):
+        index = int(i / (len(gdf) / len(colors)))
+        if index >= len(colors):
+            index = len(colors) - 1
+        gdf.loc[ind, "R"] = colors[index][0]
+        gdf.loc[ind, "G"] = colors[index][1]
+        gdf.loc[ind, "B"] = colors[index][2]
+
+    initial_view_state = pdk.ViewState(
+        latitude=40,
+        longitude=-100,
+        zoom=3,
+        max_zoom=16,
+        pitch=0,
+        bearing=0,
+        height=700,
+        width=None,
+    )
+
+    min_value = gdf[selected_col].min()
+    max_value = gdf[selected_col].max()
+    color = "color"
+    # color_exp = f"[({selected_col}-{min_value})/({max_value}-{min_value})*255, 0, 0]"
+    color_exp = f"[R, G, B]"
+
+    geojson = pdk.Layer(
+        "GeoJsonLayer",
+        gdf,
+        pickable=True,
+        opacity=0.5,
+        stroked=True,
+        filled=True,
+        extruded=show_3d,
+        wireframe=True,
+        get_elevation=f"{selected_col}",
+        elevation_scale=elev_scale,
+        # get_fill_color="color",
+        get_fill_color=color_exp,
+        get_line_color=[0, 0, 0],
+        get_line_width=2,
+        line_width_min_pixels=1,
+    )
+
+    geojson_null = pdk.Layer(
+        "GeoJsonLayer",
+        gdf_null,
+        pickable=True,
+        opacity=0.2,
+        stroked=True,
+        filled=True,
+        extruded=False,
+        wireframe=True,
+        # get_elevation="properties.ALAND/100000",
+        # get_fill_color="color",
+        get_fill_color=[200, 200, 200],
+        get_line_color=[0, 0, 0],
+        get_line_width=2,
+        line_width_min_pixels=1,
+    )
+
+    # tooltip = {"text": "Name: {NAME}"}
+
+    # tooltip_value = f"<b>Value:</b> {median_listing_price}""
+    tooltip = {
+        "html": "<b>Name:</b> {NAME}<br><b>Value:</b> {"
+        + selected_col
+        + "}<br><b>Date:</b> "
+        + selected_period
+        + "",
+        "style": {"backgroundColor": "steelblue", "color": "white"},
+    }
+
+    layers = [geojson]
+    if show_nodata:
+        layers.append(geojson_null)
+
+    r = pdk.Deck(
+        layers=layers,
+        initial_view_state=initial_view_state,
+        map_style="light",
+        tooltip=tooltip,
+    )
+
+    row3_col1, row3_col2 = st.columns([6, 1])
+
+    with row3_col1:
+        st.pydeck_chart(r)
+    with row3_col2:
+        st.write(
+            cm.create_colormap(
+                palette,
+                label=selected_col.replace("_", " ").title(),
+                width=0.2,
+                height=3,
+                orientation="vertical",
+                vmin=min_value,
+                vmax=max_value,
+                font_size=10,
+            )
+        )
+    row4_col1, row4_col2, row4_col3 = st.columns([1, 2, 3])
+    with row4_col1:
+        show_data = st.checkbox("Show raw data")
+    with row4_col2:
+        show_cols = st.multiselect("Select columns", data_cols)
+    with row4_col3:
+        show_colormaps = st.checkbox("Preview all color palettes")
+        if show_colormaps:
+            st.write(cm.plot_colormaps(return_fig=True))
+    if show_data:
+        if scale == "National":
+            st.dataframe(gdf[["NAME", "GEOID"] + show_cols])
+        elif scale == "State":
+            st.dataframe(gdf[["NAME", "STUSPS"] + show_cols])
+        elif scale == "County":
+            st.dataframe(gdf[["NAME", "STATEFP", "COUNTYFP"] + show_cols])
+        elif scale == "Metro":
+            st.dataframe(gdf[["NAME", "CBSAFP"] + show_cols])
+        elif scale == "Zip":
+            st.dataframe(gdf[["GEOID10"] + show_cols])
+
+
+app()

