site

The Site class — geometry + identity for polygon-first analysis.

Site

The Site class is the fundamental unit of analysis in gee-polygons. It wraps a polygon geometry with temporal metadata, making it easy to extract time-series features from Google Earth Engine.

A Site represents a single spatial unit (e.g., a restoration plot, conservation area, or agricultural field) that you want to analyze over time.

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source

Site

def Site(
    feature:ee.Feature, start_year:Optional[int]=None, site_id:Optional[str]=None
):

A polygon with temporal context for GEE analysis.

The Site class wraps an ee.Feature with convenient accessors for common properties like start_year and area, plus helper methods for temporal analysis.

Loading Sites

Helper functions to load sites from common formats.

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source

load_sites

def load_sites(
    path:Union[str, Path], source_crs:Optional[str]=None
)->list[Site]:

Load sites from a GeoJSON file.

Args: path: Path to GeoJSON file source_crs: Override source CRS (auto-detected from file if present)

Returns: List of Site objects

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source

sites_from_geodataframe

def sites_from_geodataframe(
    gdf:gpd.GeoDataFrame, geometry_col:str='geometry'
)->list[Site]:

Create Sites from a GeoDataFrame.

This is the recommended way to work with filtered/sorted site data: 1. Load your GeoJSON into a GeoDataFrame 2. Filter and sort using pandas/geopandas operations 3. Convert to Sites for GEE analysis

Args: gdf: A GeoDataFrame with polygon geometries geometry_col: Name of the geometry column (default ‘geometry’)

Returns: List of Site objects

Example: >>> gdf = gpd.read_file(‘sites.geojson’) >>> # Filter for large sites that started after 2015 >>> filtered = gdf[(gdf[‘area_ha’] > 10) & (gdf[‘start_year’] > 2015)] >>> # Sort by area descending >>> filtered = filtered.sort_values(‘area_ha’, ascending=False) >>> # Convert to Sites >>> sites = sites_from_geodataframe(filtered)

Example Usage

Let’s load some restoration sites and explore a single site.

# Initialize Earth Engine (uncomment when running)
ee.Authenticate()
ee.Initialize(project="hs-brazilreforestation")

# Load sites from GeoJSON
sites = load_sites('../data/restoration_sites_subset.geojson')
print(f"Loaded {len(sites)} sites")

Loaded 10 sites

# Explore a single site
site = sites[8]
print(site)
print(f"Start year: {site.start_year}")
print(f"Area: {site.area_ha:.2f} ha")
print(f"Years for analysis: {site.years(end_year=2024)}")

Site(id=9368, start_year=2012)
Start year: 2012
Area: 13.19 ha
Years for analysis: [2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024]

site.geometry

• Polygon (188 vertices)

  • type:Polygon
  • coordinates: List (6 elements)

    • 0: List (188 elements)

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        • 0:-42.2757037865831
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        • 0:-42.27509815906286
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        • 0:-42.27536091306816
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        • 0:-42.27532020600598
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        • 0:-42.27529651304267
        • 1:-22.510710948027214
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        • 0:-42.27527950612079
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        • 0:-42.27518917603976
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        • 0:-42.27521963799877
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        • 0:-42.275253578575516
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        • 0:-42.274695454372996
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      • 153: [-42.27465172345594, -22.511643684951327]

        • 0:-42.27465172345594
        • 1:-22.511643684951327
      • 154: [-42.27456665340917, -22.511913055650734]

        • 0:-42.27456665340917
        • 1:-22.511913055650734
      • 155: [-42.274588122243706, -22.511977457612172]

        • 0:-42.274588122243706
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        • 0:-42.274631049466954
        • 1:-22.511998926182862
      • 157: [-42.27480278743671, -22.511891592470047]

        • 0:-42.27480278743671
        • 1:-22.511891592470047
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        • 0:-42.2749101183695
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        • 0:-42.27489478174481
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        • 0:-42.27478132253972
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        • 0:-42.27478132200278
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        • 0:-42.27478537906706
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        • 0:-42.274792134639455
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        • 0:-42.274796023960675
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        • 0:-42.274781322133435
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        • 0:-42.27478132124345
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        • 0:-42.274781316903095
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        • 0:-42.27480278862771
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        • 0:-42.27469545813433
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        • 0:-42.27460958587823
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      • 171: [-42.274588119620184, -22.513179572933268]

        • 0:-42.274588119620184
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      • 172: [-42.27460958324118, -22.51322250651747]

        • 0:-42.27460958324118
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      • 173: [-42.274759856672944, -22.51335130468111]

        • 0:-42.274759856672944
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      • 174: [-42.27476801791958, -22.513359466751375]

