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Rob Emanuele @lossyrob
ANALYZING LARGE RASTER DATA
IN A JUPYTER NOTEBOOK
WITH GEOPYSPARK
ON AWS
Connect to the WIFI
Network: Harvard University
http://getonline.harvard.edu
Click “I am a guest”
Credentials:
U: foss4g2017@gmail.com
P: 7RFQU3rm
FIRST:
Find your Jupyter Notebook URL
https://git.io/v77lh
(lowercase L)
visit the URL next to your name
Log in to the Jupyter Hub
U: hadoop
P: hadoop
OUTLINE
8:00 - 8:30 Intro and Background
8:30 - 9:10 Section 1: Land Cover data
9:10 - 10:00 Section 2: Landsat 8 data
10:00 - 10:10 BREAK
10:10 - 10:30 Deployment and Ingestion
10:30 - 11:10 Section 3: Combining data layers
11:10 - 12:00 Section 4: Making Cool Maps
NOW:
A MOTIVATING EXAMPLE
BY
rdd.map(lambda x: x + 1)
Source: http://silverpond.com.au/2016/10/06/balancing-spark.ht
(1, 1) (2, 1)(0, 1)
(0, 0) (1, 0) (2, 0)
(1, 2) (2, 2)(0, 2)
(1, 1) (2, 1)(0, 1)
(0, 0) (1, 0) (2, 0)
(1, 2) (2, 2)(0, 2)
Node 1
Node 2
Node 3
(1, 1) (2, 1)(0, 1)
(0, 0) (1, 0) (2, 0)
(1, 2) (2, 2)(0, 2)
Node 1
Node 2
Node 3
(1, 1) (2, 1)(0, 1)
(0, 0) (1, 0) (2, 0)
(1, 2) (2, 2)(0, 2)
Node 1
Node 2
Node 3
(1, 1) (2, 1)(0, 1)
Node 1
Node 2
Node 3
(1, 1) (2, 1)(0, 1)
Node 1
Node 2
Node 3
rdd.bufferTiles(…)
+
+
Interactive and Batch Processing
of large raster data
Web-Speed Processing
of small to medium sized raster data
GeoTrellis Ecosystem
Raster Foundry by
Spark SQL and Spark ML support
Raster Frames by
Spark SQL and Spark ML support
GeoPySpark
Python bindings
Vector Pipes
Vector Tiles on Spark
PDAL integration
Point Clouds on Spark
GeoPySpark
Started December 2016
Follows PySpark’s model of communication
between the JavaVirtual Machine and Python
Access GeoTrellis functionality through Python,
and integrates with your favorite python raster
tools (numpy + friends).
0.2 is released!
GeoPySpark
EXERCISE 1:
ANALYZING LAND COVER DATA
EXERCISE 2:
WORKING WITH LANDSAT IMAGERY
AND NDVITHROUGHTIME
(SpaceTimeKey, Tile)
(SpaceTimeKey, Tile)
(SpaceTimeKey, Tile)
…
SpaceTimeKey ≈  (col, row, instant)
(SpaceTimeKey, Tile)
(SpaceTimeKey, Tile)
(SpaceTimeKey, Tile)
…
lambda
lambda
lambda
(SpatialKey, (DateTime, Tile))
(SpatialKey, (DateTime, Tile))
(SpatialKey, (DateTime, Tile))
…
…
(SpatialKey, [(DateTime, Tile)
(DateTime, Tile)])
(SpatialKey, (DateTime, Tile))
(SpatialKey, (DateTime, Tile))
(SpatialKey, (DateTime, Tile))
(SpatialKey, [(DateTime, Tile)])
…
…
(SpatialKey, [(DateTime, Tile)
(DateTime, Tile)])
(SpatialKey, (DateTime, Tile))
(SpatialKey, (DateTime, Tile))
(SpatialKey, (DateTime, Tile))
(SpatialKey, [(DateTime, Tile)])
(Shuffle)
…
(SpatialKey, [(DateTime, Tile)
(DateTime, Tile)])
(SpatialKey, [(DateTime, Tile)])
…
mosaic
(SpatialKey, Tile)
(SpatialKey, Tile)
…
mosaic
BREAK!
WHERE AND HOW ARETHESE
NOTEBOOKS RUNNING?
WHERE’STHIS DATA COMING
FROM?
Supported Backends
EXERCISE 3:
COMBINING LAND COVER AND NDVITO
DETECT CROP CYCLES
(SpaceTimeKey, Tile)
(SpaceTimeKey, Tile)
(SpaceTimeKey, Tile)
…
(SpaceTimeKey, Tile)
(SpaceTimeKey, Tile)
(SpaceTimeKey, Tile)
…
map_to_spatial
(SpatialKey, (STK, Tile))
(SpatialKey, (STK, Tile))
(SpatialKey, (STK, Tile))
…
map_to_spatial
map_to_spatial
STK = SpaceTimeKey
(SpatialKey, (STK, Tile))
(SpatialKey, (STK, Tile))
(SpatialKey, (STK, Tile))
…
(SpatialKey, Tile)
(SpatialKey, Tile)
…
ndwi_rdd
nlcd_layer.to_numpy_rdd()
(SpatialKey, ((STK, Tile), Tile))
(SpatialKey, ((STK, Tile), Tile))
(SpatialKey, ((STK, Tile),Tile))
…
(SpatialKey, (STK, Tile))
(SpatialKey, (STK, Tile))
(SpatialKey, (STK, Tile))
…
(SpatialKey, Tile)
(SpatialKey, Tile)
…
ndwi_rdd
nlcd_layer.to_numpy_rdd()
(SpatialKey, ((STK, Tile), Tile))
(SpatialKey, ((STK, Tile), Tile))
(SpatialKey, ((STK, Tile),Tile))
…
(Shuffle)
mask_ndwi
mask_ndwi
mask_ndwi
(SpaceTimeKey, Tile)
(SpaceTimeKey, Tile)
(SpaceTimeKey, Tile)
…
(SpatialKey, ((STK, Tile), Tile))
(SpatialKey, ((STK, Tile), Tile))
(SpatialKey, ((STK, Tile),Tile))
…
EXERCISE 4:
COMBINING IMAGERY, ELEVATION AND
LAND COVER DATA
TO MAKE A COOL LOOKING MAP
EXERCISE 4:
COMBINING IMAGERY, ELEVATION AND
LAND COVER DATA
TO MAKE A COOL LOOKING MAP
TWEETYOUR SWEET MAP SCREENSHOTS WITH
#GEOPYSPARK #FOSS4G!
FINAL QUESTIONS?
Thank you!

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