pages/3_🪟_Split_Map.py

@@ -0,0 +1,32 @@
+import streamlit as st
+import leafmap.foliumap as leafmap
+
+st.set_page_config(layout="wide")
+
+st.sidebar.title("About")
+st.sidebar.info(
+    """
+    Web App URL: <https://geospatial.streamlitapp.com>
+    GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
+    """
+)
+
+st.sidebar.title("Contact")
+st.sidebar.info(
+    """
+    Qiusheng Wu: <https://wetlands.io>
+    [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
+    """
+)
+
+st.title("Split-panel Map")
+
+with st.expander("See source code"):
+    with st.echo():
+        m = leafmap.Map()
+        m.split_map(
+            left_layer='ESA WorldCover 2020 S2 FCC', right_layer='ESA WorldCover 2020'
+        )
+        m.add_legend(title='ESA Land Cover', builtin_legend='ESA_WorldCover')
+
+m.to_streamlit(height=700)

pages/4_🔥_Heatmap.py

@@ -0,0 +1,30 @@
+import streamlit as st
+import leafmap.foliumap as leafmap
+
+st.set_page_config(layout="wide")
+
+markdown = """
+Web App URL: <https://template.streamlitapp.com>
+GitHub Repository: <https://github.com/giswqs/streamlit-multipage-template>
+"""
+
+st.sidebar.title("About")
+st.sidebar.info(markdown)
+logo = "https://i.imgur.com/UbOXYAU.png"
+st.sidebar.image(logo)
+
+st.title("Heatmap")
+
+with st.expander("See source code"):
+    with st.echo():
+        filepath = "https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/us_cities.csv"
+        m = leafmap.Map(center=[40, -100], zoom=4, tiles="stamentoner")
+        m.add_heatmap(
+            filepath,
+            latitude="latitude",
+            longitude="longitude",
+            value="pop_max",
+            name="Heat map",
+            radius=20,
+        )
+m.to_streamlit(height=700)

pages/5_📍_Marker_Cluster.py

@@ -0,0 +1,42 @@
+import streamlit as st
+import leafmap.foliumap as leafmap
+
+st.set_page_config(layout="wide")
+
+st.sidebar.title("About")
+st.sidebar.info(
+    """
+    Web App URL: <https://geospatial.streamlitapp.com>
+    GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
+    """
+)
+
+st.sidebar.title("Contact")
+st.sidebar.info(
+    """
+    Qiusheng Wu: <https://wetlands.io>
+    [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
+    """
+)
+
+st.title("Marker Cluster")
+
+with st.expander("See source code"):
+    with st.echo():
+
+        m = leafmap.Map(center=[40, -100], zoom=4)
+        cities = 'https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/us_cities.csv'
+        regions = 'https://raw.githubusercontent.com/giswqs/leafmap/master/examples/data/us_regions.geojson'
+
+        m.add_geojson(regions, layer_name='US Regions')
+        m.add_points_from_xy(
+            cities,
+            x="longitude",
+            y="latitude",
+            color_column='region',
+            icon_names=['gear', 'map', 'leaf', 'globe'],
+            spin=True,
+            add_legend=True,
+        )
+
+m.to_streamlit(height=700)

pages/6_🗺️_Basemaps.py

@@ -0,0 +1,62 @@
+import streamlit as st
+import leafmap.foliumap as leafmap
+
+st.set_page_config(layout="wide")
+
+st.sidebar.title("About")
+st.sidebar.info(
+    """
+    Web App URL: <https://geospatial.streamlitapp.com>
+    GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
+    """
+)
+
+st.sidebar.title("Contact")
+st.sidebar.info(
+    """
+    Qiusheng Wu: <https://wetlands.io>
+    [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
+    """
+)
+
+
+def app():
+    st.title("Search Basemaps")
+    st.markdown(
+        """
+    This app is a demonstration of searching and loading basemaps from [xyzservices](https://github.com/geopandas/xyzservices) and [Quick Map Services (QMS)](https://github.com/nextgis/quickmapservices). Selecting from 1000+ basemaps with a few clicks.  
+    """
+    )
+
+    with st.expander("See demo"):
+        st.image("https://i.imgur.com/0SkUhZh.gif")
+
+    row1_col1, row1_col2 = st.columns([3, 1])
+    width = 800
+    height = 600
+    tiles = None
+
+    with row1_col2:
+
+        checkbox = st.checkbox("Search Quick Map Services (QMS)")
+        keyword = st.text_input("Enter a keyword to search and press Enter:")
+        empty = st.empty()
+
+        if keyword:
+            options = leafmap.search_xyz_services(keyword=keyword)
+            if checkbox:
+                options = options + leafmap.search_qms(keyword=keyword)
+
+            tiles = empty.multiselect("Select XYZ tiles to add to the map:", options)
+
+        with row1_col1:
+            m = leafmap.Map()
+
+            if tiles is not None:
+                for tile in tiles:
+                    m.add_xyz_service(tile)
+
+            m.to_streamlit(width, height)
+
+
+app()