        • 0:-42.27476801791958
        • 1:-22.513359466751375
      • 175: [-42.274781323699514, -22.513372771814254]

        • 0:-42.274781323699514
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      • 176: [-42.274738383409606, -22.513501566660718]

        • 0:-42.274738383409606
        • 1:-22.513501566660718
      • 177: [-42.27473838371716, -22.513630368318474]

        • 0:-42.27473838371716
        • 1:-22.513630368318474
      • 178: [-42.274867179899275, -22.513737699105203]

        • 0:-42.274867179899275
        • 1:-22.513737699105203
      • 179: [-42.27493158135024, -22.513737705511794]

        • 0:-42.27493158135024
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      • 180: [-42.2751247797216, -22.51378062858033]

        • 0:-42.2751247797216
        • 1:-22.51378062858033
      • 181: [-42.275060385111374, -22.513930896851722]

        • 0:-42.275060385111374
        • 1:-22.513930896851722
      • 182: [-42.27506038597645, -22.513995292737828]

        • 0:-42.27506038597645
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      • 183: [-42.27506037925776, -22.514038231930602]

        • 0:-42.27506037925776
        • 1:-22.514038231930602
      • 184: [-42.27491011799394, -22.51429582561586]

        • 0:-42.27491011799394
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      • 185: [-42.27507370965564, -22.514405669996638]

        • 0:-42.27507370965564
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      • 186: [-42.275275043584585, -22.51431729322403]

        • 0:-42.275275043584585
        • 1:-22.51431729322403
      • 187: [-42.275210643251626, -22.514167030511306]

        • 0:-42.275210643251626
        • 1:-22.514167030511306
    • 1: List (4 elements)

      • 0: [-42.275511172239035, -22.513823562640546]

        • 0:-42.275511172239035
        • 1:-22.513823562640546
      • 1: [-42.27561850275632, -22.5136303685588]

        • 0:-42.27561850275632
        • 1:-22.5136303685588
      • 2: [-42.27570437568856, -22.513480103416555]

        • 0:-42.27570437568856
        • 1:-22.513480103416555
      • 3: [-42.275511172239035, -22.513823562640546]

        • 0:-42.275511172239035
        • 1:-22.513823562640546
    • 2: List (4 elements)

      • 0: [-42.276885022286095, -22.511913064626548]

        • 0:-42.276885022286095
        • 1:-22.511913064626548
      • 1: [-42.277056752651134, -22.511848667586033]

        • 0:-42.277056752651134
        • 1:-22.511848667586033
      • 2: [-42.276713288755836, -22.51199892883776]

        • 0:-42.276713288755836
        • 1:-22.51199892883776
      • 3: [-42.276885022286095, -22.511913064626548]

        • 0:-42.276885022286095
        • 1:-22.511913064626548
    • 3: List (4 elements)

      • 0: [-42.276970886536674, -22.509251235728623]

        • 0:-42.276970886536674
        • 1:-22.509251235728623
      • 1: [-42.27688502296862, -22.509208307867446]

        • 0:-42.27688502296862
        • 1:-22.509208307867446
      • 2: [-42.27697228974889, -22.50924001483783]

        • 0:-42.27697228974889
        • 1:-22.50924001483783
      • 3: [-42.276970886536674, -22.509251235728623]

        • 0:-42.276970886536674
        • 1:-22.509251235728623
    • 4: List (4 elements)

      • 0: [-42.275259678303726, -22.51343725784634]

        • 0:-42.275259678303726
        • 1:-22.51343725784634
      • 1: [-42.27519838810252, -22.513608878265188]

        • 0:-42.27519838810252
        • 1:-22.513608878265188
      • 2: [-42.2751247797216, -22.51378062858033]

        • 0:-42.2751247797216
        • 1:-22.51378062858033
      • 3: [-42.275259678303726, -22.51343725784634]

        • 0:-42.275259678303726
        • 1:-22.51343725784634
    • 5: List (4 elements)

      • 0: [-42.27527503988412, -22.513394243721432]

        • 0:-42.27527503988412
        • 1:-22.513394243721432
      • 1: [-42.27536091311331, -22.513179572966447]

        • 0:-42.27536091311331
        • 1:-22.513179572966447
      • 2: [-42.275259678303726, -22.51343725784634]

        • 0:-42.275259678303726
        • 1:-22.51343725784634
      • 3: [-42.27527503988412, -22.513394243721432]

        • 0:-42.27527503988412
        • 1:-22.513394243721432

# Get buffered geometry for context
buffer_1km = site.buffer(1000)
print(f"Buffered geometry created")

Buffered geometry created

Loading Sites from GeoDataFrame

For workflows where you need to filter or sort sites before analysis, it’s more efficient to: 1. Load the GeoJSON into a GeoDataFrame 2. Filter and sort using pandas/geopandas operations (fast, in-memory) 3. Convert only the sites you need to Site objects

This avoids creating Earth Engine features for sites you’ll filter out anyway.

