LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping

• URL: http://arxiv.org/abs/2502.20139v2
• Date: Tue, 20 May 2025 13:33:44 GMT
• Title: LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping
• Authors: J. Schmidinger, S. Vogel, V. Barkov, A. -D. Pham, R. Gebbers, H. Tavakoli, J. Correa, T. R. Tavares, P. Filippi, E. J. Jones, V. Lukas, E. Boenecke, J. Ruehlmann, I. Schroeter, E. Kramer, S. Paetzold, M. Kodaira, A. M. J. -C. Wadoux, L. Bragazza, K. Metzger, J. Huang, D. S. M. Valente, J. L. Safanelli, E. L. Bottega, R. S. D. Dalmolin, C. Farkas, A. Steiger, T. Z. Horst, L. Ramirez-Lopez, T. Scholten, F. Stumpf, P. Rosso, M. M. Costa, R. S. Zandonadi, J. Wetterlind, M. Atzmueller,
• Abstract summary: Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods.<n>LimeSoDa consists of 31 field- and farm-scale datasets from various countries.<n>We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number (< 20) of features. These benchmarking results illustrate that the performance of a method is highly context-dependent. LimeSoDa therefore provides an important resource for improving the development and evaluation of statistical methods in DSM.

Related papers

• Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning [53.527506374566485]
  We propose a novel Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning cluster framework, namely AR-DBSCAN.<n>We show that AR-DBSCAN not only improves clustering accuracy by up to 144.1% and 175.3% in the NMI and ARI metrics, respectively, but also is capable of robustly finding dominant parameters.
  arXiv  Detail & Related papers  (2025-05-07T11:37:23Z)
• CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training [63.07024608399447]
  We propose an automated framework that discovers, evaluates, and refines data mixtures in a pre-training setting. We introduce ClimbLab, a filtered 1.2-trillion-token corpus with 20 clusters as a research playground, and ClimbMix, a compact yet powerful 400-billion-token dataset.
  arXiv  Detail & Related papers  (2025-04-17T17:58:13Z)
• Adaptive Dataset Quantization [2.0105434963031463]
  We introduce a versatile framework for dataset compression, namely Adaptive dataset Quantization (ADQ)<n>We propose a novel adaptive sampling strategy through the evaluation of generated bins' representativeness score, diversity score and importance score.<n>Our method not only exhibits superior generalization capability across different architectures, but also attains state-of-the-art results, surpassing DQ by average 3% on various datasets.
  arXiv  Detail & Related papers  (2024-12-22T07:08:29Z)
• Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning [56.795078085234195]
  LLM pruning approaches universally rely on the C4 dataset as the calibration data for calculating pruning scores. In this study, we evaluate the choice of calibration data on LLM pruning, across a wide range of datasets. Our results also uncover several subtle and often unexpected findings.
  arXiv  Detail & Related papers  (2024-10-09T22:00:19Z)
• A Framework for Fine-Tuning LLMs using Heterogeneous Feedback [69.51729152929413]
  We present a framework for fine-tuning large language models (LLMs) using heterogeneous feedback. First, we combine the heterogeneous feedback data into a single supervision format, compatible with methods like SFT and RLHF. Next, given this unified feedback dataset, we extract a high-quality and diverse subset to obtain performance increases.
  arXiv  Detail & Related papers  (2024-08-05T23:20:32Z)
• Minimally Supervised Learning using Topological Projections in Self-Organizing Maps [55.31182147885694]
  We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU) Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
  arXiv  Detail & Related papers  (2024-01-12T22:51:48Z)
• DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets [34.780870585656395]
  We propose dataset-Aware Mixture-of-Experts, DAMEX. We train the experts to become an expert' of a dataset by learning to route each dataset tokens to its mapped expert. Experiments on Universal Object-Detection Benchmark show that we outperform the existing state-of-the-art.
  arXiv  Detail & Related papers  (2023-11-08T18:55:24Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction [2.554658234030785]
  This study introduces a novel approach that aims to learn the geographical link between multimodal features via self-supervised contrastive learning. The proposed approach has undergone rigorous testing on two distinct large-scale datasets.
  arXiv  Detail & Related papers  (2023-08-07T13:44:44Z)
• Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model [36.993630058695345]
  We propose a shared and specific feature learning (S2FL) model to decomposing multimodal RS data into modality-shared and modality-specific components. To better assess multimodal baselines and the newly-proposed S2FL model, three multimodal RS benchmark datasets, i.e., Houston2013 -- hyperspectral and multispectral data, Berlin -- hyperspectral and synthetic aperture radar (SAR) data, Augsburg -- hyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification.
  arXiv  Detail & Related papers  (2021-05-21T08:14:21Z)
• Shared Space Transfer Learning for analyzing multi-site fMRI data [83.41324371491774]
  Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data. MVPA works best with a well-designed feature set and an adequate sample size. Most fMRI datasets are noisy, high-dimensional, expensive to collect, and with small sample sizes. This paper proposes the Shared Space Transfer Learning (SSTL) as a novel transfer learning approach.
  arXiv  Detail & Related papers  (2020-10-24T08:50:26Z)
• Feature Ranking for Semi-supervised Learning [3.1380888953704984]
  We propose semi-supervised learning of feature ranking. To the best of our knowledge, this is the first work that treats the task of feature ranking within the semi-supervised structured output prediction context. The evaluation across 38 benchmark datasets reveals the following: Random Forests perform the best for the classification-like tasks, while for the regression-like tasks Extra-PCTs perform the best.
  arXiv  Detail & Related papers  (2020-08-10T07:50:50Z)

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.