Crop Classification
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Benchmarks
CropHarvest - Kenya
CropHarvest - Togo
CropHarvest - Brazil
CropHarvest - Global
Most implemented
Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
A Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic Segmentation
SITSMamba for Crop Classification based on Satellite Image Time Series
On the Generalizability of Foundation Models for Crop Type Mapping
Papers
Foundation Models Meet Agriculture: Challenges Beyond Pretraining
Global food security and sustainable climate action increasingly rely on robust, scalable agricultural monitoring. Earth observation foundation models have emerged as powerful, label-efficient tools across general remote…
Crop ClassificationSpace2Ground 2.0: A Multi-Source Dataset and Framework for Agricultural Monitoring through Fusion of Street-Level and Satellite Imagery
Accurate and scalable parcel-level agricultural monitoring remains challenging because satellite Earth Observation alone provides only an overhead perspective of agricultural parcels, while optical observations are furth…
Image Quality AssessmentCrop ClassificationQuantum Enchanced Multi-Scale CNN with Bi-directional Mamba for Crop Field Analysis
Hyperspectral image (HSI) crop analysis is essential for precision agriculture because it captures rich spectral and spatial information for accurate crop monitoring and assessment. However, HSI classification remains ch…
Hyperspectral image analysisCrop ClassificationAn Explainable Ensemble Learning Framework for Crop Classification with Optimized Feature Pyramids and Deep Networks
Agriculture is increasingly challenged by climate change, soil degradation, and resource depletion, and hence requires advanced data-driven crop classification and recommendation solutions. This work presents an explaina…
Crop ClassificationEnsemble LearningFields of The World: A Field Guide for Extracting Agricultural Field Boundaries
Field boundary maps are a building block for agricultural data products and support crop monitoring, yield estimation, and disease estimation. This tutorial presents the Fields of The World (FTW) ecosystem: a benchmark o…
Crop ClassificationAn Efficient Remote Sensing Super Resolution Method Exploring Diffusion Priors and Multi-Modal Constraints for Crop Type Mapping
Super resolution offers a way to harness medium even lowresolution but historically valuable remote sensing image archives. Generative models, especially diffusion models, have recently been applied to remote sensing sup…
Crop Classification