pages/7_📦_Web_Map_Service.py

@@ -0,0 +1,89 @@
+import ast
+import streamlit as st
+import leafmap.foliumap as leafmap
+
+st.set_page_config(layout="wide")
+
+st.sidebar.title("About")
+st.sidebar.info(
+    """
+    Web App URL: <https://geospatial.streamlitapp.com>
+    GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
+    """
+)
+
+st.sidebar.title("Contact")
+st.sidebar.info(
+    """
+    Qiusheng Wu: <https://wetlands.io>
+    [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
+    """
+)
+
+
+@st.cache
+def get_layers(url):
+    options = leafmap.get_wms_layers(url)
+    return options
+
+
+def app():
+    st.title("Web Map Service (WMS)")
+    st.markdown(
+        """
+    This app is a demonstration of loading Web Map Service (WMS) layers. Simply enter the URL of the WMS service 
+    in the text box below and press Enter to retrieve the layers. Go to https://apps.nationalmap.gov/services to find 
+    some WMS URLs if needed.
+    """
+    )
+
+    row1_col1, row1_col2 = st.columns([3, 1.3])
+    width = 800
+    height = 600
+    layers = None
+
+    with row1_col2:
+
+        esa_landcover = "https://services.terrascope.be/wms/v2"
+        url = st.text_input(
+            "Enter a WMS URL:", value="https://services.terrascope.be/wms/v2"
+        )
+        empty = st.empty()
+
+        if url:
+            options = get_layers(url)
+
+            default = None
+            if url == esa_landcover:
+                default = "WORLDCOVER_2020_MAP"
+            layers = empty.multiselect(
+                "Select WMS layers to add to the map:", options, default=default
+            )
+            add_legend = st.checkbox("Add a legend to the map", value=True)
+            if default == "WORLDCOVER_2020_MAP":
+                legend = str(leafmap.builtin_legends["ESA_WorldCover"])
+            else:
+                legend = ""
+            if add_legend:
+                legend_text = st.text_area(
+                    "Enter a legend as a dictionary {label: color}",
+                    value=legend,
+                    height=200,
+                )
+
+        with row1_col1:
+            m = leafmap.Map(center=(36.3, 0), zoom=2)
+
+            if layers is not None:
+                for layer in layers:
+                    m.add_wms_layer(
+                        url, layers=layer, name=layer, attribution=" ", transparent=True
+                    )
+            if add_legend and legend_text:
+                legend_dict = ast.literal_eval(legend_text)
+                m.add_legend(legend_dict=legend_dict)
+
+            m.to_streamlit(width, height)
+
+
+app()

