outputLib.thumbs Examples

Comprehensive examples of all geeViz.outputLib.thumbs output types: single thumbnails, animated GIFs, filmstrip grids, multi-feature grids, and map+chart GIFs.

Covers: basemap compositing, legends (thematic + continuous), scalebar/north arrow, inset overview maps, geometry burn-in, clip-to-geometry, CRS/projection control, font sizing, and theme options.

Setup

import geeViz.geeView as gv
import geeViz.getImagesLib as gil
from geeViz.outputLib import thumbs as tl
import geeViz.getSummaryAreasLib as sal
from IPython.display import Image, display, HTML
import os

ee = gv.ee

# Output directory
output_dir = os.path.join(os.path.dirname(os.path.abspath("__file__")), "outputs", "thumbLib_examples")
os.makedirs(output_dir, exist_ok=True)
print(f"Output dir: {output_dir}")

Define Study Areas, Projections, and Datasets

# Study area

yellowstone_pt = ee.Geometry.Point([-110.5, 44.6]).transform('EPSG:3857')
# print(yellowstone_pt)
study_area = sal.simple_buffer(yellowstone_pt,15000)

# Multi-feature study areas using the updated simple_buffer method.
# Directly pass ee.Geometry.Point to simple_buffer (no coordinate extraction needed).

features = ee.FeatureCollection([
    ee.Feature(sal.simple_buffer(ee.Geometry.Point([-110.6, 44.65]), 8000), {"name": "Old Faithful"}),
    ee.Feature(sal.simple_buffer(ee.Geometry.Point([-110.35, 44.55]), 8000), {"name": "Yellowstone Lake"}),
])

four_features = ee.FeatureCollection([
    ee.Feature(sal.simple_buffer(ee.Geometry.Point([-110.6, 44.65]), 8000), {"name": "Old Faithful"}),
    ee.Feature(sal.simple_buffer(ee.Geometry.Point([-110.35, 44.55]), 8000), {"name": "YS Lake"}),
    ee.Feature(sal.simple_buffer(ee.Geometry.Point([-110.5, 44.7]), 8000), {"name": "Mammoth"}),
    ee.Feature(sal.simple_buffer(ee.Geometry.Point([-110.4, 44.45]), 8000), {"name": "South Entrance"}),
])

# CRS options
crs_utm = "EPSG:32612"
crs_albers = "EPSG:5070"

# Thematic: LCMS Land Cover
lcms = ee.ImageCollection("USFS/GTAC/LCMS/v2024-10").select(["Land_Cover"]).filterBounds(yellowstone_pt)
lcms_single = lcms.filter(ee.Filter.calendarRange(2024, 2024, "year")).first()

# Multi-year LCMS for filmstrip/GIF
years_short = [2000, 2010, 2020, 2024]

lcms_ic = lcms.filter(ee.Filter.inList('year',years_short))

# Continuous: S2 composites
startYear, endYear = 2020, 2024
s2 = gil.superSimpleGetS2(
    study_area, f"{startYear}-01-01", f"{endYear}-12-31", 152, 273
)

def annual_composite(yr, imgs):
    yr = ee.Number(yr)
    filtered = imgs.filter(ee.Filter.calendarRange(yr, yr, "year"))
    return filtered.median().set(filtered.first().toDictionary()).set(
        "system:time_start", ee.Date.fromYMD(yr, 6, 1).millis())

s2_composites = ee.ImageCollection(
    ee.List.sequence(startYear, endYear).map(lambda yr: annual_composite(yr, s2))
)
s2_single = annual_composite(2024, s2)

# Add NDVI
s2_single_ndvi = gil.simpleAddIndices(s2_single).select("NDVI")
s2_composites_ndvi = s2_composites.map(
    lambda img: gil.simpleAddIndices(img).select("NDVI").copyProperties(img, ["system:time_start"])
)

