Meta-learning For Few-Shot Time Series Crop Type Classification: A Benchmark On The EuroCropsML Dataset

• URL: http://arxiv.org/abs/2504.11022v1
• Date: Tue, 15 Apr 2025 09:47:57 GMT
• Title: Meta-learning For Few-Shot Time Series Crop Type Classification: A Benchmark On The EuroCropsML Dataset
• Authors: Joana Reuss, Jan Macdonald, Simon Becker, Konrad Schultka, Lorenz Richter, Marco Körner,
• Abstract summary: This study benchmarks transfer learning and several meta-learning algorithms, including (First-Order) Model-Agnostic Meta-Learning ((FO)-MAML), Almost No Inner Loop (ANIL), and Task-Informed Meta-Learning (TIML)<n>We find that the transfer of knowledge between geographically disparate regions, such as Estonia and Portugal, poses significant challenges to all investigated algorithms.
• Score: 9.126120966154119
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatial imbalances in crop type data pose significant challenges for accurate classification in remote sensing applications. Algorithms aiming at transferring knowledge from data-rich to data-scarce tasks have thus surged in popularity. However, despite their effectiveness in previous evaluations, their performance in challenging real-world applications is unclear and needs to be evaluated. This study benchmarks transfer learning and several meta-learning algorithms, including (First-Order) Model-Agnostic Meta-Learning ((FO)-MAML), Almost No Inner Loop (ANIL), and Task-Informed Meta-Learning (TIML), on the real-world EuroCropsML time series dataset, which combines farmer-reported crop data with Sentinel-2 satellite observations from Estonia, Latvia, and Portugal. Our findings indicate that MAML-based meta-learning algorithms achieve slightly higher accuracy compared to simpler transfer learning methods when applied to crop type classification tasks in Estonia after pre-training on data from Latvia. However, this improvement comes at the cost of increased computational demands and training time. Moreover, we find that the transfer of knowledge between geographically disparate regions, such as Estonia and Portugal, poses significant challenges to all investigated algorithms. These insights underscore the trade-offs between accuracy and computational resource requirements in selecting machine learning methods for real-world crop type classification tasks and highlight the difficulties of transferring knowledge between different regions of the Earth. To facilitate future research in this domain, we present the first comprehensive benchmark for evaluating transfer and meta-learning methods for crop type classification under real-world conditions. The corresponding code is publicly available at https://github.com/dida-do/eurocrops-meta-learning.

Related papers

• FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image Classification [4.245694283697248]
  This study involves two phases; the first is to conduct Lithology microscopic image classification on a small dataset using transfer learning.<n>In the second phase, we formulated the classification task to a Federated Transfer Learning scheme and proposed a Fine-Tuned Aggregation strategy for Federated Learning (FTA-FTL)<n>The results are in excellent agreement and confirm the efficiency of the proposed scheme, and show that the proposed FTA-FTL algorithm is capable enough to achieve approximately the same results obtained by the centralized implementation for Lithology microscopic images classification task.
  arXiv  Detail & Related papers  (2025-01-06T19:32:14Z)
• Detecting Training Data of Large Language Models via Expectation Maximization [62.28028046993391]
  We introduce EM-MIA, a novel membership inference method that iteratively refines membership scores and prefix scores via an expectation-maximization algorithm. EM-MIA achieves state-of-the-art results on WikiMIA.
  arXiv  Detail & Related papers  (2024-10-10T03:31:16Z)
• Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification [50.28867343337997]
  This work presents a TinyML-based semantic communication framework for few-shot wireless image classification. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks.
  arXiv  Detail & Related papers  (2024-09-03T05:56:55Z)
• Optimization Efficient Open-World Visual Region Recognition [55.76437190434433]
  RegionSpot integrates position-aware localization knowledge from a localization foundation model with semantic information from a ViL model. Experiments in open-world object recognition show that our RegionSpot achieves significant performance gain over prior alternatives.
  arXiv  Detail & Related papers  (2023-11-02T16:31:49Z)
• Bi-level Alignment for Cross-Domain Crowd Counting [113.78303285148041]
  Current methods rely on external data for training an auxiliary task or apply an expensive coarse-to-fine estimation. We develop a new adversarial learning based method, which is simple and efficient to apply. We evaluate our approach on five real-world crowd counting benchmarks, where we outperform existing approaches by a large margin.
  arXiv  Detail & Related papers  (2022-05-12T02:23:25Z)
• TIML: Task-Informed Meta-Learning for Agriculture [20.555341678693495]
  We build on previous work exploring the use of meta-learning for agricultural contexts in data-sparse regions. We introduce task-informed meta-learning (TIML), an augmentation to model-agnostic meta-learning which takes advantage of task-specific metadata.
  arXiv  Detail & Related papers  (2022-02-04T13:27:55Z)
• Generating meta-learning tasks to evolve parametric loss for classification learning [1.1355370218310157]
  In existing meta-learning approaches, learning tasks for training meta-models are usually collected from public datasets. We propose a meta-learning approach based on randomly generated meta-learning tasks to obtain a parametric loss for classification learning based on big data.
  arXiv  Detail & Related papers  (2021-11-20T13:07:55Z)
• Knowledge-Aware Meta-learning for Low-Resource Text Classification [87.89624590579903]
  This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks. We propose KGML to introduce additional representation for each sentence learned from the extracted sentence-specific knowledge graph.
  arXiv  Detail & Related papers  (2021-09-10T07:20:43Z)
• Recognition and Processing of NATOM [0.0]
  This paper shows how to process the NOTAM (Notice to Airmen) data of the field in civil aviation. For the original data of the NOTAM, there is a mixture of Chinese and English, and the structure is poor. Using Glove word vector methods to represent the data for using a custom mapping vocabulary.
  arXiv  Detail & Related papers  (2021-04-29T10:12:00Z)
• Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning [27.86606560170401]
  We present a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) We train the model to adapt to another domain by utilizing only a small amount of training data. Our model surpasses a transfer learning-based approach by up to 2-4 BLEU scores.
  arXiv  Detail & Related papers  (2020-10-18T17:54:13Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)
• A Survey on Large-scale Machine Learning [67.6997613600942]
  Machine learning can provide deep insights into data, allowing machines to make high-quality predictions. Most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. Large-scale Machine Learning aims to learn patterns from big data with comparable performance efficiently.
  arXiv  Detail & Related papers  (2020-08-10T06:07:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.