pages/8_🏜️_Raster_Data_Visualization.py

@@ -0,0 +1,98 @@
+import os
+import leafmap.foliumap as leafmap
+import streamlit as st
+import palettable
+
+st.set_page_config(layout="wide")
+
+st.sidebar.title("About")
+st.sidebar.info(
+    """
+    Web App URL: <https://geospatial.streamlitapp.com>
+    GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
+    """
+)
+
+st.sidebar.title("Contact")
+st.sidebar.info(
+    """
+    Qiusheng Wu: <https://wetlands.io>
+    [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
+    """
+)
+
+
+@st.cache
+def load_cog_list():
+    print(os.getcwd())
+    in_txt = os.path.join(os.getcwd(), "data/cog_files.txt")
+    with open(in_txt) as f:
+        return [line.strip() for line in f.readlines()[1:]]
+
+
+@st.cache
+def get_palettes():
+    palettes = dir(palettable.matplotlib)[:-16]
+    return ["matplotlib." + p for p in palettes]
+
+
+def app():
+
+    st.title("Visualize Raster Datasets")
+    st.markdown(
+        """
+    An interactive web app for visualizing local raster datasets and Cloud Optimized GeoTIFF ([COG](https://www.cogeo.org)). The app was built using [streamlit](https://streamlit.io), [leafmap](https://leafmap.org), and [localtileserver](https://github.com/banesullivan/localtileserver).
+
+
+    """
+    )
+
+    row1_col1, row1_col2 = st.columns([2, 1])
+
+    with row1_col1:
+        cog_list = load_cog_list()
+        cog = st.selectbox("Select a sample Cloud Opitmized GeoTIFF (COG)", cog_list)
+
+    with row1_col2:
+        empty = st.empty()
+
+        url = empty.text_input(
+            "Enter a HTTP URL to a Cloud Optimized GeoTIFF (COG)",
+            cog,
+        )
+
+        data = st.file_uploader("Upload a raster dataset", type=["tif", "img"])
+
+        if data:
+            url = empty.text_input(
+                "Enter a URL to a Cloud Optimized GeoTIFF (COG)",
+                "",
+            )
+
+        add_palette = st.checkbox("Add a color palette")
+        if add_palette:
+            palette = st.selectbox("Select a color palette", get_palettes())
+        else:
+            palette = None
+
+        submit = st.button("Submit")
+
+    m = leafmap.Map(latlon_control=False)
+
+    if submit:
+        if data or url:
+            try:
+                if data:
+                    file_path = leafmap.save_data(data)
+                    m.add_local_tile(file_path, palette=palette, debug=True)
+                elif url:
+                    m.add_remote_tile(url, palette=palette, debug=True)
+            except Exception as e:
+                with row1_col2:
+                    st.error("Work in progress. Try it again later.")
+
+    with row1_col1:
+        m.to_streamlit()
+
+
+app()

pages/9_🔲_Vector_Data_Visualization.py

@@ -0,0 +1,118 @@
+import os
+import geopandas as gpd
+import streamlit as st
+
+st.set_page_config(layout="wide")
+
+st.sidebar.title("About")
+st.sidebar.info(
+    """
+    Web App URL: <https://geospatial.streamlitapp.com>
+    GitHub repository: <https://github.com/giswqs/streamlit-geospatial>
+    """
+)
+
+st.sidebar.title("Contact")
+st.sidebar.info(
+    """
+    Qiusheng Wu: <https://wetlands.io>
+    [GitHub](https://github.com/giswqs) | [Twitter](https://twitter.com/giswqs) | [YouTube](https://www.youtube.com/c/QiushengWu) | [LinkedIn](https://www.linkedin.com/in/qiushengwu)
+    """
+)
+
+
+def save_uploaded_file(file_content, file_name):
+    """
+    Save the uploaded file to a temporary directory
+    """
+    import tempfile
+    import os
+    import uuid
+
+    _, file_extension = os.path.splitext(file_name)
+    file_id = str(uuid.uuid4())
+    file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{file_extension}")
+
+    with open(file_path, "wb") as file:
+        file.write(file_content.getbuffer())
+
+    return file_path
+
+
+def app():
+
+    st.title("Upload Vector Data")
+
+    row1_col1, row1_col2 = st.columns([2, 1])
+    width = 950
+    height = 600
+
+    with row1_col2:
+
+        backend = st.selectbox(
+            "Select a plotting backend", ["folium", "kepler.gl", "pydeck"], index=2
+        )
+
+        if backend == "folium":
+            import leafmap.foliumap as leafmap
+        elif backend == "kepler.gl":
+            import leafmap.kepler as leafmap
+        elif backend == "pydeck":
+            import leafmap.deck as leafmap
+
+        url = st.text_input(
+            "Enter a URL to a vector dataset",
+            "https://github.com/giswqs/streamlit-geospatial/raw/master/data/us_states.geojson",
+        )
+
+        data = st.file_uploader(
+            "Upload a vector dataset", type=["geojson", "kml", "zip", "tab"]
+        )
+
+        container = st.container()
+
+        if data or url:
+            if data:
+                file_path = save_uploaded_file(data, data.name)
+                layer_name = os.path.splitext(data.name)[0]
+            elif url:
+                file_path = url
+                layer_name = url.split("/")[-1].split(".")[0]
+
+            with row1_col1:
+                if file_path.lower().endswith(".kml"):
+                    gpd.io.file.fiona.drvsupport.supported_drivers["KML"] = "rw"
+                    gdf = gpd.read_file(file_path, driver="KML")
+                else:
+                    gdf = gpd.read_file(file_path)
+                lon, lat = leafmap.gdf_centroid(gdf)
+                if backend == "pydeck":
+
+                    column_names = gdf.columns.values.tolist()
+                    random_column = None
+                    with container:
+                        random_color = st.checkbox("Apply random colors", True)
+                        if random_color:
+                            random_column = st.selectbox(
+                                "Select a column to apply random colors", column_names
+                            )
+
+                    m = leafmap.Map(center=(lat, lon))
+                    m.add_gdf(gdf, random_color_column=random_column)
+                    st.pydeck_chart(m)
+
+                else:
+                    m = leafmap.Map(center=(lat, lon), draw_export=True)
+                    m.add_gdf(gdf, layer_name=layer_name)
+                    # m.add_vector(file_path, layer_name=layer_name)
+                    if backend == "folium":
+                        m.zoom_to_gdf(gdf)
+                    m.to_streamlit(width=width, height=height)
+
+        else:
+            with row1_col1:
+                m = leafmap.Map()
+                st.pydeck_chart(m)
+
+
+app()

