Crop Classification
15 papers with code • 5 benchmarks • 4 datasets
Latest papers with no code
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
Deep learning models have proven to be effective for this task by mapping time series data to high-level representation for prediction.
Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning
In this research, We explore the effectiveness of conditional tabular generative adversarial network (CTGAN) as a synthetic data generation method based on a deep learning network, in addressing the challenge of limited training data for minority classes in crop classification using the fusion of SAR-optical data.
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
This paper attempts to highlight a use-case of state-of-the-art image segmentation models like SAM for crop-type mapping and related specific needs of the agriculture industry, offering a potential avenue for automatic, efficient, and cost-effective data products for precision agriculture practices.
PhytNet -- Tailored Convolutional Neural Networks for Custom Botanical Data
We address this gap with informed data collection and the development of a new CNN architecture, PhytNet.
XAI for Early Crop Classification
We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods.
Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data
To evaluate our approach, we release a new annotated dataset of 7. 4 million agricultural parcels in France and Netherlands.
Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series
The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery.
A 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).
Time Gated Convolutional Neural Networks for Crop Classification
Overall, our experiments demonstrate that TGCNN is advantageous in this earth observation time series classification task.
Tampered VAE for Improved Satellite Image Time Series Classification
We hope the proposed framework can serve as a baseline for crop classification with SITS for its modularity and simplicity.