Scene

Back to Scenes

The Scene class holds metadata about a single scene in the Descartes Labs catalog.

Example

>>> import descarteslabs as dl
>>> scene, ctx = dl.scenes.Scene.from_id("landsat:LC08:PRE:TOAR:meta_LC80270312016188_v1")  # doctest: +SKIP
>>> ctx  # a default GeoContext to use when loading raster data from this Scene  # doctest: +SKIP
AOI(geometry=None,
    resolution=15.0,
    crs='EPSG:32615',
    align_pixels=False,
    bounds=(258292.5, 4503907.5, 493732.5, 4743307.5),
    bounds_crs='EPSG:32615',
    shape=None)
>>> scene.properties.id  # doctest: +SKIP
'landsat:LC08:PRE:TOAR:meta_LC80270312016188_v1'
>>> scene.properties.date  # doctest: +SKIP
datetime.datetime(2016, 7, 6, 16, 59, 42, 753476)
>>> scene.properties.bands.red.resolution  # doctest: +SKIP
15
>>> arr = scene.ndarray("red green blue", ctx)  # doctest: +SKIP
>>> type(arr)  # doctest: +SKIP
<class 'numpy.ma.core.MaskedArray'>
>>> arr.shape  # doctest: +SKIP
(3, 15960, 15696)
class Scene(scene_dict, bands_dict)[source]

Object holding metadata about a single scene in the Descartes Labs catalog.

A Scene is structured like a GeoJSON Feature, with geometry and properties.

geometry

The region the scene’s data covers, in WGS84 (lat-lon) coordinates, represented as a Shapely polygon.

Type:shapely.geometry.Polygon
properties

Metadata about the scene. Some fields will vary between products, but these will be present:

  • id : str

    Descartes Labs ID of this scene

  • crs : str

    Native coordinate reference system of the scene, as an EPSG code or PROJ.4 definition

  • date : datetime.datetime

    'acquired' date parsed as a Python datetime if set, otherwise None

  • bands : DotDict[str, DotDict]

    Metadata about the bands available in the scene, as the mapping {band name: band metadata}.

    Band names are either the band’s name field (like “red”), or for derived bands, the band’s id (like “derived:ndvi”).

    Each band metadata dict should contain these fields:

    • id : str
      Descartes Labs ID of the band; unique to every band of every product
    • name : str
      Human-readable name of the band
    • dtype : str
      Native type in which the band’s data is stored
    • data_range : list
      List of [min, max] values the band’s data can have

    These fields are useful and available in most products, but may not always be available:

    • resolution : float
      Native resolution of the band, in resolution_unit, that the edge of each pixel represents on the ground
    • resolution_unit : str
      Units of resolution field, such as "m"
    • physical_range : list
      [min, max] range of values the band’s data represents. Values of data have physical meaning (such as a reflectance fraction from 0-1), but often those values are remapped to a different numerical range for more efficient storage (since fixed-point integers require less space than floats). To return data to numbers with physical meaning, they should be mapped from data_range to physical_range.
    • wavelength_min
      Minimum wavelength captured by the sensor in this band
    • wavelength_center
      Central wavelength captured by the sensor in this band
    • wavelength_max
      Maximum wavelength captured by the sensor in this band
    • wavelength_unit
      Units of the wavelength fields, such as "nm"
Type:DotDict

__init__ instantiates a Scene from a dict returned by Metadata.search and Metadata.get_bands_by_id.

It’s preferred to use Scene.from_id or scenes.search instead.

coverage(geom)[source]

The fraction of a geometry-like object covered by this Scene’s geometry.

Parameters:geom (GeoJSON-like dict, GeoContext, or object with __geo_interface__) – Geometry to which to compare this Scene’s geometry
Returns:coverage – The fraction of geom’s area that overlaps with this Scene, between 0 and 1.
Return type:float

Example

>>> import descarteslabs as dl
>>> scene, ctx = dl.scenes.Scene.from_id("landsat:LC08:PRE:TOAR:meta_LC80270312016188_v1")  # doctest: +SKIP
>>> scene.coverage(scene.geometry.buffer(1))  # doctest: +SKIP
0.8
default_ctx()[source]

Return an AOI GeoContext for loading this Scene’s original, unwarped data.

These defaults are used:

  • resolution: resolution determined from the Scene’s geotrans
  • crs: native CRS of the Scene (often, a UTM CRS)
  • bounds: bounds determined from the Scene’s geotrans and raster_size
  • bounds_crs: native CRS of the Scene
  • align_pixels: False, to prevent interpolation snapping pixels to a new grid
  • geometry: None

Note

Using this GeoContext will only return original, unwarped data if the Scene is axis-aligned (“north-up”) within the CRS. If its geotrans applies a rotation, a warning will be raised. In that case, use Raster.ndarray or Raster.raster to retrieve original data. (The GeoContext paradigm requires bounds for consistentcy, which are inherently axis-aligned.)

