Crop Classification

17 papers with code • 5 benchmarks • 4 datasets

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Most implemented papers

A Comparative Assessment of Multi-view fusion learning for Crop Classification

fmenat/multiviewcropclassification 10 Aug 2023

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.

End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping

marccoru/elects 30 Jan 2019

In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.

The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation

cropandweed/cropandweed-dataset Winter Conference on Applications of Computer Vision (WACV) 2023

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.

Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides

BMIRDS/deepslide 20 Nov 2018

Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results.

Spatio-temporal crop classification of low-resolution satellite imagery with capsule layers and distributed attention

JohnMBrandt/capsule-attention-networks 23 Apr 2019

Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing.

Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations

nandometzger/ODEcrop 4 Dec 2020

We propose to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences.

Crop mapping from image time series: deep learning with multi-scale label hierarchies

0zgur0/ms-convSTAR 17 Feb 2021

The three-level label hierarchy is encoded in a convolutional, recurrent neural network (convRNN), such that for each pixel the model predicts three labels at different level of granularity.

Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

felixquinton1/deep-crop-rotation 15 Oct 2021

While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping.

TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation

jnyborg/timematch 4 Nov 2021

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.

Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding

jnyborg/tpe 17 Mar 2022

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.