# Viz params
viz_false = {"min": 500, "max": [5000, 6000, 6000], "bands": "swir1,nir,red", "gamma": 1.6}
viz_ndvi = {"bands": ["NDVI"], "min": 0, "max": 0.8, "palette": "brown,yellow,green"}

print("Setup complete")

1. Single Thumbnails - generate_thumbs()

Thematic data with basemap, legend, scalebar, north arrow

# 1a. Thematic, default: no basemap, with legend and north arrow
r = tl.generate_thumbs(
    lcms_single, study_area,
    output_path=os.path.join(output_dir, "01a_thematic_default_no_basemap.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Default Thematic | No basemap | Legend + scalebar + north arrow")

# 1b. Thematic, satellite basemap
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    output_path=os.path.join(output_dir, "01b_thematic_satellite_basemap.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Thematic | Satellite basemap | Legend + scalebar + north arrow")

# 1c. Thematic, topo basemap, inset ON the map (lower overlay opacity)
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-topo",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    inset_map=True, inset_on_map=True,overlay_opacity=0.4,
    output_path=os.path.join(output_dir, "01c_thematic_topo_inset_on_map.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Thematic | Topo basemap | Inset ON map (lower-right corner) | Overlay Opacity 0.4")

# 1d. Thematic, satellite basemap, inset OFF map (in legend column)
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    inset_map=True, inset_on_map=False,
    output_path=os.path.join(output_dir, "01d_thematic_inset_off_map.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Thematic | Satellite basemap | Inset OFF map (in legend column)")

2. Geometry Burn-in & Clip Options

# 2a. White outline, clipped to geometry (default)
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="white", geometry_fill_color="#F00F",clip_to_geometry=True,
    output_path=os.path.join(output_dir, "02a_burnin_white_clipped.png"),
)
display(Image(data=r["thumb_bytes"]))
print("White outline | Clipped to geometry")

# 2b. Green outline, NOT clipped
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="green", clip_to_geometry=False,
    output_path=os.path.join(output_dir, "02b_burnin_green_unclipped.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Green outline | NOT clipped (data extends beyond boundary)")

# 2c. Yellow outline with semi-transparent fill, NOT clipped
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="yellow",
    geometry_fill_color="FFFF0033", clip_to_geometry=False,
    output_path=os.path.join(output_dir, "02c_burnin_yellow_fill.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Yellow outline + semi-transparent fill | NOT clipped")

# 2d. Geometry-only (ee_obj=None), satellite basemap
r = tl.generate_thumbs(
    None, study_area,
    basemap="esri-satellite",
    scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="red", geometry_fill_color="FF000033",
    output_path=os.path.join(output_dir, "02d_geometry_only_satellite.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Geometry-only (no raster data) | Satellite basemap")

# 2e. Geometry-only, topo basemap
r = tl.generate_thumbs(
    None, study_area,
    basemap="esri-topo",
    scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="green", geometry_fill_color="00FF0033",
    output_path=os.path.join(output_dir, "02e_geometry_only_topo.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Geometry-only | Topo basemap")

3. Continuous Data

# 3a. False color composite (3-band RGB, no legend)
r = tl.generate_thumbs(
    s2_single, study_area,
    viz_params=viz_false,
    basemap="esri-satellite", scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="white",
    output_path=os.path.join(output_dir, "03a_continuous_false_color.png"),
)
display(Image(data=r["thumb_bytes"]))
print("S2 False Color (swir1, nir, red) | No legend (3-band RGB)")

# 3b. NDVI with palette and continuous colorbar legend
r = tl.generate_thumbs(
    s2_single_ndvi, study_area,
    viz_params=viz_ndvi,
    basemap="esri-satellite", burn_in_legend=True,
    scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="white",
    output_path=os.path.join(output_dir, "03b_continuous_ndvi_legend.png"),
)
display(Image(data=r["thumb_bytes"]))
print("NDVI | Continuous colorbar legend | Satellite basemap")

