# DLTiles¶

When working with satellite imagery it can be challening to apply an analysis over a large area. In order to make this easier to do we have created DLTiles in our platform. For a region defined by geojson or latitude and longitude the platform will derive a list of tiles. These tiles will be the same across various imagery sources allowing you to easily work with data from different satellites. The tiles are projected in UTM and support whatever resolution you request.

For a very simple example on a small area I am going to calculate the land area of Rhode Island. I have written very simplistic method that mask the pixels determined to be water and that then sum up all the unmasked pixels giving us a total number of pixels that are land.

In [1]:

import os
from pprint import pprint
import descarteslabs as dl
dl.raster.url = 'https://platform-services.descarteslabs.com/raster/dev'
import sys
sys.path.append('/Users/conor/anaconda3/envs/descartes/Lib/site-packages') # yay windows
%matplotlib inline
import matplotlib.pyplot as plt
import shapely.geometry
import cartopy
import json
import numpy as np

def mask_water(image):
shape = image.shape
length = image.size

# reshape to linear
x = image.reshape(length)

# slice every 4th element
y = x[0::4]

# mask if less than 60 for NIR
sixty = np.ones(len(y))*60
z = y < sixty

# multiply by 4
a = np.repeat(z, 4)

# apply mask to original array
b = np.ma.masked_array(x, a)
b = np.ma.filled(b, 0)

# reshape
c = b.reshape(shape)
return c

# returns a count of all the pixels in an image that haven't been masked out
def get_land_pixel_count(image):
length = image.size
x = image.reshape(length)
y = x[3::4]
return np.count_nonzero(y)

# get the geometry for Newport County, Rhode Island
matches = dl.places.find('rhode-island_newport')
aoi = matches[0]
# get the shape of Newport County
shape = dl.places.shape(aoi['slug'], geom='low')
# get ids for imagery
feature_collection = dl.metadata.search(const_id='L8', start_time='2016-06-01',
end_time='2016-06-30', limit=10, place=aoi['slug'])
ids = [f['id'] for f in feature_collection['features']]

#rasterize the imagery and cut it to the shape of Newport County
arr, meta = dl.raster.ndarray(
ids,
bands=['nir', 'swir1', 'red', 'alpha'],
scales=[[0,6000], [0, 6000], [0, 6000], None],
data_type='Byte',
resolution=30,
cutline = shape['geometry']
)

# elimiate water
arr = mask_water(arr)

# get land area
print("land area = " + str(get_land_pixel_count(arr)*900) + " meters squared")

# plot the pretty picture
import matplotlib.pyplot as plt
%matplotlib inline
plt.figure(figsize=[24,24])
plt.imshow(arr)


land area = 266893200 meters squared

Out[1]:

<matplotlib.image.AxesImage at 0x180361ff780>


I have first run this analysis over Newport County, as shown above and come up with land area of 266893200 or about 103 square miles. Newport County has land area of 102 square miles which means our answer is almost respectable. Lets see how we do for the whole state.

The first step is to get a set of DL Tiles for Rhode Island.

In [2]:

lil_rhody = dl.places.shape("north-america_united-states_rhode-island")
tiles = dl.raster.dltiles_from_shape(30.0, 2048, 16, lil_rhody)
pprint(tiles['features'][0])
pprint("Total number of tiles for Rhode Island: " + str(len(tiles['features'])))

{'geometry': {'coordinates': [[[-71.92898332230831, 41.02873098011615],
[-71.18735024674605, 41.04520331997488],
[-71.20622433237934, 41.606871549447824],
[-71.95423703966668, 41.590072611375206],
[-71.92898332230831, 41.02873098011615]]],
'type': 'Polygon'},
'properties': {'cs_code': 'EPSG:32619',
'key': '2048:16:30.0:19:-4:74',
'outputBounds': [253760.0, 4546080.0, 316160.0, 4608480.0],
'pad': 16,
'resolution': 30.0,
'ti': -4,
'tilesize': 2048,
'tj': 74,
'zone': 19},
'type': 'Feature'}
'Total number of tiles for Rhode Island: 4'


We have gotten 4 tiles of with a resolution of 30 meters, a size of 2048 pixels per side, and with an overlap between tiles of 16 pixels. We can use any of the shapes from the places endpoint, a geojson, or use latitude and longitude to define an area to be tiled. That area is then divided up as appropriate and returned as a set. Lets take a look at how our tiles relate to the shape of the state.

