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This page provides a guide to the BigQuery tables used for training models,
including their schemas and field descriptions. You'll also find a breakdown of
the core concepts—snapshots, assets, and observations—that structure the data,
along with details on how to use the tables where relevant.
snapshots
A snapshot is a fixed, unchangeable copy of a dataset at a specific moment in
time. The snapshot table provides metadata associated with snapshots, to
allow you to understand the temporal state of the data at a high level.
Column name
Type
Description
snapshot_id
STRING
Unique identifier for the snapshot. Used as a key to join tables.
subscription_id
STRING
Unique identifier for the subscription.
creation_time
TIMESTAMP
ISO 8601 formatted timestamp e.g., 2019-09-25 17:26:27.757171 UTC.
all_observations
An "observation" is a sighting of an asset in the real world. Observations
differ from assets in that they contain metadata for the detection of an asset,
i.e., the time the image was captured and the position of the camera.
The all_observations table provides observations from all snapshots. You can
use this table to detect differences in observations between snapshots.
Column name
Type
Description
snapshot_id
STRING
Unique identifier for the snapshot. Used as a key to join tables.
asset_id
STRING
Unique identifier for the asset.
asset_type
STRING
Major classification of the asset e.g., ASSET_CLASS_ROAD_SIGN.
location
STRUCT
Struct containing lat/lng coordinates as floats.
detection_time
TIMESTAMP
ISO 8601 formatted timestamp e.g., 2019-09-25 17:26:27.757171 UTC.
observation_id
STRING
String that uniquely identifies the observation.
bbox
STRUCT
Struct of structs containing the x/y coordinates aligning the asset's bounding box.
camera_pose
STRUCT
Struct containing floats for lat/lng, altitude (in meters), pitch, heading, and roll.
capture_time
TIMESTAMP
ISO 8601 formatted timestamp e.g., 2019-09-25 17:26:27.757171 UTC.
gcs_uri
STRING
Google Cloud Storage URI where the image is hosted.
map_url
STRING
Google Maps URL that shows the location of the observation.
all_assets
An "asset" is an object in the real world. The all_assets table provides
assets from all snapshots. You can use this table to detect differences in
assets between snapshots.
Column name
Type
Description
asset_id
STRING
Unique identifier for the asset.
snapshot_id
STRING
Unique identifier for the snapshot. Used as a key to join tables.
asset_type
STRING
Major classification of the asset e.g., ASSET_CLASS_ROAD_SIGN.
observation_id
STRING
String that uniquely identifies the observation.
location
STRUCT
Struct containing lat/lng coordinates as floats.
detection_time
TIMESTAMP
ISO 8601 formatted timestamp e.g., 2019-09-25 17:26:27.757171 UTC.
latest_observations
The latest_observations table provides observations only from the most recent
snapshot.
Column name
Type
Description
snapshot_id
STRING
Unique identifier for the snapshot. Used as a key to join tables.
asset_id
STRING
Unique identifier for the asset.
asset_type
STRING
Major classification of the asset e.g., ASSET_CLASS_ROAD_SIGN.
location
STRUCT
Struct containing lat/lng coordinates as floats.
detection_time
TIMESTAMP
ISO 8601 formatted timestamp e.g., 2019-09-25 17:26:27.757171 UTC.
observation_id
STRING
String that uniquely identifies the observation.
bbox
STRUCT
Struct of structs containing the x/y coordinates aligning the asset's bounding box.
camera_pose
STRUCT
Struct containing floats for lat/lng, altitude (in meters), pitch, heading, and roll.
capture_time
TIMESTAMP
ISO 8601 formatted timestamp e.g., 2019-09-25 17:26:27.757171 UTC.
gcs_uri
STRING
Google Cloud Storage URI where the image is hosted.
map_url
STRING
Google Maps URL that shows the location of the observation.
latest_assets
The latest_assets table provides assets only from the most recent snapshot.
Column name
Type
Description
asset_id
STRING
Unique identifier for the asset.
snapshot_id
STRING
Unique identifier for the snapshot. Used as a key to join tables.
asset_type
STRING
Major classification of the asset e.g., ASSET_CLASS_ROAD_SIGN.
observation_id
STRING
String that uniquely identifies the observation.
location
STRUCT
Struct containing lat/lng coordinates as floats.
