# Time Gated Convolutional Neural Networks for Crop Classification

arXiv: https://arxiv.org/abs/2206.09756 · HTML: https://mlsanity.com/paper/2206.09756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Neural Controlled Differential Equations for Irregular Time Series](https://mlsanity.com/paper/2005.08926/) (2020)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [Gated Transformer Networks for Multivariate Time Series Classification](https://mlsanity.com/paper/2103.14438/) (2021)
- [XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2009.04796/) (2020)

## Built on by (most cited first, 1 of 1)

- [Plantation Monitoring Using Drone Images: A Dataset and Performance Review](https://mlsanity.com/paper/2502.08233/) (2025)

## Abstract (arXiv)

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 vegetation indices were constructed to expand dimensions of input data to take advantage of spectral information. Both spatial (channel-wise) and temporal (step-wise) correlation are considered in TGCNN. Specifically, our preliminary analysis indicates that step-wise information is of greater importance in this data set. Lastly, the gating mechanism helps capture high-order relationship. Our TGCNN solution achieves 0.973 F1 score, 0.977 AUC ROC and 0.948 IoU, respectively. In addition, it outperforms three other benchmarks in different local tasks (Kenya, Brazil and Togo). Overall, our experiments demonstrate that TGCNN is advantageous in this earth observation time series classification task.