In [3]:



lonlat_crs = cartopy.crs.PlateCarree()
albers = cartopy.crs.AlbersEqualArea(central_latitude=41.0, central_longitude=-71)

fig = plt.figure(figsize=(6, 8))
ax = plt.subplot(projection=albers) # Specify projection of the map here

ax.add_geometries([shapely.geometry.shape(lil_rhody['geometry'])],
lonlat_crs)

# Get the geometry from each feature
shapes = [shapely.geometry.shape(f['geometry']) for
f in tiles['features']]
ax.add_geometries(shapes, lonlat_crs, alpha=0.3, color='green')

# Get a bounding box of the combined scenes
union = shapely.geometry.MultiPolygon(polygons=shapes)
bbox = union.bounds
ax.set_extent((bbox[0], bbox[2], bbox[1], bbox[3]), crs=lonlat_crs)
ax.gridlines(crs=lonlat_crs)

plt.show()

C:\Users\conor\Anaconda3\lib\site-packages\matplotlib\ticker.py:1693: UserWarning: Steps argument should be a sequence of numbers
increasing from 1 to 10, inclusive. Behavior with
values outside this range is undefined, and will
raise a ValueError in future versions of mpl.
warnings.warn('Steps argument should be a sequence of numbers\n'


Lets look at imagery for these tiles for July 2016. By using the data contained in the tile for our raster call we’re able to get the imagery that corresponds with the tile. We also need to use a cutline that we generated from the shapes endpoint to limit the imagery returned to just the area of Rhode Island.

In [4]:

dates = [['2016-07-01','2016-07-31']]

tile_images = []

for date in dates:
print('from ' + date[0] + ' to ' + date[1])
counter = 0;
for tile in tiles['features']:
images = dl.metadata.search(
const_id=["L8"],
start_time=date[0],
end_time=date[1],
geom=json.dumps(tile['geometry']),
cloud_fraction=0.2,
limit = 1000
)

print('number of scenes for this tile: ' + str(len(images['features'])))
ids = []
for image in images['features']:
ids.append(image['id'])

arr, meta = dl.raster.ndarray(
ids,
bands=['nir', 'swir1', 'red', 'alpha'],
scales=[[0,6000], [0, 6000], [0, 6000], None],
data_type='Byte',
srs = tile['properties']['cs_code'],
resolution = tile['properties']['resolution'],
bounds = tile['properties']['outputBounds'],
cutline = lil_rhody['geometry'])

arr = arr[16:-16, 16:-16]

tile_images.append([np.copy(arr),meta])

plt.figure(figsize=[16,16])
plt.imshow(arr)

from 2016-07-01 to 2016-07-31
number of scenes for this tile: 4
number of scenes for this tile: 5
number of scenes for this tile: 4
number of scenes for this tile: 2


And look at that, Rhode Island all broken up into tiles ready to be analyzed. So lets see how much land area we come up with for the whole state.

In [5]:

print('running land area analysis')

total_land_pixels = 0

for the_image in tile_images:
meta = the_image[1]
image_pixels = the_image[0]
image_pixels = mask_water(image_pixels)
plt.figure(figsize=[16,16])
plt.imshow(image_pixels)
cur_land_count = get_land_pixel_count(image_pixels)
total_land_pixels += cur_land_count

print("land area = " + str(total_land_pixels*900) + " meters squared")

running land area analysis
land area = 2658275100 meters squared


This gives ups 2658275100 square meters which works out to 1044 square miles which is only 86% of the land area of Rhode Island. Judging by the swiss cheese looking images of the state it is a safe guess that cloud shadows are getting classified as water which accounts for the error.

Rhode Isand is a tiny little state that barely merits using tiles - lets take a look at New York. Because New York is so much larger we’ll go with 60 meter resolution instead if 30.

