Check out the best practices for handling geometries to learn more about the different aspects to consider when working with geometries, including antimeridian crossings and pole coverings.
When querying, you can specify arbitrary geometries as an area of interest. Tilebox currently supports Polygon and MultiPolygon geometries as query filters.

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Filtering by Area of Interest

To filter by an area of interest, use a Polygon or MultiPolygon geometry as the spatial extent parameter. Here is how to query Sentinel-2 S2A_S2MSI2A data over Colorado for a specific day in April 2025. 
from shapely import Polygon
from tilebox.datasets import Client

area = Polygon(  # area roughly covering the state of Colorado
    ((-109.05, 41.00), (-109.045, 37.0), (-102.05, 37.0), (-102.05, 41.00), (-109.05, 41.00)),
)

client = Client()
sentinel2_msi = client.dataset("open_data.copernicus.sentinel2_msi")
collection = sentinel2_msi.collection("S2A_S2MSI2A")
data = collection.query(
    temporal_extent=("2025-04-02", "2025-04-03"),
    spatial_extent=area,
)

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Intersection mode

By default, queries return all datapoints that intersect with the specified geometry. You can alter this behavior to return only datapoints fully contained within the geometry. Tilebox supports this by allowing you to specify a mode for the spatial filter.
Query results with intersects modeQuery results with intersects mode

mode: intersects

Query results with contains modeQuery results with contains mode

mode: contains

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Intersects

The intersects mode is the default for spatial queries. It matches all datapoints whose geometries intersect with the query geometry. 
area = Polygon(  # area roughly covering the state of Colorado
    ((-109.05, 41.00), (-109.045, 37.0), (-102.05, 37.0), (-102.05, 41.00), (-109.05, 41.00)),
)

data = collection.query(
    temporal_extent=("2025-04-02", "2025-04-03"),
    # intersects is the default, so can also be omitted entirely
    spatial_extent={"geometry": area, "mode": "intersects"},
)
print(f"There are {data.sizes['time']} Sentinel-2A granules intersecting the area of Colorado on April 2nd, 2025")
Output
There are 27 Sentinel-2A granules intersecting the area of Colorado on April 2nd, 2025

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Contains

The contains mode matches all datapoints whose geometries are fully contained within the query geometry. 
area = Polygon(  # area roughly covering the state of Colorado
    ((-109.05, 41.00), (-109.045, 37.0), (-102.05, 37.0), (-102.05, 41.00), (-109.05, 41.00)),
)

data = collection.query(
    temporal_extent=("2025-04-01", "2025-05-02"),
    spatial_extent={"geometry": area, "mode": "contains"},
)
print(f"There are {data.sizes['time']} Sentinel-2A granules fully contained within the area of Colorado on April 2nd, 2025")
Output
There are 16 Sentinel-2A granules fully contained within the area of Colorado on April 2nd, 2025

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Antimeridian Crossings

In many applications, geometries that cross the antimeridian cause issues. Since such geometries are common in satellite data, Tilebox does take extra care to handle them out of the box correctly, by building the necessary internal spatial index structures in a way that correctly handles antimeridian crossings and pole coverings. To get accurate results also at query time, it’s recommend to use the spherical coordinate reference system for querying (which is the default), as it correctly handles the non-linearity introduced by the antimeridian in cartesian space.
Even if you stick to the spherical coordinate reference system when querying, it’s still recommended to follow the best practices for handling geometries. In doing so, you can ensure that no geometry related issues will arise even when interfacing with other libraries and tools that may not properly support non-linearities in geometries.

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Coordinate reference system

Geometry intersection and containment checks can either be performed in a 3D spherical coordinate system or in a standard 2D cartesian lat/lon coordinate system.
Spherical coordinate reference systemSpherical coordinate reference system

Spherical coordinate reference system

Cartesian coordinate reference systemCartesian coordinate reference system

Cartesian coordinate reference system

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Spherical

The spherical coordinate reference system is the default and recommended choice. It correctly handles antimeridian crossings and is the most robust option, regardless of how datapoint geometries are cut along the antimeridian.
To learn more about antimeridian crossings and how to handle them correctly, check out the Antimeridian Crossings section above.
When querying with the spherical coordinate reference system, Tilebox automatically converts all geometries to their x, y, z coordinates on the unit sphere and performs the intersection and containment checks in 3D. 
area = Polygon(  # area roughly covering the state of Colorado
    ((-109.05, 41.00), (-109.045, 37.0), (-102.05, 37.0), (-102.05, 41.00), (-109.05, 41.00)),
)

data = collection.query(
    temporal_extent=("2025-04-01", "2025-05-02"),
    # spherical is the default, so can also be omitted entirely
    spatial_extent={"geometry": area, "coordinate_system": "spherical"},
)

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Cartesian

Tilebox can also be configured to use a standard 2D cartesian lat/lon coordinate system for geometry intersection and containment checks. This is done by specifying the cartesian coordinate reference system when querying. 
area = Polygon(  # area roughly covering the state of Colorado
    ((-109.05, 41.00), (-109.045, 37.0), (-102.05, 37.0), (-102.05, 41.00), (-109.05, 41.00)),
)

data = collection.query(
    temporal_extent=("2025-04-01", "2025-05-02"),
    spatial_extent={"geometry": area, "coordinate_system": "cartesian"},
)
When using the cartesian coordinate system, antimeridian crossings may cause issues if datapoint geometries or the query geometry do not properly respect the antimeridian cut. Check out the best practices for handling geometries to learn more about how to avoid such issues.