Creating Raster Information Product using Raster Analytics¶

Raster Analytics¶

ArcGIS Enterprise at 10.5 provides you with the ability to perform large raster analysis using distributed computing. This capability is provided in the arcgis.raster.analytics module and includes functionality to summarize data, analyze patterns, images, terrain and manage data. This sample show the capabilities of imagery layers and raster analytics.

Imagery layers¶

In [3]:

import arcgis
from arcgis.gis import GIS
from IPython.display import display

gis = GIS('https://pythonapi.playground.esri.com/portal', 'arcgis_python', 'amazing_arcgis_123')

Here we're getting the multispectral landsat imagery item:

In [2]:

items = gis.content.get('4ca13f0e4e29403fa68c46d188c4be73')
imglyr = items.layers[0]
items

Out[2]:

Landsat 8 Views
Landsat 8 multispectral and multitemporal imagery with on-the-fly renderings and indices for visualization and analysis. The imagery in this layer is updated daily and is directly sourced from the Landsat on AWS collection.

Imagery Layer by esri_livingatlas
Last Modified: May 13, 2020
0 comments, 0 views

The code below cycles through and lists the Raster Functions published with the imglyr:

In [6]:

for fn in imglyr.properties['rasterFunctionInfos']:
    print (fn['name'])

Agriculture with DRA
Bathymetric with DRA
Color Infrared with DRA
Natural Color with DRA
Short-wave Infrared with DRA
Geology with DRA
Agriculture
Bathymetric
Color Infrared
Geology
Natural Color
Short-wave Infrared
NDVI Colorized
Normalized Difference Moisture Index Colorized
NDVI Raw
NBR Raw
None

Let us create a map widget and load this layer

In [ ]:

marthasbasin = arcgis.geocoding.geocode("Marthas Basin, Montana")[0]

In [ ]:

map1 = gis.map(marthasbasin, zoomlevel=12)

In [1]:

map1

Out[1]:

In [ ]:

map1.add_layer(imglyr)

The utility of raster functions is better seen when we interactively cycle through these raster functions and apply them to the map. The code below cycles through the first 6 raster functions stored with the Imagery Layer and a small time delay to illustrate. The image processing occurs on-the-fly at display resolution to show how the layer can be visualized using these different raster functions published with the layer.

In [11]:

import time
from arcgis.raster.functions import apply

for fn in imglyr.properties['rasterFunctionInfos'][:6]:
    print(fn['name'])
    map1.remove_layers()
    map1.add_layer(apply(imglyr, fn['name']))
    time.sleep(4)

Agriculture with DRA
Bathymetric with DRA
Color Infrared with DRA
Natural Color with DRA
Short-wave Infrared with DRA
Geology with DRA

Raster functions¶

Developers can create their own raster functions, by chaining different raster functions. For instance, the code below is doing an Extract Band and extracting out the [4,5,3] band combination, and applying a Stretch to get the land-water boundary visualization that makes it easy to see where land is and where water is. Its worth noting that the raster function is applied at display resolution and only for the visible extent using on the fly image processing.

In [ ]:

from arcgis.raster.functions import stretch, extract_band

def process_bands(layer, bands):
    return stretch(extract_band(layer, bands), 
                    stretch_type='percentclip', min_percent=0.1, max_percent=0.1, gamma=[1, 1, 1], dra=True)

Let us apply this raster function to the image layer to visualize the results.

In [3]:

map2 = gis.map(marthasbasin, zoomlevel=12)
map2

Out[3]:

ArcGIS API for Python Sample Notebooks

Creating Raster Information Product using Raster Analytics¶

Raster Analytics¶

Imagery layers¶

Raster functions¶