Radiant MLHub

Drone Imagery Classification Training Dataset for Crop Types in Rwanda

satellite

RTI International (RTI) generated 2,611 labeled point locations representing 19 different land cover types, clustered in 5 distinct agroecological zones within Rwanda. These land cover types were reduced to three crop types (Banana, Maize, and Legume), two additional non-crop land cover types (Forest and Structure), and a catch-all Other land cover type to provide training/evaluation data for a crop classification model. Each point is attributed with its latitude and longitude, the land cover type, and the degree of confidence the labeler had when classifying the point location. For each location there are also three corresponding image chips (4.5 m x 4.5 m in size) with the point id as part of the image name. Each image contains a P1, P2, or P3 designation in the name, indicating the time period. P1 corresponds to December 2018, P2 corresponds to January 2019, and P3 corresponds to February 2019. These data were used in the development of research documented in greater detail in “Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images” (Chew et al., 2020).

Dataset ID

rti_rwanda_crop_type

DOI

10.34911/rdnt.r4p1fr

Creator

RTI International

Contact

jrin@rti.org

Documentation

Citation

Rineer J., Beach R., Lapidus D., O’Neil M., Temple D., Ujeneza N., Cajka J., & Chew R. (2021) “Drone Imagery Classification Training Dataset for Crop Types in Rwanda”, Version 1.0, Radiant MLHub https://doi.org/10.34911/rdnt.r4p1fr

Python Client example

from radiant_mlhub import Dataset

ds = Dataset.fetch('rti_rwanda_crop_type')
for c in ds.collections:
    print(c.id)

Python Client quick-start guide

Source Imagery Collections

Description

RTI International (RTI) generated 2,611 labeled point locations representing 19 different land cover types, clustered in 5 distinct agroecological zones within Rwanda. These land cover types were reduced to three crop types (Banana, Maize, and Legume), two additional non-crop land cover types (Forest and Structure), and a catch-all Other land cover type to provide training/evaluation data for a crop classification model. Each point is attributed with its latitude and longitude, the land cover type, and the degree of confidence the labeler had when classifying the point location. For each location there are also three corresponding image chips (4.5 m x 4.5 m in size) with the point id as part of the image name. Each image contains a P1, P2, or P3 designation in the name, indicating the time period. P1 corresponds to December 2018, P2 corresponds to January 2019, and P3 corresponds to February 2019. These data were used in the development of research documented in greater detail in “Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images” (Chew et al., 2020).

License

CC-BY-NC-SA-4.0

Collection ID

rti_rwanda_crop_type_source

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Description

RTI International (RTI) generated 2,611 labeled point locations representing 19 different land cover types, clustered in 5 distinct agroecological zones within Rwanda. These land cover types were reduced to three crop types (Banana, Maize, and Legume), two additional non-crop land cover types (Forest and Structure), and a catch-all Other land cover type to provide training/evaluation data for a crop classification model. Each point is attributed with its latitude and longitude, the land cover type, and the degree of confidence the labeler had when classifying the point location. For each location there are also three corresponding image chips (4.5 m x 4.5 m in size) with the point id as part of the image name. Each image contains a P1, P2, or P3 designation in the name, indicating the time period. P1 corresponds to December 2018, P2 corresponds to January 2019, and P3 corresponds to February 2019. These data were used in the development of research documented in greater detail in “Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images” (Chew et al., 2020).

License

CC-BY-NC-SA-4.0

Collection ID

rti_rwanda_crop_type_raw

Download

Labels Collection

Description

RTI International (RTI) generated 2,611 labeled point locations representing 19 different land cover types, clustered in 5 distinct agroecological zones within Rwanda. These land cover types were reduced to three crop types (Banana, Maize, and Legume), two additional non-crop land cover types (Forest and Structure), and a catch-all Other land cover type to provide training/evaluation data for a crop classification model. Each point is attributed with its latitude and longitude, the land cover type, and the degree of confidence the labeler had when classifying the point location. For each location there are also three corresponding image chips (4.5 m x 4.5 m in size) with the point id as part of the image name. Each image contains a P1, P2, or P3 designation in the name, indicating the time period. P1 corresponds to December 2018, P2 corresponds to January 2019, and P3 corresponds to February 2019. These data were used in the development of research documented in greater detail in “Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images” (Chew et al., 2020).

License

CC-BY-NC-SA-4.0

Collection ID

rti_rwanda_crop_type_labels

Download