# Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

arXiv: https://arxiv.org/abs/1802.02080 · HTML: https://mlsanity.com/paper/1802.02080/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 30 of 61)

- [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)
- [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)
- [Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding](https://mlsanity.com/paper/2203.09175/) (2022)
- [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)
- [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)
- [Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification](https://mlsanity.com/paper/2203.06041/) (2022)
- [Revisiting the Encoding of Satellite Image Time Series](https://mlsanity.com/paper/2305.02086/) (2023)
- [FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping](https://mlsanity.com/paper/2506.07080/) (2025)
- [Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series](https://mlsanity.com/paper/2110.08187/) (2021)
- [Satellite Image Time Series Analysis for Big Earth Observation Data](https://mlsanity.com/paper/2204.11301/) (2022)
- [Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery](https://mlsanity.com/paper/2208.11607/) (2022)
- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series](https://mlsanity.com/paper/2505.08723/) (2025)
- [Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution](https://mlsanity.com/paper/2208.10838/) (2022)
- [A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture](https://mlsanity.com/paper/2210.01272/) (2022)
- [SITSMamba for Crop Classification based on Satellite Image Time Series](https://mlsanity.com/paper/2409.09673/) (2024)
- [Exact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation](https://mlsanity.com/paper/2412.03968/) (2024)
- [Empirical Study of PEFT techniques for Winter Wheat Segmentation](https://mlsanity.com/paper/2310.01825/) (2023)
- [SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping](https://mlsanity.com/paper/2608.09497/) (2026)
- [DeepSatData: Building large scale datasets of satellite images for training machine learning models](https://mlsanity.com/paper/2104.13824/) (2021)
- [Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification: A survey](https://mlsanity.com/paper/2204.08461/) (2022)
- [Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities](https://mlsanity.com/paper/2311.04491/) (2023)
- [Cloud gap-filling with deep learning for improved grassland monitoring](https://mlsanity.com/paper/2403.09554/) (2024)

## Abstract (arXiv)

Earth observation (EO) sensors deliver data with daily or weekly temporal resolution. Most land use and land cover (LULC) approaches, however, expect cloud-free and mono-temporal observations. The increasing temporal capabilities of today's sensors enables the use of temporal, along with spectral and spatial features. Domains, such as speech recognition or neural machine translation, work with inherently temporal data and, today, achieve impressive results using sequential encoder-decoder structures. Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize internal activations over a sequence of cloudy and non-cloudy images and find several recurrent cells, which reduce the input activity for cloudy observations. Hence, we assume that our network has learned cloud-filtering schemes solely from input data, which could alleviate the need for tedious cloud-filtering as a preprocessing step for many EO approaches. Moreover, using unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, we achieved in our experiments state-of-the-art classification accuracies on a large number of crop classes with minimal preprocessing compared to other classification approaches.