# 3c. Auto-viz continuous (no viz_params — auto-detects stretch)
r = tl.generate_thumbs(
    s2_single_ndvi, study_area,
    basemap="esri-satellite", burn_in_legend=True,
    scalebar=True, north_arrow=True,
    output_path=os.path.join(output_dir, "03c_continuous_auto_viz.png"),
)
display(Image(data=r["thumb_bytes"]))
print("NDVI | Auto-viz (no viz_params provided)")

4. CRS / Projection Options

# 4a. UTM projection
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    crs=crs_utm, scale=30,
    burn_in_geometry=True, geometry_outline_color="white",
    output_path=os.path.join(output_dir, "04a_utm_projection.png"),
)
display(Image(data=r["thumb_bytes"]))
print(f"UTM ({crs_utm}) | Basemap auto-reprojected")

# 4b. Albers Equal Area (EPSG:5070)
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    crs=crs_albers, scale=30,
    burn_in_geometry=True, geometry_outline_color="white",
    output_path=os.path.join(output_dir, "04b_albers_projection.png"),
)
display(Image(data=r["thumb_bytes"]))
print(f"Albers ({crs_albers}) | North arrow rotated for grid convergence")

# 4c. Albers, geometry-only
r = tl.generate_thumbs(
    None, study_area,
    basemap="esri-topo",
    scalebar=True, north_arrow=True,
    crs=crs_albers,
    burn_in_geometry=True, geometry_outline_color="green", geometry_fill_color="00FF0033",
    output_path=os.path.join(output_dir, "04c_albers_geometry_only.png"),
)
display(Image(data=r["thumb_bytes"]))
print(f"Albers ({crs_albers}) | Geometry-only | Topo basemap")

# 4d. Default CRS (EPSG:3857) for comparison
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="white",
    output_path=os.path.join(output_dir, "04d_default_crs_3857.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Default CRS (EPSG:3857) | Compare with UTM and Albers above")

5. Animated GIFs - generate_gif()

# 5a. Thematic GIF with all cartographic elements
r = tl.generate_gif(
    lcms_ic, study_area,
    basemap="esri-satellite",
    burn_in_date=True, burn_in_legend=True,
    scalebar=True, north_arrow=True,
    inset_map=True,
    burn_in_geometry=True, geometry_outline_color="white",
    title="LCMS Land Cover",
    output_path=os.path.join(output_dir, "05a_gif_thematic_full.gif"),
)
display(Image(data=r["gif_bytes"]))
print("Thematic GIF | Satellite basemap | Date + legend + inset + scalebar")

# 5b. Continuous GIF (false color)
r = tl.generate_gif(
    s2_composites, study_area,
    viz_params=viz_false,
    basemap="esri-satellite",
    burn_in_date=True,
    scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="white",
    title="Sentinel-2 False Color",
    output_path=os.path.join(output_dir, "05b_gif_continuous_false.gif"),
)
display(Image(data=r["gif_bytes"]))
print("Continuous GIF | S2 False Color | Date labels")

# 5c. NDVI GIF with continuous legend
r = tl.generate_gif(
    s2_composites_ndvi, study_area,
    viz_params=viz_ndvi,
    basemap="esri-satellite",
    burn_in_date=True, burn_in_legend=True,
    scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="white",
    title="NDVI",
    output_path=os.path.join(output_dir, "05c_gif_ndvi_legend.gif"),
)
display(Image(data=r["gif_bytes"]))
print("NDVI GIF | Continuous colorbar legend")

6. Filmstrip Grid - generate_filmstrip()

# 6a. 4-column single row, inset BESIDE legend
r = tl.generate_filmstrip(
    lcms_ic, study_area, columns=4,
    basemap="esri-satellite", burn_in_legend=True,
    scalebar=True, north_arrow=True,
    inset_map=True, inset_on_map=False,
    burn_in_geometry=True, geometry_outline_color="white",
    title="LCMS Land Cover - 4 Columns",
    output_path=os.path.join(output_dir, "06a_filmstrip_4col_inset_beside.png"),
)
display(Image(data=r["thumb_bytes"]))
print("4-col filmstrip | Inset beside legend (single row)")