# Load GeoJSON into GeoDataFrame for fast filtering
gdf = gpd.read_file('../data/restoration_sites_subset.geojson')
print(f"Loaded {len(gdf)} sites into GeoDataFrame")
print(f"CRS: {gdf.crs}")
gdf.head()

Loaded 10 sites into GeoDataFrame
CRS: EPSG:5880

	category	source	has_project	project_name	executing_institution	managing_institution	financing_institution	main_method	combined_method	strategy	...	project_total_area_ha	project_mean_site_area_ha	project_mean_dispersion_km	project_max_dispersion_km	centroid_lon	centroid_lat	perimeter_m	shape_complexity	rid	geometry
0	restauracao	Aliança pela Restauração na Amazônia	Sim	Programa de recuperacao de areas degradadas - ...	Onca Puma Mineracao	nao identificado	nao identificado	enriquecimento	controle das plantas competidoras	Regeneração Natural com Manejo	...	86.704527	28.901509	2.699984e+04	4.030311e+04	5.322801e+06	9.280290e+06	2713.351189	4.455536	3107	POLYGON Z ((5322649.663 9279737.977 0, 5322649...
1	restauracao	Pacto pela Restauração da Mata Atlântica	Sim	Mata Atlantica, das encostas as areas alagadas...	SPVS	nao identificado	FUNBIO,MMA,KFW	enriquecimento	nao identificado	Regeneração Natural com Manejo	...	651.186341	10.853106	6.590103e+05	2.725318e+06	5.525798e+06	7.183378e+06	3136.600149	6.355574	3153	POLYGON Z ((5525444.601 7183290.446 0, 5525444...
2	restauracao	Pacto pela Restauração da Mata Atlântica	Sim	nao identificado	nao identificado	SEMIL-SP	nao identificado	adensamento	nao identificado	Regeneração Natural com Manejo	...	228.300262	5.435721	2.400005e+07	4.269652e+07	5.791646e+06	7.431742e+06	6347.796596	9.286339	3226	POLYGON Z ((5790809.752 7431272.341 0, 5790776...
3	restauracao	Pacto pela Restauração da Mata Atlântica	Sim	nao identificado	nao identificado	SEMIL-SP	nao identificado	adensamento	nao identificado	Regeneração Natural com Manejo	...	104.184078	14.883440	1.902321e+07	2.225268e+07	5.187825e+06	7.531541e+06	7300.762070	12.221759	4210	POLYGON Z ((5187930.677 7531850.711 0, 5187971...
4	restauracao	Pacto pela Restauração da Mata Atlântica	Sim	nao identificado	nao identificado	SEMIL-SP	nao identificado	plantio de mudas	nao identificado	Plantio em Área Total	...	204.103345	2.685570	2.713580e+07	4.506196e+07	5.619900e+06	7.654089e+06	2602.480123	7.461996	5461	POLYGON Z ((5620178.276 7654188.507 0, 5620178...

5 rows × 36 columns

# Filter for sites larger than 20 hectares
filtered = gdf[gdf['area_ha'] > 20].copy()
print(f"Filtered to {len(filtered)} sites with area > 10 ha")

# Sort by area (largest first)
filtered = filtered.sort_values('area_ha', ascending=False)

# Convert filtered GeoDataFrame to Sites
sites = sites_from_geodataframe(filtered)
print(f"Created {len(sites)} Site objects")

# Check the first site
print(sites[0])

Filtered to 5 sites with area > 10 ha
Created 5 Site objects
Site(id=3226, start_year=2021)

# You can also create a single Site from a specific row
row = gdf.iloc[0]  # Get first row
single_site = Site.from_geodataframe_row(row, source_crs=str(gdf.crs))
print(f"Single site: {single_site}")

Single site: Site(id=3107, start_year=2016)

Visualizing Sites

Now that we have a site loaded, we’d like to see it on a map. Let’s add a show() method using geemap’s interactive mapping.

---

source

Site.show

def show(
    zoom:int=14, basemap:str='SATELLITE', color:str='blue', fill_color:str='#0000ff33'
)->geemap.Map:

Display the site on an interactive map.

Args: zoom: Initial zoom level (default 14) basemap: Basemap type - ‘SATELLITE’, ‘ROADMAP’, ‘TERRAIN’, ‘HYBRID’ color: Outline color (default ‘blue’) fill_color: Fill color with alpha (default semi-transparent blue)

Returns: A geemap.Map object centered on the site

# View the site on a satellite map
site.show()