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Crop Classification

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–48 of 48 papers

TitleStatusHype
PhytNet -- Tailored Convolutional Neural Networks for Custom Botanical Data—0
XAI for Early Crop Classification—0
Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data—0
Crop identification using deep learning on LUCAS crop cover photosCode0
Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series—0
A Strategy Optimized Pix2pix Approach for SAR-to-Optical Image Translation Task—0
Time Gated Convolutional Neural Networks for Crop Classification—0
Bagged Polynomial Regression and Neural Networks—0
Activation Regression for Continuous Domain Generalization with Applications to Crop ClassificationCode0
Tampered VAE for Improved Satellite Image Time Series Classification—0
Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach—0
Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery—0
Spatio-temporal Crop Classification On Volumetric Data—0
Certified robustness against physically-realizable patch attack via randomized cropping—0
Crop and weed classification based on AutoML—0
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)—0
Bayesian aggregation improves traditional single image crop classification approaches—0
Logistic regression models for aggregated data—0
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China—0
Spatio-temporal crop classification of low-resolution satellite imagery with capsule layers and distributed attentionCode0
End-to-End Learned Early Classification of Time Series for In-Season Crop Type MappingCode0
Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series—0
Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological SlidesCode0
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All datasetsCropHarvest - KenyaCropHarvest - TogoCropHarvest - BrazilCropHarvest - GlobalCropHarvest multicrop - Global

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PrestoR - no DWTarget Binary F10.86—Unverified
2Feature-level fusion (sum)Target Binary F10.79—Unverified
3Gated Fusion (Feature-level)Target Binary F10.77—Unverified
4Radar TS with TempCNNAverage Accuracy0.68—Unverified
5Input Fusion with TAEAverage Accuracy0.67—Unverified
#ModelMetricClaimedVerifiedStatus
1Ensemble aggregation with GRUAverage Accuracy0.84—Unverified
2Ensemble aggregationAverage Accuracy0.84—Unverified
3Decision fusion with GRUAverage Accuracy0.83—Unverified
4PrestoRTarget Binary F10.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Feature fusion with LSTMAverage Accuracy0.98—Unverified
2Hybrid fusion with LSTMAverage Accuracy0.97—Unverified
3PrestoRTarget Binary F10.89—Unverified
#ModelMetricClaimedVerifiedStatus
1Feature Gated FusionAverage Accuracy0.85—Unverified
2Input FusionAverage Accuracy0.85—Unverified
3Ensemble strategyAverage Accuracy0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1Input FusionAverage Accuracy0.74—Unverified
2Feature Gated FusionAverage Accuracy0.73—Unverified
3Ensemble strategyAverage Accuracy0.72—Unverified