Crop Classification CNN (Example only, not suitable for production use!)
ID
1
Description
!! This model is for development purposes only, it is not suitable for production use !!
An example convolutional neural network trained on 7 Sentinel-2 images throughout the Dutch growing season, using bands R, G, B, and NIR of each image, and ground truth data taken from the Dutch agricultural land registration. All data used was from 2018, and the model has been trained to infer 76 different crop types.
Main category
DL
Other category
No response
Pubblication date
2022
Objective
segmentation
Platform
Rasdaman
Framework
PyTorch
Architecture
cnn
Approach
supervised
Algorithm
Convolutional-Neural-Network
Processor
gpu
OS
linux
Keyword
Dutch crop types, Sentinel-2, Convolutional Neural Network, Case Study, PyTorch
Feature data:
7 Sentinel-2 images, R,G,B,NIR bands, representative of the Dutch growing season 2018. The data was in UTM projection and only cloud free images have been used. It covered a study area in the North-East of the country.
Label data:
The Dutch agricultural land registration data from 2018 of the study area has been used as ground truth data. It contains the farm parcel boundaries and the planted crops. The full list of crops has been reduced to 76 major types that were at least present in the region and thought to be potentially recognisable from the feature data. Still, the labels are significantly imbalanced.
Biases and ethical aspects
The crop data (labels) are significantly imbalanced, particularly towards grasslands. The trained model is merely a proof of concept and not recommended for serious applications or use outside of the study region and/or for years it has not been trained for.
Output data obtained
In rasdaman?
Characteristics of output data
The model produces a spatial dataset with the inferred crop type as integer index value for each grid cell. The index is sequential and can be translated into the actual crop type.
Performance
This model is mostly a technological proof of concept and performance strongly varies per crop type (30% - 80%). Furthermore it achieves only low IoU values and the straight-forward CNN architecture used is not capable of reproducing parcel boundaries very well.
Name of resource
Crop Classification CNN (Example only, not suitable for production use!)
ID
1
Description
!! This model is for development purposes only, it is not suitable for production use !!
An example convolutional neural network trained on 7 Sentinel-2 images throughout the Dutch growing season, using bands R, G, B, and NIR of each image, and ground truth data taken from the Dutch agricultural land registration. All data used was from 2018, and the model has been trained to infer 76 different crop types.
Main category
DL
Other category
No response
Pubblication date
2022
Objective
segmentation
Platform
Rasdaman
Framework
PyTorch
Architecture
cnn
Approach
supervised
Algorithm
Convolutional-Neural-Network
Processor
gpu
OS
linux
Keyword
Dutch crop types, Sentinel-2, Convolutional Neural Network, Case Study, PyTorch
Reference link
https://github.com/FAIRiCUBE/uc2-agriculture-biodiversity-nexus/blob/main/rasdaman-ml-udf/proof_of_concept/FAIRICUBE%20Machine%20Learning%20UDF%20Proof%20of%20Concept.ipynb
Example
https://github.com/FAIRiCUBE/uc2-biodiversity-agriculture/tree/main/rasdaman-ml-udf
Input data used
In rasdaman
Characteristics of input data
Feature data: 7 Sentinel-2 images, R,G,B,NIR bands, representative of the Dutch growing season 2018. The data was in UTM projection and only cloud free images have been used. It covered a study area in the North-East of the country.
Label data: The Dutch agricultural land registration data from 2018 of the study area has been used as ground truth data. It contains the farm parcel boundaries and the planted crops. The full list of crops has been reduced to 76 major types that were at least present in the region and thought to be potentially recognisable from the feature data. Still, the labels are significantly imbalanced.
Biases and ethical aspects
The crop data (labels) are significantly imbalanced, particularly towards grasslands. The trained model is merely a proof of concept and not recommended for serious applications or use outside of the study region and/or for years it has not been trained for.
Output data obtained
In rasdaman?
Characteristics of output data
The model produces a spatial dataset with the inferred crop type as integer index value for each grid cell. The index is sequential and can be translated into the actual crop type.
Performance
This model is mostly a technological proof of concept and performance strongly varies per crop type (30% - 80%). Furthermore it achieves only low IoU values and the straight-forward CNN architecture used is not capable of reproducing parcel boundaries very well.
Conditions for access and use
cc-by-nc-sa-4.0
Constraints
No response