postBuild

@@ -0,0 +1,6 @@
+# enable nbserverproxy
+jupyter serverextension enable --sys-prefix nbserverproxy
+# streamlit launches at startup
+mv streamlit_call.py ${NB_PYTHON_PREFIX}/lib/python*/site-packages/
+# enable streamlit extension
+jupyter serverextension enable --sys-prefix streamlit_call

requirements.txt

@@ -1,18 +1,20 @@
 --find-links=https://girder.github.io/large_image_wheels GDAL
 # cartopy
+geemap
 geopandas
+jupyter-server-proxy
 keplergl
-palettable
+leafmap
 localtileserver
+nbserverproxy
 owslib
+palettable
 plotly
 streamlit
+streamlit-bokeh-events
 streamlit-folium
 streamlit-keplergl
-streamlit-bokeh-events
 tropycal
-# leafmap
-# geemap
-git+https://github.com/giswqs/leafmap
-git+https://github.com/giswqs/geemap
+# git+https://github.com/giswqs/leafmap
+# git+https://github.com/giswqs/geemap
 

streamlit_app.py

@@ -0,0 +1,40 @@
+import streamlit as st
+import leafmap.foliumap as leafmap
+
+st.set_page_config(layout="wide")
+
+# Customize the sidebar
+markdown = """
+Web App URL: <https://template.streamlitapp.com>
+GitHub Repository: <https://github.com/giswqs/streamlit-multipage-template>
+"""
+
+st.sidebar.title("About")
+st.sidebar.info(markdown)
+logo = "https://i.imgur.com/UbOXYAU.png"
+st.sidebar.image(logo)
+
+# Customize page title
+st.title("Streamlit for Geospatial Applications")
+
+st.markdown(
+    """
+    This multipage app template demonstrates various interactive web apps created using [streamlit](https://streamlit.io) and [leafmap](https://leafmap.org). It is an open-source project and you are very welcome to contribute to the [GitHub repository](https://github.com/giswqs/streamlit-multipage-template).
+    """
+)
+
+st.header("Instructions")
+
+markdown = """
+1. For the [GitHub repository](https://github.com/giswqs/streamlit-multipage-template) or [use it as a template](https://github.com/giswqs/streamlit-multipage-template/generate) for your own project.
+2. Customize the sidebar by changing the sidebar text and logo in each Python files.
+3. Find your favorite emoji from https://emojipedia.org.
+4. Add a new app to the `pages/` directory with an emoji in the file name, e.g., `1_🚀_Chart.py`.
+
+"""
+
+st.markdown(markdown)
+
+m = leafmap.Map(minimap_control=True)
+m.add_basemap("OpenTopoMap")
+m.to_streamlit(height=500)

streamlit_call.py

@@ -0,0 +1,14 @@
+from subprocess import Popen
+
+
+def load_jupyter_server_extension(nbapp):
+    """serve the streamlit app"""
+    Popen(
+        [
+            "streamlit",
+            "run",
+            "Home.py",
+            "--browser.serverAddress=0.0.0.0",
+            "--server.enableCORS=False",
+        ]
+    )