# Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series

arXiv: https://arxiv.org/abs/1901.10503 · HTML: https://mlsanity.com/paper/1901.10503/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2019.

## Built on by (most cited first, 18 of 18)

- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [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)
- [Crop mapping from image time series: deep learning with multi-scale label hierarchies](https://mlsanity.com/paper/2102.08820/) (2021)
- [Deep Learning for Satellite Image Time Series Analysis: A Review](https://mlsanity.com/paper/2404.03936/) (2024)
- [Context-self contrastive pretraining for crop type semantic segmentation](https://mlsanity.com/paper/2104.04310/) (2021)
- [Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles](https://mlsanity.com/paper/2111.13154/) (2021)
- [Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data](https://mlsanity.com/paper/2305.12011/) (2023)
- [Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping](https://mlsanity.com/paper/2105.02963/) (2021)
- [Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal](https://mlsanity.com/paper/2508.00858/) (2025)
- [Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series](https://mlsanity.com/paper/2110.08187/) (2021)
- [SITSMamba for Crop Classification based on Satellite Image Time Series](https://mlsanity.com/paper/2409.09673/) (2024)
- [Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification: A survey](https://mlsanity.com/paper/2204.08461/) (2022)
- [In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data](https://mlsanity.com/paper/2403.16582/) (2024)
- [VistaFormer: Scalable Vision Transformers for Satellite Image Time Series Segmentation](https://mlsanity.com/paper/2409.08461/) (2024)
- [XAI-Guided Enhancement of Vegetation Indices for Crop Mapping](https://mlsanity.com/paper/2407.08298/) (2024)
- [Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation](https://mlsanity.com/paper/2506.16318/) (2025)
- [TEA: Temporal Adaptive Satellite Image Semantic Segmentation](https://mlsanity.com/paper/2601.04956/) (2026)

## Abstract (arXiv)

In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respective importance of spatial and temporal structures in such data. With this objective, we consider several designs of convolutional, recurrent, and hybrid neural networks, and assess their performance on a large dataset of freely available Sentinel-2 imagery. We find that the best-performing approaches are hybrid configurations for which most of the parameters (up to 90%) are allocated to modeling the temporal structure of the data. Our results thus constitute a set of guidelines for the design of bespoke deep learning models for crop type classification.