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

arXiv 1901.10503 ↗First posted 29 Jan 2019eess.IV cs.CV cs.LGcited by 18

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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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2019.

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Citing papers per year: 2021: 6; 2022: 1; 2023: 3; 2024: 5; 2025: 2; 2026: 1.

18 papers.

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  1. 2021Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networkscited by 141
  2. 2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
  3. 2023ViTs for SITS: Vision Transformers for Satellite Image Time Seriescited by 55
  4. 2021Crop mapping from image time series: deep learning with multi-scale label hierarchiescited by 42
  5. 2024Deep Learning for Satellite Image Time Series Analysis: A Reviewcited by 13
  6. 2021Context-self contrastive pretraining for crop type semantic segmentationcited by 11
  7. 2021Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensemblescited by 9
  8. 2023Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Datacited by 8
  9. 2021Attention-augmented Spatio-Temporal Segmentation for Land Cover Mappingcited by 7
  10. 2021Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Seriescited by 5
  11. 2025Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCerealcited by 5
  12. 2024SITSMamba for Crop Classification based on Satellite Image Time Seriescited by 4
6 more
  1. 2022Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification: A surveycited by 2
  2. 2024In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Datacited by 1
  3. 2024VistaFormer: Scalable Vision Transformers for Satellite Image Time Series Segmentationcited by 1
  4. 2024XAI-Guided Enhancement of Vegetation Indices for Crop Mappingcited by 0
  5. 2025Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineationcited by 0
  6. 2026TEA: Temporal Adaptive Satellite Image Semantic Segmentationcited by 0