Method and apparatus for employing deep learning neural network to predict cropland data layer
A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.
1 . A computer-implemented method for predicting a cropland data layer for use within a current growing year, the computer-implemented method comprising:
retrieving a first set of records from a historical cropland data layer database, wherein the first set of records corresponds to randomly sampled areas of a geographic region taken over a prescribed time period for a prescribed number of years;
retrieving a second set of records from a historical imagery database, wherein the second set of records corresponds to the randomly sampled areas of the geographic region, the prescribed time period, and the prescribed number of years;
employing the second set of records as inputs to train a deep learning convolutional neural network to generate the first set of records and using parameters generated during training to configure a trained deep learning convolutional neural network for execution, the employing comprising:
cleansing the second set of records from the historical imagery database by removing duplicate information, inferring missing values, substituting for unconventional characters and symbols, or removing outlier values;
retrieving a third set of records from a current imagery database, wherein the third set of records corresponds to a prescribed geographic region, and wherein the third set of records corresponds to the prescribed time period and the current growing year; and
using the third set of records as inputs and executing the trained deep learning convolutional neural network to generate a predicted cropland data layer for the current growing year, the using comprising:
stitching adjacent records from the current imagery database using coordinates of corresponding farms.
2 . The computer-implemented method as recited in claim 1 , wherein the deep learning convolutional neural network and the trained deep learning convolutional neural network each comprise 5 layers.
3 . The computer-implemented method as recited in claim 1 , wherein each of the second and third sets of records each comprise 128×128 pixel images.
4 . The computer-implemented method as recited in claim 3 , wherein the each of the 128×128 pixel images comprise Sentinel satellite red channel, blue channel, green channel, near infrared channel, and cloud mask channel.
5 . The computer-implemented method as recited in claim 4 , wherein the prescribed time period comprises May through October, and wherein the number of 128×128 pixel images for each of the prescribed number of years comprises 12 images.
6 . The computer-implemented method as recited in claim 1 , wherein the prescribed number of years comprises three 3 years previous to the current growing year.
7 . The computer-implemented method as recited in claim 1 , wherein the predicted cropland data layer comprises a raster of color-coded pixels, and wherein each pixel comprises a 30 meter×30 meter area within the prescribed geographic region, and wherein each pixel's color is indicative of a particular type of land covering for the 30 meter×30 meter area.
8 . A non-transitory computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform a method for predicting a cropland data layer for use within a current growing year, the method comprising:
retrieving a first set of records from a historical cropland data layer database, wherein the first set of records corresponds to randomly sampled areas of a geographic region taken over a prescribed time period for a prescribed number of years;
retrieving a second set of records from a historical imagery database, wherein the second set of records corresponds to the randomly sampled areas of the geographic region, the prescribed time period, and the prescribed number of years;
employing the second set of records as inputs to train a deep learning convolutional neural network to generate the first set of records and using parameters generated during training to configure a trained deep learning convolutional neural network for execution, the employing comprising
cleansing the second set of records from the historical imagery database by removing duplicate information, inferring missing values, substituting for unconventional characters and symbols, or removing outlier values;
retrieving a third set of records from a current imagery database, wherein the third set of records corresponds to a prescribed geographic region, and wherein the third set of records corresponds to the prescribed time period and the current growing year; and
using the third set of records as inputs and executing the trained deep learning convolutional neural network to generate a predicted cropland data layer for the current growing year, the using comprising:
stitching adjacent records from the current imagery database using coordinates of corresponding farms.
9 . The non-transitory computer-readable storage medium as recited in claim 8 , wherein the deep learning convolutional neural network and the trained deep learning convolutional neural network each comprise 5 layers.
10 . The non-transitory computer-readable storage medium as recited in claim 8 , wherein each of the second and third sets of records each comprise 128×128 pixel images.
11 . The non-transitory computer-readable storage medium as recited in claim 10 , wherein the each of the 128×128 pixel images comprise Sentinel satellite red channel, blue channel, green channel, near infrared channel, and cloud mask channel.
12 . The non-transitory computer-readable storage medium as recited in claim 11 , wherein the prescribed time period comprises May through October, and wherein the number of 128×128 pixel images for each of the prescribed number of years comprises 12 images.
13 . The non-transitory computer-readable storage medium as recited in claim 8 , wherein the prescribed number of years comprises three 3 years previous to the current growing year.
14 . The non-transitory computer-readable storage medium as recited in claim 8 , wherein the predicted cropland data layer comprises a raster of color-coded pixels, and wherein each pixel comprises a 30 meter×30 meter area within the prescribed geographic region, and wherein each pixel's color is indicative of a particular type of land covering for the 30 meter×30 meter area.
15 . A computer program product for predicting a cropland data layer for use within a current growing year, the computer program product comprising:
a computer readable non-transitory medium having computer readable program code stored thereon, the computer readable program code comprising:
program instructions to retrieve a first set of records from a historical cropland data layer database, wherein the first set of records corresponds to randomly sampled areas of a geographic region taken over a prescribed time period for a prescribed number of years;
program instructions to retrieve a second set of records from a historical imagery database, wherein the second set of records corresponds to the randomly sampled areas of the geographic region, the prescribed time period, and the prescribed number of years;
program instructions to employ the second set of records as inputs to train a deep learning convolutional neural network to generate the first set of records and to use parameters generated during training to configure a trained deep learning convolutional neural network for execution, wherein the second set of records from the historical imagery database are cleansed by removing duplicate information, inferring missing values, substituting for unconventional characters and symbols, or removing outlier values;
program instructions to retrieve a third set of records from a current imagery database, wherein the third set of records corresponds to a prescribed geographic region, and wherein the third set of records corresponds to the prescribed time period and the current growing year; and
program instructions to use the third set of records as inputs and to execute the trained deep learning convolutional neural network to generate a predicted cropland data layer for the current growing year, wherein adjacent records from the current imagery database are stitched together using coordinates of corresponding farms.
16 . The computer program product as recited in claim 15 , wherein the deep learning convolutional neural network and the trained deep learning convolutional neural network each comprise 5 layers.
17 . The computer program product as recited in claim 15 , wherein each of the second and third sets of records each comprise 128×128 pixel images.
18 . The computer program product as recited in claim 17 wherein the prescribed time period comprises May through October, and wherein the number of 128×128 pixel images for each of the prescribed number of years comprises 12 images.
19 . The computer program product as recited in claim 15 , wherein the prescribed number of years comprises three 3 years previous to the current growing year.
20 . The computer program product as recited in claim 15 , wherein the predicted cropland data layer comprises a raster of color-coded pixels, and wherein each pixel comprises a 30 meter×30 meter area within the prescribed geographic region, and wherein each pixel's color is indicative of a particular type of land covering for the 30 meter×30 meter area.