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*`image.first_datetime`: timestamp of first image in sequence
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*`image.first_coordinates`: co-ordinated of first image
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*`image.last_datetime`: timestamp of last image in sequence
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*`image.last_coordinates`: co-ordinated of last image
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*`weather.temperature_celsius`
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*`weather.wind_speed_kmh`
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*`weather.wind_direction_degrees`
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*`weather.weather_descriptions`
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*`weather.precipitation_mm`
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*`weather.humidity_pc`
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*`weather.visibility_km`
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*`weather.pressure_millibar`
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*`weather.cloudcover_pc`
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*`weather.heatindex_celsius`
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*`weather.dewpoint_celsius`
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*`weather.windchill_celsius`
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*`weather.windgust_kmh`
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*`weather.feelslike_celsius`
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*`weather.uv_index_celsius`
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*`air_quality.aqi`
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*`air_quality.co`
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*`air_quality.no`
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*`air_quality.no2`
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*`air_quality.o3`
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*`air_quality.so2`
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*`air_quality.pm2_5`
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*`air_quality.pm10`
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*`air_quality.nh3`
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*`favourited_user_ids`: (a list of user IDs who have marked the sequence as a favourite)
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Note, the assumption here is that the photo values (make, model, height and width) are the same forall imagesin the sequence, which is truein the way we import images as sequences.
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All `weather.` data is pulled from hourly weather information using `image.first_datetime` and `image.first_coordinates` of the first photo in sequence. Data is pulled from WeatherStack ([as described here](/blog/historic-weather-data-lookup)).
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All `air_quality.` data is pulled from the OpenWeather Air Pollution API using `image.first_datetime` and `image.first_coordinates` of the first photo in sequence. Also described in ([this post](/blog/historic-weather-data-lookup)).
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For each sequence one or more image. For each image we hold the following data that is all auto-assigned
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*`uuid`: Trek View Image ID
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*`owner_uuid`: Trek View user ID of Imgae owner
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*`sequence_uuid`: The sequence ID the image belongs too
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*`mapillary_id`
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*`mapillary.id`
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* data.id: string, ID of the image
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*`mapillary_altitude`
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*`mapillary.altitude`
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* data.altitude: float, original altitude from camera Exif calculated from sea level.
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*`mapillary_captured_at`
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*`mapillary.captured_at`
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* data.captured_at: timestamp, capture time.
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*`mapillary_compass_angle`
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*`mapillary.compass_angle`
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* data.compass_angle: float, original compass angle of the image.
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*`mapillary_geometry_coordinates`
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*`mapillary.geometry_coordinates`
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* data.geometry.coordinates: GeoJSON Point geometry.
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*`mapillary_computed_altitude`
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*`mapillary.computed_altitude`
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* data.computed_altitude: float, altitude after running image processing, from sea level.
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*`mapillary_computed_compass_angle`
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*`mapillary.computed_compass_angle`
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* data.computed_compass_angle: float, compass angle after running image processing.
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*`mapillary_computed_coordinates`
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*`mapillary.computed_coordinates`
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* data.computed_geometry.coordinates: GeoJSON Point, location after running image processing.
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*`mapillary_computed_rotation`
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*`mapillary.computed_rotation`
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* data.computed_rotation: enum, corrected orientation of the image.
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*`mapillary_thumb_original_url` (seems to be full size image, not thumbnail -- the name is confusing)
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*`mapillary.thumb_original_url` (seems to be full size image, not thumbnail -- the name is confusing)
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* data.thumb_original_url: string, URL to the original wide thumbnail.
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*`mapillary_thumb_256_url`
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*`mapillary.thumb_256_url`
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* data.thumb_256_url: string, URL to the 256px wide thumbnail.
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*`mapillary_thumb_1024_url`
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*`mapillary.thumb_1024_url`
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* data.thumb_1024_url: string, URL to the 1024px wide thumbnail.
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*`mapillary_thumb_2048_url`
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*`mapillary.thumb_2048_url`
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* data.thumb_2048_url: string, URL to the 2048px wide thumbnail.
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*`favourited_user_ids` (a list of user IDs who have marked the image viewpoint)
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@@ -327,4 +357,4 @@ Though that wasn't the aim of these posts. I wanted to see if I could build a St
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No money was a little ambitious, although a small VPS would probably be more than adequate for the implementation described above, but actually building this thing seems totally possible.
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Watch this space...
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In the next post I'll show you some early designs...
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