Returns:ctx
Return type:AOI
download(bands, ctx, dest=None, format='tif', resampler='near', processing_level=None, scaling=None, data_type=None, raster_client=None)[source]

Save bands from this scene as a GeoTIFF, PNG, or JPEG, writing to a path or file-like object.

Parameters:
  • bands (str or Sequence[str]) – Band names to load. Can be a single string of band names separated by spaces ("red green blue derived:ndvi"), or a sequence of band names (["red", "green", "blue", "derived:ndvi"]). Names must be keys in self.properties.bands.
  • ctx (GeoContext) – A GeoContext to use when loading this Scene
  • dest (str, path-like object, or file-like object, default None) –

    Where to write the image file.

    • If None (default), it’s written to an image file of the given format in the current directory, named by the Scene’s ID and requested bands, like "sentinel-2:L1C:2018-08-10_10TGK_68_S2A_v1-red-green-blue.tif"
    • If a string or path-like object, it’s written to that path.

      Any file already existing at that path will be overwritten.

      Any intermediate directories will be created if they don’t exist.

      Note that path-like objects (such as pathlib.Path) are only supported in Python 3.6 or later.

    • If a file-like object, it’s written into that file.
  • format (str, default "tif") –

    If a file-like object or None is given as dest: one of “tif”, “png”, or “jpg”.

    If a str or path-like object is given as dest, format is ignored and determined from the extension on the path (one of “.tif”, “.png”, or “.jpg”).

  • resampler (str, default "near") – Algorithm used to interpolate pixel values when scaling and transforming the image to its new resolution or SRS. Possible values are near (nearest-neighbor), bilinear, cubic, cubicsplice, lanczos, average, mode, max, min, med, q1, q3.
  • processing_level (str, optional) – How the processing level of the underlying data should be adjusted. Possible values are toa (top of atmosphere) and surface. For products that support it, surface applies Descartes Labs’ general surface reflectance algorithm to the output.
  • scaling (None, str, list, dict) – Band scaling specification. Please see scaling_parameters() for a full description of this parameter.
  • data_type (None, str) – Output data type. Please see scaling_parameters() for a full description of this parameter.
  • raster_client (Raster, optional) – Unneeded in general use; lets you use a specific client instance with non-default auth and parameters.
Returns:

path – If dest is None or a path, the path where the image file was written is returned. If dest is file-like, nothing is returned.

Return type:

str or None

Example

>>> import descarteslabs as dl
>>> scene, ctx = dl.scenes.Scene.from_id("landsat:LC08:PRE:TOAR:meta_LC80270312016188_v1")  # doctest: +SKIP
>>> scene.download("red green blue", ctx)  # doctest: +SKIP
"landsat:LC08:PRE:TOAR:meta_LC80270312016188_v1_red-green-blue.tif"
>>> import os
>>> os.listdir(".")  # doctest: +SKIP
["landsat:LC08:PRE:TOAR:meta_LC80270312016188_v1_red-green-blue.tif"]
>>> scene.download(
...     "nir swir1",
...     ctx,
...     "rasters/{ctx.resolution}-{scene.properties.id}.jpg".format(ctx=ctx, scene=scene)
... )  # doctest: +SKIP
"rasters/15-landsat:LC08:PRE:TOAR:meta_LC80270312016188_v1.tif"
>>> with open("another_raster.jpg", "wb") as f:
...     scene.download("swir2", ctx, dest=f, format="jpg")  # doctest: +SKIP
Raises:
  • ValueError – If requested bands are unavailable. If band names are not given or are invalid. If the requested bands have incompatible dtypes. If format is invalid, or the path has an invalid extension.
  • NotFoundError – If a Scene’s ID cannot be found in the Descartes Labs catalog
  • BadRequestError – If the Descartes Labs platform is given invalid parameters
classmethod from_id(scene_id, metadata_client=None)[source]

Return the metadata for a Descartes Labs scene ID as a Scene object.

Also returns a GeoContext for loading the Scene’s original, unwarped data.

Parameters:
  • scene_id (str) – Descartes Labs scene ID, e.g. “landsat:LC08:PRE:TOAR:meta_LC80270312016188_v1”
  • metadata_client (Metadata, optional) – Unneeded in general use; lets you use a specific client instance with non-default auth and parameters.
Returns:

  • scene (Scene) – Scene instance with metadata loaded from the Descartes Labs catalog
  • ctx (AOI) – A GeoContext for loading this Scene’s original data. The defaults used are described in Scene.default_ctx.

