Crop Type Mapping
4 papers with code • 1 benchmarks • 1 datasets
SICKLE
Most implemented papers
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
marccoru/elects
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In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.
BreizhCrops: A Time Series Dataset for Crop Type Mapping
We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series.
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
felixquinton1/deep-crop-rotation
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While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping.
SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters
Out of the 2, 370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields.
