Crop Classification
15 papers with code • 5 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Crop Classification
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CropAndWeed Dataset
EuroCrops
Sen4AgriNet
Latest papers
Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications
fmenat/missingviews-study-eo
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In this work, we assess the impact of missing temporal and static EO sources in trained models across four datasets with classification and regression tasks.
A Comparative Assessment of Multi-view fusion learning for Crop Classification
fmenat/multiviewcropclassification
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Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance.
Crop identification using deep learning on LUCAS crop cover photos
The aim of this paper is to select and publish a subset of LUCAS Cover photos for 12 mature major crops across the EU, to deploy, benchmark, and identify the best configuration of Mobile-net for the classification task, to showcase the possibility of using entropy-based metrics for post-processing of results, and finally to show the applications and limitations of the model in a practical and policy relevant context.
Lightweight, 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.
The 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.
Activation 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.
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning
orion-ai-lab/s4a
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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.
Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding
jnyborg/tpe
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Unlike previous positional encoding based on calendar time (e. g. day-of-year), TPE is based on thermal time, which is obtained by accumulating daily average temperatures over the growing season.
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation
However, when applied to target regions spatially different from the training region, these models perform poorly without any target labels due to the temporal shift of crop phenology between regions.
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
felixquinton1/deep-crop-rotation
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While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping.
