9 papers with code • 0 benchmarks • 2 datasets
These leaderboards are used to track progress in Crop Classification
Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides
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
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing.
We propose to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences.
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.
While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping.
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.
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.
A 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.
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions.