Papers Crop Classification
“Crop Classification” 태그가 달린 논문 56편 · 필터 해제
Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
Earth observation (EO) applications involving complex and heterogeneous data sources are commonly approached with machine learning models. However, there is a common assumption that data sources will be persistently avai…
ClassificationClassification on Time Series with Missing DataCrop ClassificationEarth Observation+3Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning
Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing SAR and optical images can significantly enhance the accuracy of classificat…
Crop ClassificationGenerative Adversarial NetworkSynthetic Data GenerationCross Domain Early Crop Mapping using CropSTGAN
Driven by abundant satellite imagery, machine learning-based approaches have recently been promoted to generate high-resolution crop cultivation maps to support many agricultural applications. One of the major challenges…
Crop ClassificationGenerative Adversarial NetworkCan 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
Climate change is increasingly disrupting worldwide agriculture, making global food production less reliable. To tackle the growing challenges in feeding the planet, cutting-edge management strategies, such as precision …
Crop ClassificationCrop Type MappingImage SegmentationSemantic SegmentationPhytNet -- Tailored Convolutional Neural Networks for Custom Botanical Data
Automated disease, weed and crop classification with computer vision will be invaluable in the future of agriculture. However, existing model architectures like ResNet, EfficientNet and ConvNeXt often underperform on sma…
Crop ClassificationXAI for Early Crop Classification
We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods. Our approach consists of training a baseline crop classification model to carry out layer-wi…
ClassificationCrop ClassificationA Comparative Assessment of Multi-view fusion learning for Crop Classification
With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering the differences in resolution, magnitud…
Crop ClassificationMULTI-VIEW LEARNINGSensor FusionBoosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data
Accurate in-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patterns and their spatio-temporal variability…
Crop ClassificationData AugmentationDomain AdaptationTime Series+1Crop identification using deep learning on LUCAS crop cover photos
Crop classification via deep learning on ground imagery can deliver timely and accurate crop-specific information to various stakeholders. Dedicated ground-based image acquisition exercises can help to collect data in da…
Crop ClassificationDeep LearningLightweight, Pre-trained Transformers for Remote Sensing Timeseries
Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire. Self-supervision is a natural solution in settings …
Crop ClassificationSelf-Supervised LearningTransfer LearningThe CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation
Precision Agriculture and especially the application of automated weed intervention represents an increasingly essential research area, as sustainability and efficiency considerations are becoming more and more relevant.…
BenchmarkingCrop ClassificationCrop Yield PredictionDomain Adaptation+7Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series
Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution (FSR) remotely sensed imagery now offers great op…
ClassificationCrop ClassificationCrop Yield PredictionObject+2A Strategy Optimized Pix2pix Approach for SAR-to-Optical Image Translation Task
This technical report summarizes the analysis and approach on the image-to-image translation task in the Multimodal Learning for Earth and Environment Challenge (MultiEarth 2022). In terms of strategy optimization, cloud…
Crop ClassificationImage-to-Image TranslationTime Series AnalysisTime Series Classification+1Time Gated Convolutional Neural Networks for Crop Classification
This paper presented a state-of-the-art framework, Time Gated Convolutional Neural Network (TGCNN) that takes advantage of temporal information and gating mechanisms for the crop classification problem. Besides, several …
ClassificationCrop ClassificationEarth ObservationTime Series+2Bagged Polynomial Regression and Neural Networks
Series and polynomial regression are able to approximate the same function classes as neural networks. However, these methods are rarely used in practice, although they offer more interpretability than neural networks. I…
Crop ClassificationregressionActivation Regression for Continuous Domain Generalization with Applications to Crop Classification
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. In this paper, we model geographic generalisation in medium resolution Landsat-8 satellite imagery as …
Crop ClassificationDomain AdaptationDomain GeneralizationregressionA Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning
In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning. Sen4AgriNet dataset is annotated fr…
Crop ClassificationSemantic SegmentationTime Series AnalysisTampered VAE for Improved Satellite Image Time Series Classification
The unprecedented availability of spatial and temporal high-resolution satellite image time series (SITS) for crop type mapping is believed to necessitate deep learning architectures to accommodate challenges arising fro…
ClassificationCrop ClassificationCrop Type MappingGPU+3Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding
Large-scale crop type classification is a task at the core of remote sensing efforts with applications of both economic and ecological importance. Current state-of-the-art deep learning methods are based on self-attentio…
Crop ClassificationTime SeriesTime Series AnalysisTimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation
The recent developments of deep learning models that capture complex temporal patterns of crop phenology have greatly advanced crop classification from Satellite Image Time Series (SITS). However, when applied to target …
Crop ClassificationDomain AdaptationTime SeriesTime Series Analysis+1