# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

arXiv: https://arxiv.org/abs/1811.10166 · HTML: https://mlsanity.com/paper/1811.10166/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 30 of 76)

- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [Lightweight, Pre-trained Transformers for Remote Sensing Timeseries](https://mlsanity.com/paper/2304.14065/) (2023)
- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [ViTs for SITS: Vision Transformers for Satellite Image Time Series](https://mlsanity.com/paper/2301.04944/) (2023)
- [Transformers in Time-series Analysis: A Tutorial](https://mlsanity.com/paper/2205.01138/) (2022)
- [Crop mapping from image time series: deep learning with multi-scale label hierarchies](https://mlsanity.com/paper/2102.08820/) (2021)
- [Dynamic Sparse Network for Time Series Classification: Learning What to "see''](https://mlsanity.com/paper/2212.09840/) (2022)
- [TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation](https://mlsanity.com/paper/2111.02682/) (2021)
- [A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning](https://mlsanity.com/paper/2204.00951/) (2022)
- [Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis](https://mlsanity.com/paper/2408.07314/) (2024)
- [Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding](https://mlsanity.com/paper/2203.09175/) (2022)
- [A Novel CNN-LSTM-based Approach to Predict Urban Expansion](https://mlsanity.com/paper/2103.01695/) (2021)
- [Deep Learning for Satellite Image Time Series Analysis: A Review](https://mlsanity.com/paper/2404.03936/) (2024)
- [Score-oriented loss (SOL) functions](https://mlsanity.com/paper/2103.15522/) (2021)
- [Context-self contrastive pretraining for crop type semantic segmentation](https://mlsanity.com/paper/2104.04310/) (2021)
- [Pixel-wise Agricultural Image Time Series Classification: Comparisons and a Deformable Prototype-based Approach](https://mlsanity.com/paper/2303.12533/) (2023)
- [Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data](https://mlsanity.com/paper/2305.12011/) (2023)
- [AiTLAS: Artificial Intelligence Toolbox for Earth Observation](https://mlsanity.com/paper/2201.08789/) (2022)
- [Location-aware Adaptive Normalization: A Deep Learning Approach For Wildfire Danger Forecasting](https://mlsanity.com/paper/2212.08208/) (2022)
- [A transformer-based model for default prediction in mid-cap corporate markets](https://mlsanity.com/paper/2111.09902/) (2021)
- [Revisiting the Encoding of Satellite Image Time Series](https://mlsanity.com/paper/2305.02086/) (2023)
- [Paving the way toward foundation models for irregular and unaligned Satellite Image Time Series](https://mlsanity.com/paper/2407.08448/) (2024)
- [MONSTER: Monash Scalable Time Series Evaluation Repository](https://mlsanity.com/paper/2502.15122/) (2025)
- [Satellite Image Time Series Analysis for Big Earth Observation Data](https://mlsanity.com/paper/2204.11301/) (2022)
- [Cross Domain Early Crop Mapping using CropSTGAN](https://mlsanity.com/paper/2401.07398/) (2024)
- [SITSMamba for Crop Classification based on Satellite Image Time Series](https://mlsanity.com/paper/2409.09673/) (2024)
- [DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications](https://mlsanity.com/paper/2502.17066/) (2025)
- [Towards Space-to-Ground Data Availability for Agriculture Monitoring](https://mlsanity.com/paper/2205.07721/) (2022)
- [Semi-supervised multiscale dual-encoding method for faulty traffic data detection](https://mlsanity.com/paper/2212.13596/) (2022)

## Abstract (arXiv)

New remote sensing sensors now acquire high spatial and spectral Satellite Image Time Series (SITS) of the world. These series of images are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth's surfaces. More specifically, the combination of the temporal, spectral and spatial resolutions of new SITS makes possible to monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied for SITS classification, these algorithms do not make the most of the temporal domain. Conversely, some approaches that take into account the temporal dimension have recently been tested, especially Recurrent Neural Networks (RNNs). This paper proposes an exhaustive study of another deep learning approaches, namely Temporal Convolutional Neural Networks (TempCNNs) where convolutions are applied in the temporal dimension. The goal is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification. This paper proposes a set of experiments performed on one million time series extracted from 46 Formosat-2 images. The experimental results show that TempCNNs are more accurate than RF and RNNs, that are the current state of the art for SITS classification. We also highlight some differences with results obtained in computer vision, e.g. about pooling layers. Moreover, we provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size. Finally, we assess the visual quality of the land cover maps produced by TempCNNs.