detection_time
TIMESTAMP
ISO 8601 formatted timestamp e.g., 2019-09-25 17:26:27.757171 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-03 UTC."],[],[],null,["# Reference\n\nThis page provides a guide to the BigQuery tables used for training models,\nincluding their schemas and field descriptions. You'll also find a breakdown of\nthe core concepts---snapshots, assets, and observations---that structure the data,\nalong with details on how to use the tables where relevant.\n\n`snapshots`\n-----------\n\nA snapshot is a fixed, unchangeable copy of a dataset at a specific moment in\ntime. The snapshot table provides metadata associated with snapshots, to\nallow you to understand the temporal state of the data at a high level.\n\n| Column name | Type | Description |\n|-------------------|-----------|----------------------------------------------------------------------|\n| `snapshot_id` | STRING | Unique identifier for the snapshot. Used as a key to join tables. |\n| `subscription_id` | STRING | Unique identifier for the subscription. |\n| `creation_time` | TIMESTAMP | ISO 8601 formatted timestamp e.g., `2019-09-25 17:26:27.757171 UTC`. |\n\n`all_observations`\n------------------\n\nAn \"observation\" is a sighting of an asset in the real world. Observations\ndiffer from assets in that they contain metadata for the detection of an asset,\ni.e., the time the image was captured and the position of the camera.\n\nThe `all_observations` table provides observations from all snapshots. You can\nuse this table to detect differences in observations between snapshots.\n\n| Column name | Type | Description |\n|------------------|-----------|---------------------------------------------------------------------------------------|\n| `snapshot_id` | STRING | Unique identifier for the snapshot. Used as a key to join tables. |\n| `asset_id` | STRING | Unique identifier for the asset. |\n| `asset_type` | STRING | Major classification of the asset e.g., `ASSET_CLASS_ROAD_SIGN`. |\n| `location` | STRUCT | Struct containing lat/lng coordinates as floats. |\n| `detection_time` | TIMESTAMP | ISO 8601 formatted timestamp e.g., `2019-09-25 17:26:27.757171 UTC`. |\n| `observation_id` | STRING | String that uniquely identifies the observation. |\n| `bbox` | STRUCT | Struct of structs containing the x/y coordinates aligning the asset's bounding box. |\n| `camera_pose` | STRUCT | Struct containing floats for lat/lng, altitude (in meters), pitch, heading, and roll. |\n| `capture_time` | TIMESTAMP | ISO 8601 formatted timestamp e.g., `2019-09-25 17:26:27.757171 UTC`. |\n| `gcs_uri` | STRING | Google Cloud Storage URI where the image is hosted. |\n| `map_url` | STRING | Google Maps URL that shows the location of the observation. |\n\n`all_assets`\n------------\n\nAn \"asset\" is an object in the real world. The `all_assets` table provides\nassets from all snapshots. You can use this table to detect differences in\nassets between snapshots.\n\n| Column name | Type | Description |\n|------------------|-----------|----------------------------------------------------------------------|\n| `asset_id` | STRING | Unique identifier for the asset. |\n| `snapshot_id` | STRING | Unique identifier for the snapshot. Used as a key to join tables. |\n| `asset_type` | STRING | Major classification of the asset e.g., `ASSET_CLASS_ROAD_SIGN`. |\n| `observation_id` | STRING | String that uniquely identifies the observation. |\n| `location` | STRUCT | Struct containing lat/lng coordinates as floats. |\n| `detection_time` | TIMESTAMP | ISO 8601 formatted timestamp e.g., `2019-09-25 17:26:27.757171 UTC`. |\n\n`latest_observations`\n---------------------\n\nThe `latest_observations` table provides observations only from the most recent\nsnapshot.\n\n| Column name | Type | Description |\n|------------------|-----------|---------------------------------------------------------------------------------------|\n| `snapshot_id` | STRING | Unique identifier for the snapshot. Used as a key to join tables. |\n| `asset_id` | STRING | Unique identifier for the asset. |\n| `asset_type` | STRING | Major classification of the asset e.g., `ASSET_CLASS_ROAD_SIGN`. |\n| `location` | STRUCT | Struct containing lat/lng coordinates as floats. |\n| `detection_time` | TIMESTAMP | ISO 8601 formatted timestamp e.g., `2019-09-25 17:26:27.757171 UTC`. |\n| `observation_id` | STRING | String that uniquely identifies the observation. |\n| `bbox` | STRUCT | Struct of structs containing the x/y coordinates aligning the asset's bounding box. |\n| `camera_pose` | STRUCT | Struct containing floats for lat/lng, altitude (in meters), pitch, heading, and roll. |\n| `capture_time` | TIMESTAMP | ISO 8601 formatted timestamp e.g., `2019-09-25 17:26:27.757171 UTC`. |\n| `gcs_uri` | STRING | Google Cloud Storage URI where the image is hosted. |\n| `map_url` | STRING | Google Maps URL that shows the location of the observation. |\n\n`latest_assets`\n---------------\n\nThe `latest_assets` table provides assets only from the most recent snapshot.\n\n| Column name | Type | Description |\n|------------------|-----------|----------------------------------------------------------------------|\n| `asset_id` | STRING | Unique identifier for the asset. |\n| `snapshot_id` | STRING | Unique identifier for the snapshot. Used as a key to join tables. |\n| `asset_type` | STRING | Major classification of the asset e.g., `ASSET_CLASS_ROAD_SIGN`. |\n| `observation_id` | STRING | String that uniquely identifies the observation. |\n| `location` | STRUCT | Struct containing lat/lng coordinates as floats. |\n| `detection_time` | TIMESTAMP | ISO 8601 formatted timestamp e.g., `2019-09-25 17:26:27.757171 UTC`. |"]]
