SoilNet: An Attention-based Spatio-temporal Deep Learning Framework for Soil Organic Carbon Prediction with Digital Soil Mapping in Europe

• URL: http://arxiv.org/abs/2308.03586v2
• Date: Fri, 24 May 2024 09:54:03 GMT
• Title: SoilNet: An Attention-based Spatio-temporal Deep Learning Framework for Soil Organic Carbon Prediction with Digital Soil Mapping in Europe
• Authors: Nafiseh Kakhani, Moien Rangzan, Ali Jamali, Sara Attarchi, Seyed Kazem Alavipanah, Thomas Scholten,
• Abstract summary: Digital soil mapping (DSM) is an advanced approach that integrates statistical modeling and cutting-edge technologies. This study highlights the significance of spatial-temporal deep learning (DL) techniques within the DSM framework. A novel architecture is proposed, incorporating spatial information using a base convolutional neural network (CNN) model and spatial attention mechanism, along with climate temporal information.
• Score: 1.9736611005116438
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Digital soil mapping (DSM) is an advanced approach that integrates statistical modeling and cutting-edge technologies, including machine learning (ML) methods, to accurately depict soil properties and their spatial distribution. Soil organic carbon (SOC) is a crucial soil attribute providing valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. This study highlights the significance of spatial-temporal deep learning (DL) techniques within the DSM framework. A novel architecture is proposed, incorporating spatial information using a base convolutional neural network (CNN) model and spatial attention mechanism, along with climate temporal information using a long short-term memory (LSTM) network, for SOC prediction across Europe. The model utilizes a comprehensive set of environmental features, including Landsat-8 images, topography, remote sensing indices, and climate time series, as input features. Results demonstrate that the proposed framework outperforms conventional ML approaches like random forest commonly used in DSM, yielding lower root mean square error (RMSE). This model is a robust tool for predicting SOC and could be applied to other soil properties, thereby contributing to the advancement of DSM techniques and facilitating land management and decision-making processes based on accurate information.

Related papers

• GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction [15.87410077173391]
  We present GreenHyperSpectra, a pretraining dataset encompassing real-world cross-sensor and cross-ecosystem samples.<n>We successfully leverage GreenHyperSpectra to pretrain label-efficient multi-output regression models.<n>Our empirical analyses demonstrate substantial improvements in learning spectral representations for trait prediction.
  arXiv  Detail & Related papers  (2025-07-09T12:51:46Z)
• Private Training & Data Generation by Clustering Embeddings [74.00687214400021]
  Differential privacy (DP) provides a robust framework for protecting individual data.<n>We introduce a novel principled method for DP synthetic image embedding generation.<n> Empirically, a simple two-layer neural network trained on synthetically generated embeddings achieves state-of-the-art (SOTA) classification accuracy.
  arXiv  Detail & Related papers  (2025-06-20T00:17:14Z)
• 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. 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) 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)
• Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models [56.00251589760559]
  Large language models (LLMs) can act as gradient priors in a zero-shot setting. We introduce LM-GC, a novel method that integrates LLMs with arithmetic coding. Experiments indicate that LM-GC surpasses existing state-of-the-art lossless compression methods.
  arXiv  Detail & Related papers  (2024-09-26T13:38:33Z)
• Machine Learning for Methane Detection and Quantification from Space -- A survey [49.7996292123687]
  Methane (CH_4) is a potent anthropogenic greenhouse gas, contributing 86 times more to global warming than Carbon Dioxide (CO_2) over 20 years. This work expands existing information on operational methane point source detection sensors in the Short-Wave Infrared (SWIR) bands. It reviews the state-of-the-art for traditional as well as Machine Learning (ML) approaches.
  arXiv  Detail & Related papers  (2024-08-27T15:03:20Z)
• Enhancing Ecological Monitoring with Multi-Objective Optimization: A Novel Dataset and Methodology for Segmentation Algorithms [17.802456388479616]
  We introduce a unique semantic segmentation dataset of 6,096 high-resolution aerial images capturing indigenous and invasive grass species in Bega Valley, New South Wales, Australia. This dataset presents a challenging task due to the overlap and distribution of grass species. The dataset and code will be made publicly available, aiming to drive research in computer vision, machine learning, and ecological studies.
  arXiv  Detail & Related papers  (2024-07-25T18:27:27Z)
• Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks [62.12107686529827]
  This article highlights a significant shift towards leveraging quantum computing techniques in processing large volumes of remote sensing data. The proposed Quanv4EO model introduces a quanvolution method for preprocessing multi-dimensional EO data. Key findings suggest that the proposed model not only maintains high precision in image classification but also shows improvements of around 5% in EO use cases.
  arXiv  Detail & Related papers  (2024-07-24T09:11:34Z)
• Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines [0.0]
  This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft engines. We consider the well-known benchmark NASA Commercial Modular Aero-Propulsion System Simulation System (C-MAPSS) data as the main data for this paper. C-MAPSS is a well-studied dataset with much existing work in the literature that address RUL prediction with classical and deep learning methods.
  arXiv  Detail & Related papers  (2024-06-21T19:55:34Z)
• GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping [2.7998963147546148]
  We propose a self-supervised approach to train a backbone neural network in a self-supervised manner using unlabeled geospatial data alone. Our results demonstrate that self-supervision promotes robustness in learned features, improving prospectivity predictions. We leverage explainable artificial intelligence techniques to demonstrate that individual predictions can be interpreted from a geological perspective.
  arXiv  Detail & Related papers  (2024-06-18T16:24:28Z)
• MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and Attention-based Regression for Cloud Property Retrieval [4.24122904716917]
  This paper introduces MT-HCCAR, an end-to-end deep learning model employing multi-task learning to tackle cloud masking, cloud phase retrieval, and COT prediction. The MT-HCCAR integrates a hierarchical classification network (HC) and a classification-assisted attention-based regression network (CAR) to enhance precision and robustness in cloud labeling and COT prediction.
  arXiv  Detail & Related papers  (2024-01-29T19:50:50Z)
• 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)
• Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning [77.34726150561087]
  We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data. We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
  arXiv  Detail & Related papers  (2022-09-28T18:51:59Z)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)
• From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling [2.9897531698031403]
  We propose a differentiable parameter learning framework that efficiently learns a global mapping between inputs and parameters. As training data increases, dPL achieves better performance, more physical coherence, and better generalizability. We demonstrate examples that learned from soil moisture and streamflow, where dPL drastically outperformed existing evolutionary and regionalization methods.
  arXiv  Detail & Related papers  (2020-07-30T21:38:56Z)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)

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.