The document discusses integrating the SpatioTemporal Asset Catalog (STAC) specification with RasterFrames to enable querying geospatial raster data using SQL. It describes RasterFrames, an open source library that structures earth observation data in Spark DataFrames for machine learning applications. The document poses questions about using the STAC API for large-scale queries and streaming results, and integrating cloud-optimized geospatial (COG) formats.

1. Toward STAC in RasterFrames
Satellite Data Interoperablity Workshop
August 13-15, 2018
Simeon H.K. Fitch
VP of R&D, Astraea, Inc.
fitch@astraea.earth
GitHub: @metasim

2. • Data Science Platform Company
• Core product: RasterFlow AI
– On-demand supercomputer for the Earth
– Enables non-expert leveraging of cutting-edge
machine learning on big geospatial data.
– Global Scale. Deep Insight. For Everyone.
• Built around our open source core:
See the Earth. As it was, as it is, as it could be.℠
2

3. GeoTrellis
Layers
GeoTIFF
GeoTIFF
GeoTIFF
STAC SparkSQLDataSourceAPI
EC2/EMR
RasterFrames: Data Ecosystem
3
Amazon PDS
(experimental)

4. RasterFrames: Anatomy
4
EO Data Product
DataFrame Row
(i.e. ML Observation)
Tiled BandsTPE Extent Metadata
λa
[0,0] λb
[0,0]
λc
[0,0]TPEA
[0,0]
. . .
. . .TPEA
[0,1]
. . .
. . .
. . .. . .. . .. . .. . .
TPEA
[1,0]
TPEA
[1,1]
. . .. . .. . .. . .. . .
λa
[0,1] λb
[0,1] λc
[0,1]
λa
[1,0] λb
[1,0] λc
[1,0]
λc
[1,1]λb
[1,1]λa
[1,1]
SM[0,0]
SM[0,1]
SM[1,1]
SM[1,0]
. . .
. . .
Spark DataFrame
Layer-wide Metadata
RasterFrameBandc
Bandb
Banda
Temporal
Projected
Extent (TPE)
Scene Metadata (SM)
… …
add_of f set
Band 32 emi ssi vi t y
scal e_f act or
Ti l eI D
Value
0. 002
1, 255
0. 49
l ong_name
Key
val i d_r ange
51004010
Multiband
Tile
Granule/Scene
GeoTIFF, HDF-EOS,
GML-JPEG2000, etc.
Spark DataFrame
Reader
Restructuring EO for Scaled ML

5. SELECT geometry, datetime, b1, b2, b3
WHERE
provider = 'NAIP' AND
st_intersects(geometry, st_geomFromText('WKT...')) AND
datetime > '2014-03-12' AND
datetime <= '2018-01-09' AND
eo:cloud_cover < 0.05
FROM stac_catalog
RasterFrames: Canonical Query
5
λa
[0,0] λb
[0,0] λc
[0,0]TPEA
[0,0]
. . .
. . .TPEA
[0,1]
. . .
. . .
. . .. . .. . .. . .. . .
TPEA
[1,0]
TPEA
[1,1]
. . .. . .. . .. . .. . .
λa
[0,1] λb
[0,1] λc
[0,1]
λa
[1,0] λb
[1,0] λc
[1,0]
λc
[1,1]λb
[1,1]λa
[1,1]
SM[0,0]
SM[0,1]
SM[1,1]
SM[1,0]
. . .
. . .
Layer-wide Metadata

6. RasterFrames: Query Deconstruction
6

7. Questions & Concerns
7

8. Questions & Concerns
8
• Is STAC-API appropriate for this use case?
– Would GraphQL help? (thx Jason Gilman for idea!)
• Can we reduce # of HTTP requests?
• Are there limits on results set size?
– Is paging assumed? Can we stream?
• How to integrate COG range requests?
– STAC extension?
– Also: Header offsets.

9. 9
http://rasterframes.io
fitch@astraea.earth

10. APPENDIX
10

11. DataSource Schema
11
override def schema: StructType = StructType(Seq(
StructField(C.ID, StringType, false),
StructField(C.TIMESTAMP, TimestampType, false),
StructField(C.PROPERTIES, DataTypes.createMapType(StringType, StringType, false)),
StructField(C.BOUNDS, geomSchema, true),
StructField(C.GEOMETRY, geomSchema, false),
StructField(C.ASSETS, assetSchema, false)
))

12. DataSource Table Build
12
override def buildScan(requiredColumns: Array[String], attributeFilters: Array[Filter]): RDD[Row] = {
val activeFilters = filters ++ attributeFilters.filterNot(_.references.contains(C.TIMESTAMP))
/** If no filters are provided, were not going to return the whole catalog. */
if(filters.isEmpty) sqlContext.sparkContext.emptyRDD[Row]
else {
val query = STACRelation.toUrl(base, activeFilters)
val result = getJson(query)
val featureRDD = result.asJsObject.fields("features") match {
case JsArray(features) ⇒ sqlContext.sparkContext.makeRDD(features
case _ ⇒ throw new IllegalArgumentException("Unexpected JSON response recevied:n" + result)
}
featureRDD.map(_.convertTo[DOM.GeoJsonFeature]).map { feature ⇒
val entries = requiredColumns.map {
case C.ID ⇒ feature.properties(C.ID).convertTo[String]
case C.TIMESTAMP ⇒ feature.properties("datetime").convertTo[Timestamp]
case C.PROPERTIES ⇒ feature.properties.mapValues(_.toString)
case C.BOUNDS ⇒ feature.bbox.map(_.jtsGeom).orNull
case C.GEOMETRY ⇒ feature.geometry
case C.ASSETS ⇒ feature.assets.mapValues(_.toString)
}
Row(entries: _*)
}
}

13. SQL Example
13
SELECT geometry, datetime, b1, b2, b3
WHERE
provider = 'NAIP' AND
st_intersects(geometry, st_geomFromText('WKT...')) AND
datetime > '2014-03-12' AND
datetime <= '2018-01-09' AND
eo:cloud_cover < 0.05
FROM stac_catalog