SOTAVerified

Crop Classification

Papers

Showing 4148 of 48 papers

TitleStatusHype
Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning0
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China0
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)0
Logistic regression models for aggregated data0
MT-CYP-Net: Multi-Task Network for Pixel-Level Crop Yield Prediction Under Very Few Samples0
PhytNet -- Tailored Convolutional Neural Networks for Custom Botanical Data0
Spatio-temporal Crop Classification On Volumetric Data0
Tampered VAE for Improved Satellite Image Time Series Classification0
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Benchmark Results

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