# 6b. 2-column grid (2x2), inset at row 2
r = tl.generate_filmstrip(
    lcms_ic, study_area, columns=2,
    basemap="esri-satellite", burn_in_legend=True,
    scalebar=True, north_arrow=True,
    inset_map=True, inset_on_map=False,
    burn_in_geometry=True, geometry_outline_color="white",
    title="LCMS Land Cover - 2x2 Grid",
    output_path=os.path.join(output_dir, "06b_filmstrip_2x2_inset_row2.png"),
)
display(Image(data=r["thumb_bytes"]))
print("2x2 grid | Legend at row 1, inset at row 2")

# 6c. Filmstrip, inset ON the map (last frame)
r = tl.generate_filmstrip(
    lcms_ic, study_area, columns=4,
    basemap="esri-satellite", burn_in_legend=True,
    scalebar=True, north_arrow=True,
    inset_map=True, inset_on_map=True,
    burn_in_geometry=True, geometry_outline_color="white",
    title="Inset On Map (Last Frame)",
    output_path=os.path.join(output_dir, "06c_filmstrip_inset_on_map.png"),
)
display(Image(data=r["thumb_bytes"]))
print("4-col filmstrip | Inset ON last frame (lower-right)")

# 6d. Continuous NDVI filmstrip with colorbar legend
r = tl.generate_filmstrip(
    s2_composites_ndvi, study_area,
    viz_params=viz_ndvi, columns=5,
    basemap="esri-satellite", burn_in_legend=True,
    scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="white",
    title="NDVI Filmstrip 2020-2024",
    output_path=os.path.join(output_dir, "06d_filmstrip_ndvi_continuous.png"),
)
display(Image(data=r["thumb_bytes"]))
print("NDVI Filmstrip | Continuous colorbar legend")

# 6e. Filmstrip without basemap
r = tl.generate_filmstrip(
    lcms_ic, study_area, columns=4,
    burn_in_legend=True, scalebar=True, north_arrow=True,
    title="No Basemap",
    output_path=os.path.join(output_dir, "06e_filmstrip_no_basemap.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Filmstrip | No basemap")

7. Multi-Feature Grids - generate_thumbs() with FeatureCollection

# 7a. Multi-feature thematic (2 features side-by-side)
r = tl.generate_thumbs(
    lcms_single, features, feature_label="name",
    basemap="esri-satellite", burn_in_legend=True,
    scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="white",
    inset_map=True, inset_on_map=False,
    output_path=os.path.join(output_dir, "07a_multi_feature_thematic.png"),
)
display(Image(data=r["thumb_bytes"]))
print(f"Multi-feature thematic | 2 features | is_grid={r.get('is_grid')}")

# 7b. Multi-feature continuous (NDVI)
r = tl.generate_thumbs(
    s2_single_ndvi, features, feature_label="name",
    viz_params=viz_ndvi,
    basemap="esri-satellite", burn_in_legend=True,
    scalebar=True, north_arrow=True,
    burn_in_geometry=True, geometry_outline_color="white",
    output_path=os.path.join(output_dir, "07b_multi_feature_ndvi.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Multi-feature NDVI | Continuous colorbar")

# 7c. 4-feature 2x2 grid with inset
r = tl.generate_thumbs(
    lcms_single, four_features, feature_label="name",
    columns=2,
    basemap="esri-satellite", burn_in_legend=True,
    scalebar=True, north_arrow=True,
    inset_map=True, inset_on_map=False,
    burn_in_geometry=True, geometry_outline_color="white",
    output_path=os.path.join(output_dir, "07c_multi_feature_4_grid.png"),
)
display(Image(data=r["thumb_bytes"]))
print("4-feature 2x2 grid | Legend at row 1, inset at row 2")

8. Map + Chart - generate_map_chart() and generate_map_chart_gif()

generate_map_chart() produces a combined map thumbnail + chart output. For ee.Image inputs it returns a static PNG; for ee.ImageCollection inputs it auto-delegates to generate_map_chart_gif() and returns an animated GIF. The title and legend are de-duplicated (title appears once above the combined output; thematic legend on the map only).