Example

>>> import descarteslabs as dl
>>> scene, ctx = dl.scenes.Scene.from_id("landsat:LC08:PRE:TOAR:meta_LC80260322016197_v1")  # doctest: +SKIP
>>> ctx  # doctest: +SKIP
AOI(geometry=None,
    resolution=15.0,
    crs='EPSG:32615',
    align_pixels=False,
    bounds=(348592.5, 4345567.5, 581632.5, 4582807.5),
    bounds_crs='EPSG:32615',
    shape=None)
>>> scene.properties.date  # doctest: +SKIP
datetime.datetime(2016, 7, 15, 16, 53, 59, 495435)
Raises:NotFoundError – If the scene_id cannot be found in the Descartes Labs catalog
ndarray(bands, ctx, mask_nodata=True, mask_alpha=None, bands_axis=0, raster_info=False, resampler='near', processing_level=None, scaling=None, data_type=None, raster_client=None)[source]

Load bands from this scene as an ndarray, optionally masking invalid data.

If the selected bands have different data types the resulting ndarray has the most general of those data types. This table defines which data types can be cast to which more general data types:

  • Byte to: UInt16, UInt32, Int16, Int32, Float32, Float64
  • UInt16 to: UInt32, Int32, Float32, Float64
  • UInt32 to: Float64
  • Int16 to: Int32, Float32, Float64
  • Int32 to: Float32, Float64
  • Float32 to: Float64
  • Float64 to: No possible casts
Parameters:
  • bands (str or Sequence[str]) – Band names to load. Can be a single string of band names separated by spaces ("red green blue derived:ndvi"), or a sequence of band names (["red", "green", "blue", "derived:ndvi"]). Names must be keys in self.properties.bands. If the alpha band is requested, it must be last in the list to reduce rasterization errors.
  • ctx (GeoContext) – A GeoContext to use when loading this Scene
  • mask_nodata (bool, default True) – Whether to mask out values in each band that equal that band’s nodata sentinel value.
  • mask_alpha (bool or str or None, default None) – Whether to mask pixels in all bands where the alpha band of all scenes is 0. Provide a string to use an alternate band name for masking. If the alpha band is available for all scenes in the collection and mask_alpha is None, mask_alpha is set to True. If not, mask_alpha is set to False.
  • bands_axis (int, default 0) –

    Axis along which bands should be located in the returned array. If 0, the array will have shape (band, y, x), if -1, it will have shape (y, x, band).

    It’s usually easier to work with bands as the outermost axis, but when working with large arrays, or with many arrays concatenated together, NumPy operations aggregating each xy point across bands can be slightly faster with bands as the innermost axis.

  • raster_info (bool, default False) – Whether to also return a dict of information about the rasterization of the scene, including the coordinate system WKT and geotransform matrix. Generally only useful if you plan to upload data derived from this scene back to the Descartes catalog, or use it with GDAL.
  • resampler (str, default "near") – Algorithm used to interpolate pixel values when scaling and transforming the image to its new resolution or CRS. Possible values are near (nearest-neighbor), bilinear, cubic, cubicsplice, lanczos, average, mode, max, min, med, q1, q3.
  • processing_level (str, optional) – How the processing level of the underlying data should be adjusted. Possible values are toa (top of atmosphere) and surface. For products that support it, surface applies Descartes Labs’ general surface reflectance algorithm to the output.
  • scaling (None, str, list, dict) – Band scaling specification. Please see scaling_parameters() for a full description of this parameter.
  • data_type (None, str) – Output data type. Please see scaling_parameters() for a full description of this parameter.
  • raster_client (Raster, optional) – Unneeded in general use; lets you use a specific client instance with non-default auth and parameters.
Returns:

  • arr (ndarray) – Returned array’s shape will be (band, y, x) if bands_axis is 0, (y, x, band) if bands_axis is -1. If mask_nodata or mask_alpha is True, arr will be a masked array. The data type (“dtype”) of the array is the most general of the data types among the bands being rastered.
  • raster_info (dict) – If raster_info=True, a raster information dict is also returned.

Example

>>> import descarteslabs as dl
>>> scene, ctx = dl.scenes.Scene.from_id("landsat:LC08:PRE:TOAR:meta_LC80270312016188_v1")  # doctest: +SKIP
>>> arr = scene.ndarray("red green blue", ctx)  # doctest: +SKIP
>>> type(arr)  # doctest: +SKIP
<class 'numpy.ma.core.MaskedArray'>
>>> arr.shape  # doctest: +SKIP
(3, 15960, 15696)
>>> red_band = arr[0]  # doctest: +SKIP
Raises:
  • ValueError – If requested bands are unavailable. If band names are not given or are invalid. If the requested bands have incompatible dtypes.
  • NotFoundError – If a Scene’s ID cannot be found in the Descartes Labs catalog
  • BadRequestError – If the Descartes Labs platform is given invalid parameters
scaling_parameters(bands, scaling=None, data_type=None)[source]

Computes fully defaulted scaling parameters and output data_type from provided specifications.

