Crop Classification
15 papers with code • 5 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Crop Classification
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CropAndWeed Dataset
EuroCrops
Sen4AgriNet
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.
The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation
Precision Agriculture and especially the application of automated weed intervention represents an increasingly essential research area, as sustainability and efficiency considerations are becoming more and more relevant.
Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides
BMIRDS/deepslide
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Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results.
Spatio-temporal crop classification of low-resolution satellite imagery with capsule layers and distributed attention
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing.
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations
We propose to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences.
Crop mapping from image time series: deep learning with multi-scale label hierarchies
The three-level label hierarchy is encoded in a convolutional, recurrent neural network (convRNN), such that for each pixel the model predicts three labels at different level of granularity.
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.
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation
However, when applied to target regions spatially different from the training region, these models perform poorly without any target labels due to the temporal shift of crop phenology between regions.
Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding
jnyborg/tpe
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Unlike previous positional encoding based on calendar time (e. g. day-of-year), TPE is based on thermal time, which is obtained by accumulating daily average temperatures over the growing season.
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning
orion-ai-lab/s4a
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In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning.