In [6]:

new_york = dl.places.shape("north-america_united-states_new-york")
tiles = dl.raster.dltiles_from_shape(60.0, 2048, 16, new_york)
pprint(tiles['features'][0])
pprint("Total number of tiles for New York: " + str(len(tiles['features'])))

{'geometry': {'coordinates': [[[-81.01142542548452, 41.061641925635584],
[-79.52635307127217, 41.0522195452879],
[-79.50054542369706, 42.17594834200205],
[-81.01162560699078, 42.185747884904536],
[-81.01142542548452, 41.061641925635584]]],
'type': 'Polygon'},
'properties': {'cs_code': 'EPSG:32617',
'key': '2048:16:60.0:17:0:37',
'outputBounds': [499040.0, 4545600.0, 623840.0, 4670400.0],
'pad': 16,
'resolution': 60.0,
'ti': 0,
'tilesize': 2048,
'tj': 37,
'zone': 17},
'type': 'Feature'}
'Total number of tiles for New York: 32'


32 tiles, now we’re talking! Lets see how much land area New York has. This will take a non-trivial amount of time to run.

In [7]:

dates = [['2016-06-01','2016-06-30']]

total_land_pixels = 0
counter = 1

for date in dates:
print('from ' + date[0] + ' to ' + date[1])
counter = 0;
for tile in tiles['features']:
images = dl.metadata.search(
const_id=["L8"],
start_time=date[0],
end_time=date[1],
geom=json.dumps(tile['geometry']),
cloud_fraction=0.2,
limit = 1000
)

print('Tile #' + str(counter) + '. Number of scenes for this tile: ' + str(len(images['features'])))
counter += 1
ids = []
for image in images['features']:
ids.append(image['id'])

arr, meta = dl.raster.ndarray(
ids,
bands=['nir', 'swir1', 'red', 'alpha'],
scales=[[0,6000], [0, 6000], [0, 6000], None],
data_type='Byte',
srs = tile['properties']['cs_code'],
resolution = tile['properties']['resolution'],
bounds = tile['properties']['outputBounds'],
cutline = new_york['geometry'])

arr = arr[16:-16, 16:-16]

arr = mask_water(arr)
total_land_pixels += get_land_pixel_count(arr)

print('total land pixels: ' + str(total_land_pixels))
print('square meters: ' + str(total_land_pixels * 3600))

from 2016-06-01 to 2016-06-30
Tile #0. Number of scenes for this tile: 6
Tile #1. Number of scenes for this tile: 6
Tile #2. Number of scenes for this tile: 4
Tile #3. Number of scenes for this tile: 4
Tile #4. Number of scenes for this tile: 5
Tile #5. Number of scenes for this tile: 2
Tile #6. Number of scenes for this tile: 2
Tile #7. Number of scenes for this tile: 3
Tile #8. Number of scenes for this tile: 4
Tile #9. Number of scenes for this tile: 2
Tile #10. Number of scenes for this tile: 2
Tile #11. Number of scenes for this tile: 3
Tile #12. Number of scenes for this tile: 4
Tile #13. Number of scenes for this tile: 3
Tile #14. Number of scenes for this tile: 3
Tile #15. Number of scenes for this tile: 5
Tile #16. Number of scenes for this tile: 6
Tile #17. Number of scenes for this tile: 6
Tile #18. Number of scenes for this tile: 5
Tile #19. Number of scenes for this tile: 4
Tile #20. Number of scenes for this tile: 5
Tile #21. Number of scenes for this tile: 7
Tile #22. Number of scenes for this tile: 6
Tile #23. Number of scenes for this tile: 7
Tile #24. Number of scenes for this tile: 5
Tile #25. Number of scenes for this tile: 5
Tile #26. Number of scenes for this tile: 4
Tile #27. Number of scenes for this tile: 5
Tile #28. Number of scenes for this tile: 3
Tile #29. Number of scenes for this tile: 5
Tile #30. Number of scenes for this tile: 3
Tile #31. Number of scenes for this tile: 5
total land pixels: 30787583
square meters: 110835298800


This gives us 44,449 square miles which is 81 percent of the 54,556 square miles that actually make up New York state. Ultimately I wrote a pretty terrible algorithm for analysis but using DLTiles it was very easy for us to determine that, and it would also be easy to iterate on this and turn it into a good algorithm. Using DLTiles we can scale our analysis up all the way to the entire surface of the Earth.