Datasets

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Overview

This section provides a quick overview of the API for listing and accessing datasets.

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Related Guides

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Dataset types

Each dataset is of a specific type. Each dataset type comes with a set of required fields for each data point. The dataset type also determines the query capabilities for a dataset, e.g. whether a dataset supports time-based queries or additionally also spatially filtered queries.

To find out which fields are required for each dataset type check out the documentation for the available dataset types below.

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Dataset specific fields

Additionally, each dataset has a set of fields that are specific to that dataset. Fields are defined during dataset creation. That way, all data points in a dataset are strongly typed and are validated during ingestion. The required fields of the dataset type, as well as the custom fields specific to each dataset together make up the dataset schema.

Once a dataset schema is defined, existing fields cannot be removed or edited as soon as data has been ingested into it. However, you can always add new fields to a dataset, since all fields are always optional.

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Field types

When defining the data schema, you can specify the type of each field. The following field types are supported.

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Primitives

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Time

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Identifier

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Geospatial

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Arrays

Every type is also available as an array, allowing to ingest multiple values of the underlying type for each data point. The size of the array is flexible, and can be different for each data point.

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Creating a dataset

You can create a dataset in Tilebox using the Tilebox Console. Check out the Creating a dataset guide for an example of how to achieve this.

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Listing datasets

You can use your client instance to access the datasets available to you. To list all available datasets, use the datasets method of the client.

from tilebox.datasets import Client

client = Client()
datasets = client.datasets()
print(datasets)

open_data:
    asf:
        ers_sar: European Remote Sensing Satellite (ERS) Synthetic Aperture Radar (SAR) Granules
    copernicus:
        landsat8_oli_tirs: Landsat-8 is part of the long-running Landsat programme ...
        sentinel1_sar: The Sentinel-1 mission is the European Radar Observatory for the ...
        sentinel2_msi: Sentinel-2 is equipped with an optical instrument payload that samples ...
        sentinel3_olci: OLCI (Ocean and Land Colour Instrument) is an optical instrument used to ...
        ...

Once you have your dataset object, you can use it to list the available collections for the dataset.

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Accessing a dataset

Each dataset has an automatically generated code name that can be used to access it. The code name is the name of the group, followed by a dot, followed by the dataset name. For example, the code name for the Sentinel-2 MSI dataset above, which is part of the open_data.copernicus group, the code name is open_data.copernicus.sentinel2_msi.

To access a dataset, use the dataset method of your client instance and pass the code name of the dataset as an argument.

from tilebox.datasets import Client

client = Client()
s2_msi_dataset = client.dataset("open_data.copernicus.sentinel2_msi")

Once you have your dataset object, you can use it to access available collections for the dataset.