# 8a. Image + single geometry - bar chart (default)
r = tl.generate_map_chart(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_geometry=True, geometry_outline_color="white",
    title="LCMS Land Cover - Bar",
    chart_scale=30,
    output_path=os.path.join(output_dir, "08a_map_chart_bar.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Map + Bar chart | Legend on thumb only, no chart legend")

# 8b. Image + single geometry - donut chart
r = tl.generate_map_chart(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_geometry=True, geometry_outline_color="white",
    chart_type="donut",
    title="LCMS Land Cover - Donut",
    chart_scale=30,
    output_path=os.path.join(output_dir, "08b_map_chart_donut.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Map + Donut chart")

# 8c. Image + multi-feature FC - grouped bar
r = tl.generate_map_chart(
    lcms_single, four_features,
    feature_label="name", columns=2,
    basemap="esri-satellite",
    burn_in_geometry=True, geometry_outline_color="white",
    chart_type="stacked_bar",
    title="LCMS by Feature - Grouped Bar",
    chart_scale=30,
    output_path=os.path.join(output_dir, "08c_map_chart_multi_bar.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Multi-feature map grid + grouped bar chart")

# 8d. Scatter — S2 swir2 vs nir coloured by LCMS Land Cover
s2_lc = ee.Image(s2_single.select(["swir2", "nir"]).addBands(
    lcms_single
).copyProperties(lcms_single)).setDefaultProjection(crs_albers)

scatter_pts = ee.FeatureCollection.randomPoints(study_area, 500, seed=42)

r = tl.generate_map_chart(
    s2_lc, scatter_pts,
    chart_type="scatter",
    band_names=["swir2", "nir"],
    thematic_band_name="Land_Cover",
    basemap="esri-satellite",
    geometry_outline_color="yellow",
    title="Scatter: SWIR2 vs NIR by Land Cover",
    chart_scale=30,
    opacity=0.5,
    output_path=os.path.join(output_dir, "08d_map_chart_scatter.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Map (with sample points) + scatter plot coloured by Land Cover class")

# 8e. ImageCollection auto-delegates to GIF
r = tl.generate_map_chart(
    lcms_ic, study_area,
    basemap="esri-satellite",
    burn_in_geometry=True, geometry_outline_color="white",
    title="LCMS Time Series (auto GIF)",
    chart_scale=30,
    output_path=os.path.join(output_dir, "08e_map_chart_auto_gif.gif"),
)
# Returns gif_bytes since it detected ImageCollection
display(Image(data=r["gif_bytes"]))
print("ImageCollection auto-delegated to generate_map_chart_gif()")

# 8f. Stacked layout (map above chart)
r = tl.generate_map_chart(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_geometry=True, geometry_outline_color="white",
    chart_type="donut",
    title="Stacked Layout",
    layout="stacked",
    chart_scale=30,
    output_path=os.path.join(output_dir, "08f_map_chart_stacked.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Stacked layout (map above, chart below)")

9. Theme & Font Options

# 9a. Light theme
r = tl.generate_thumbs(
    lcms_single, study_area,
    bg_color="#d6d1ca",
    basemap="esri-topo", burn_in_legend=True,
    scalebar=True, north_arrow=True,
    output_path=os.path.join(output_dir, "09a_light_theme.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Light theme (bg_color='#d6d1ca')")

# 9b. Large fonts
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    title_font_size=24, label_font_size=16,
    burn_in_geometry=True, geometry_outline_color="white",
    output_path=os.path.join(output_dir, "09b_large_fonts.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Large fonts (title=24, label=16)")

# 9c. Small fonts
r = tl.generate_thumbs(
    lcms_single, study_area,
    basemap="esri-satellite",
    burn_in_legend=True, scalebar=True, north_arrow=True,
    title_font_size=12, label_font_size=8,
    burn_in_geometry=True, geometry_outline_color="white",
    output_path=os.path.join(output_dir, "09c_small_fonts.png"),
)
display(Image(data=r["thumb_bytes"]))
print("Small fonts (title=12, label=8)")

