Papers Crop Classification
“Crop Classification” 태그가 달린 논문 56편 · 필터 해제
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 ClassificationInvariant Features for Global Crop Type Classification
Accurate global crop type mapping supports agricultural monitoring and food security, yet remains limited by the scarcity of labeled data in many regions. A key challenge is enabling models trained in one geography to ge…
Crop ClassificationHieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Hierarchical land cover and land use (LCLU) classification aims to assign pixel-wise labels with multiple levels of semantic granularity to remote sensing (RS) imagery. However, existing deep learning-based methods face …
Crop ClassificationA Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic Segmentation
Satellite Image Time Series (SITS) is crucial for agricultural semantic segmentation. However, Cloud contamination introduces time gaps in SITS, disrupting temporal dependencies and causing feature shifts, leading to deg…
Crop ClassificationData AugmentationPredictionSemantic Segmentation+1MT-CYP-Net: Multi-Task Network for Pixel-Level Crop Yield Prediction Under Very Few Samples
Accurate and fine-grained crop yield prediction plays a crucial role in advancing global agriculture. However, the accuracy of pixel-level yield estimation based on satellite remote sensing data has been constrained by t…
Crop ClassificationCrop Yield PredictionPredictionYOLO-RS: Remote Sensing Enhanced Crop Detection Methods
With the rapid development of remote sensing technology, crop classification and health detection based on deep learning have gradually become a research hotspot. However, the existing target detection methods show poor …
Crop ClassificationTowards more efficient agricultural practices via transformer-based crop type classification
Machine learning has great potential to increase crop production and resilience to climate change. Accurate maps of where crops are grown are a key input to a number of downstream policy and research applications. In thi…
Crop ClassificationMeta-LearningTime SeriesSITSMamba for Crop Classification based on Satellite Image Time Series
Satellite image time series (SITS) data provides continuous observations over time, allowing for the tracking of vegetation changes and growth patterns throughout the seasons and years. Numerous deep learning (DL) approa…
ClassificationCrop ClassificationMambaTime SeriesOn the Generalizability of Foundation Models for Crop Type Mapping
Foundation models pre-trained using self-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, including language understanding, text generation, and image recognition. The E…
Crop ClassificationCrop Type MappingDisaster ResponseEarth Observation+4Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification
The increasing popularity of Artificial Intelligence in recent years has led to a surge in interest in image classification, especially in the agricultural sector. With the help of Computer Vision, Machine Learning, and …
Crop Classificationimage-classificationImage ClassificationInterpretable Machine Learning+1Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
Multi-sensor ML models for EO aim to enhance prediction accuracy by integrating data from various sources. However, the presence of missing data poses a significant challenge, particularly in non-persistent sensors that …
Air Quality InferenceCrop ClassificationEarth ObservationMULTI-VIEW LEARNINGXAI-Guided Enhancement of Vegetation Indices for Crop Mapping
Vegetation indices allow to efficiently monitor vegetation growth and agricultural activities. Previous generations of satellites were capturing a limited number of spectral bands, and a few expert-designed vegetation in…
Crop ClassificationFeature ImportanceCMTNet: Convolutional Meets Transformer Network for Hyperspectral Images Classification
Hyperspectral remote sensing (HIS) enables the detailed capture of spectral information from the Earth's surface, facilitating precise classification and identification of surface crops due to its superior spectral diagn…
ClassificationCrop ClassificationDiagnosticLow-Resource Crop Classification from Multi-Spectral Time Series Using Lossless Compressors
Deep learning has significantly improved the accuracy of crop classification using multispectral temporal data. However, these models have complex structures with numerous parameters, requiring large amounts of data and …
Crop ClassificationDeep LearningTime SeriesIn the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
Studying and analyzing cropland is a difficult task due to its dynamic and heterogeneous growth behavior. Usually, diverse data sources can be collected for its estimation. Although deep learning models have proven to ex…
Crop ClassificationMultimodal Deep LearningMULTI-VIEW LEARNINGSensor Fusion+1