SOTAVerified

Crop Classification

Papers

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Showing 1–48 of 48 papers

TitleStatusHype
Lightweight, Pre-trained Transformers for Remote Sensing TimeseriesCode2
SITSMamba for Crop Classification based on Satellite Image Time SeriesCode1
A Comparative Assessment of Multi-view fusion learning for Crop ClassificationCode1
The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed ManipulationCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
Generalized Classification of Satellite Image Time Series with Thermal Positional EncodingCode1
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift EstimationCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time SeriesCode1
Crop mapping from image time series: deep learning with multi-scale label hierarchiesCode1
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential EquationsCode1
A Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic SegmentationCode0
MT-CYP-Net: Multi-Task Network for Pixel-Level Crop Yield Prediction Under Very Few Samples—0
YOLO-RS: Remote Sensing Enhanced Crop Detection Methods—0
Towards more efficient agricultural practices via transformer-based crop type classification—0
On the Generalizability of Foundation Models for Crop Type MappingCode0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Increasing the Robustness of Model Predictions to Missing Sensors in Earth ObservationCode0
XAI-Guided Enhancement of Vegetation Indices for Crop Mapping—0
CMTNet: Convolutional Meets Transformer Network for Hyperspectral Images Classification—0
Low-Resource Crop Classification from Multi-Spectral Time Series Using Lossless CompressorsCode0
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing DataCode0
Impact Assessment of Missing Data in Model Predictions for Earth Observation ApplicationsCode0
Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning—0
Cross Domain Early Crop Mapping using CropSTGAN—0
Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture—0
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
Show:102550
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