# 9d-e. Legend scale options
for scale_val in [1.5, 0.6]:
    r = tl.generate_thumbs(
        lcms_single, study_area,
        basemap="esri-satellite",
        burn_in_legend=True, legend_scale=scale_val,
        scalebar=True, north_arrow=True,
        output_path=os.path.join(output_dir, f"09{'d' if scale_val > 1 else 'e'}_legend_scale_{str(scale_val).replace('.','_')}.png"),
    )
    display(Image(data=r["thumb_bytes"]))
    print(f"Legend scale = {scale_val}")

10. Basemap Presets

Available presets: esri-satellite, esri-topo, esri-street, esri-terrain, esri-natgeo, esri-hillshade, esri-hillshade-dark, esri-ocean, esri-dark-gray, esri-light-gray, usgs-topo, usgs-imagery-topo, usgs-shaded-relief, usgs-hydro, google-satellite, google-hybrid, osm

Underscores work too (e.g. esri_world_imagery resolves to esri-satellite).

basemaps_to_test = ["esri-satellite", "esri-topo", "esri-hillshade", "esri-dark-gray", "usgs-topo"]
for bm in basemaps_to_test:
    r = tl.generate_thumbs(
        None, study_area, basemap=bm,
        scalebar=True, north_arrow=True,
        burn_in_geometry=True, geometry_outline_color="red", geometry_fill_color="FF000022",
        output_path=os.path.join(output_dir, f"10_{bm.replace('-','_')}.png"),
    )
    display(Image(data=r["thumb_bytes"]))
    print(f"Basemap: {bm}")

Summary

Function	Output	Legend	Basemap	Inset	Geometry	CRS
generate_thumbs()	Single PNG or multi-feature grid	Thematic + continuous	Yes	On-map or off-map	burn_in, clip, outline/fill color	Any EPSG
generate_gif()	Animated GIF	Thematic + continuous	Yes	On all frames	burn_in, clip, outline/fill color	Any EPSG
generate_filmstrip()	Grid PNG of dated frames	Thematic + continuous	Yes	Beside legend (1 row) or at row 2	burn_in, clip, outline/fill color	Any EPSG
generate_map_chart()	Map + chart PNG (Image) or GIF (IC)	On thumb only (de-duped)	Yes	Yes	burn_in, points for scatter	Any EPSG
generate_map_chart_gif()	Map + chart animated GIF	Horizontal below	Yes	Yes	burn_in, clip, outline/fill color	Any EPSG

Common Parameters

Parameter	Description	Default
basemap	Preset name or ArcGIS MapServer URL	None
crs	Output projection EPSG code	EPSG:3857
burn_in_geometry	Paint study area boundary on frames	False
geometry_outline_color	Boundary line color (name or hex)	auto-detected
geometry_fill_color	Boundary fill (hex with alpha, e.g. FF000033)	None
clip_to_geometry	Mask data outside boundary	True
inset_map / inset_on_map	Overview locator map	True / False
scalebar / north_arrow	Cartographic elements	True
burn_in_legend / legend_scale	Legend panel	True / 1.0
title_font_size / label_font_size	Font sizing	18 / 12
bg_color	Background / theme (black=dark, light hex=light)	None (dark)
gamma	Gamma correction for auto_viz continuous stretch	1.6

Chart Types for generate_map_chart()

chart_type	Input	Description
bar (default)	ee.Image	Horizontal bar chart of class areas
stacked_bar	ee.Image	Stacked bar (single or grouped by feature)
donut	ee.Image (thematic only)	Donut chart, one per feature for multi-FC
scatter	ee.Image + ee.FeatureCollection	Scatter of 2 bands, optionally coloured by thematic_band_name
line+markers etc.	ee.ImageCollection	Auto-delegates to generate_map_chart_gif()