This method makes accessible the scales and data_type parameters which will be generated and passed to the Raster API by methods such as ndarray() and download(). It is provided as a convenience to the user to aid in understanding how the scaling and data_type parameters will be handled by those methods. It would not usually be used in a normal workflow.

Parameters:
  • bands (list) – List of bands to be scaled.
  • scaling (None or str or list or dict, default None) – Supplied scaling specification, see below.
  • data_type (None or str, default None) – Result data type desired, as a standard data type string (e.g. "Byte", "Uint16", or "Float64"). If not specified, will be deduced from the scaling specification. Typically this is left unset and the appropriate type will be determined automatically.
Returns:

  • scales (list(tuple)) – The fully specified scaling parameter, compatible with the Raster API and the output data type.
  • data_type (str) – The result data type as a standard GDAL type string.

Raises:

ValueError – If any invalid or incompatible value is passed to any of the three parameters.

Scaling is determined on a band-by-band basis, incorporating the user provided specification, the output data_type, and properties for the band, such as the band type, the band data type, and the default_range, data_range, and physical_range properties. Ultimately the scaling for each band will be resolved to either None or a tuple of numeric values of length 0, 2, or 4, as accepted by the Raster API. The result is a list (with length equal to the number of bands) of one of these values, or may be a None value which is just a shorthand equivalent for a list of None values.

A None indicates that no scaling should be performed.

A 0-tuple () indicates that the band data should be automatically scaled from the minimum and maximum values present in the image data to the display range 0-255.

A 2-tuple (input-min, input-max) indicates that the band data should be scaled from the specified input range to the display range of 0-255.

A 4-tuple (input-min, input-max, output-min, output-max) indicates that the band data should be scaled from the input range to the output range.

In all cases, the scaling will be performed as a multiply and add, and the resulting values are only clipped as necessary to fit in the output data type. As such, if the input and output ranges are the same, it is effectively a no-op equivalent to None.

The support for scaling parameters in the Scenes API includes the concept of an automated scaling mode. The four supported modes are as follows.

"raw":
Equivalent to a None, the data should not be scaled.
"auto":
Equivalent to a 0-tuple, the data should be scaled by the Raster service so that the actual range of data in the input is scaled up to the full display range (0-255). It is not possible to determine the bounds of this input range in the Scenes API as the actual band data is not accessible.
"display":
The data should be scaled from any specified input bounds, defaulting to the default_range property for the band, to the output range, defaulting to 0-255.
"physical":
The data should be scaled from the input range, defaulting to the data_range property for the band, to the output range, defaulting to the physical_range property for the band.

The mode may be explicitly specified, or it may be determined implicitly from other characteristics such as the length and contents of the tuples for each band, or from the output data_type if this is explicitly specified (e.g. "Byte" implies display mode, "Float64" implies physical mode).

If it is not possible to infer the mode, and a mode is required in order to fully determine the results of this method, an error will be raised. It is also an error to explicitly specify more than one mode, with several exceptions: auto and display mode are compatible, while a raw display mode for a band which is of type “mask” or type “class” does not conflict with any other mode specification.

Normally the data_type parameter is not provided by the user, and is instead determined from the mode as follows.

"raw":
The data type that best matches the data types of all the bands, preserving the precision and range of the original data.
"auto" and "display":
"Byte"
"physical":
"Float64"

The scaling parameter passed to this method can be any of the following:

None:
No scaling for all bands. Equivalent to [None, ...].
str:
Any of the four supported automatic modes as described above.
list or Iterable:
A list or similar iterable must contain a number of elements equal to the number of bands specified. Each element must either be a None, a 0-, 2-, or 4-tuple, or one of the above four automatic mode strings. The elements of each tuple must either be a numeric value or a string containing a valid numerical string followed by a “%” character. The latter will be interpreted as a percentage of the appropriate range (e.g. default_range, data_range, or physical_range) according to the mode. For example, a tuple of ("25%", "75%") with a default_range of [0, 4000] will yield (1000, 3000).
dict or Mapping:
A dictionary or similar mapping with keys corresponding to band names and values as accepted as elements for each band as with a list described above. Each band name is used to lookup a value in the mapping. If none is found, and the band is not of type “mask” or “class”, then the special key "default_" is looked up in the mapping if it exists. Otherwise a value of None will be used for the band. This is strictly a convenience for constructing a list of scale values, one for each band, but can be useful if a single general-purpose mapping is defined for all possible or relevant bands and then reused across many calls to the different methods in the Scenes API which accept a scaling parameter.

See also

Scenes Guide : This contains many examples of the use of the scaling and data_type parameters.