Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning

• URL: http://arxiv.org/abs/2506.17302v1
• Date: Tue, 17 Jun 2025 22:09:48 GMT
• Title: Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning
• Authors: Yijun Lin, Theresa Chen, Colby Brungard, Grunwald Sabine, Sue Ives, Matt Macander, Timm Nawrocki, Yao-Yi Chiang, Nic Jelinski,
• Abstract summary: High-resolution soil maps are essential for characterizing permafrost distribution, identifying vulnerable areas, and informing adaptation strategies.<n>We present MISO, a vision-based machine learning (ML) model to produce statewide fine-scale soil maps for near-surface permafrost and soil taxonomy.<n>We compare MISO with Random Forest (RF), a traditional ML model that has been widely used in soil mapping applications.
• Score: 1.4786253394033289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-scale soil mapping in Alaska, traditionally relying on fieldwork and localized simulations, remains a critical yet underdeveloped task, despite the region's ecological importance and extensive permafrost coverage. As permafrost thaw accelerates due to climate change, it threatens infrastructure stability and key ecosystem services, such as soil carbon storage. High-resolution soil maps are essential for characterizing permafrost distribution, identifying vulnerable areas, and informing adaptation strategies. We present MISO, a vision-based machine learning (ML) model to produce statewide fine-scale soil maps for near-surface permafrost and soil taxonomy. The model integrates a geospatial foundation model for visual feature extraction, implicit neural representations for continuous spatial prediction, and contrastive learning for multimodal alignment and geo-location awareness. We compare MISO with Random Forest (RF), a traditional ML model that has been widely used in soil mapping applications. Spatial cross-validation and regional analysis across Permafrost Zones and Major Land Resource Areas (MLRAs) show that MISO generalizes better to remote, unseen locations and achieves higher recall than RF, which is critical for monitoring permafrost thaw and related environmental processes. These findings demonstrate the potential of advanced ML approaches for fine-scale soil mapping and provide practical guidance for future soil sampling and infrastructure planning in permafrost-affected landscapes. The project will be released at https://github.com/knowledge-computing/Peatland-permafrost.

Related papers

• TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation [65.74990259650984]
  We introduce TerraFM, a scalable self-supervised learning model that leverages globally distributed Sentinel-1 and Sentinel-2 imagery.<n>Our training strategy integrates local-global contrastive learning and introduces a dual-centering mechanism.<n>TerraFM achieves strong generalization on both classification and segmentation tasks, outperforming prior models on GEO-Bench and Copernicus-Bench.
  arXiv  Detail & Related papers  (2025-06-06T17:59:50Z)
• Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
  Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
  arXiv  Detail & Related papers  (2025-05-21T03:57:10Z)
• Leveraging Land Cover Priors for Isoprene Emission Super-Resolution [15.868193361155656]
  This study contributes to atmospheric chemistry and climate modeling by providing a cost-effective, data-driven approach to refining BVOC emission maps.<n>The proposed method enhances the usability of satellite-based emissions data, supporting applications in air quality forecasting, climate impact assessments, and environmental studies.
  arXiv  Detail & Related papers  (2025-03-24T13:23:46Z)
• EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis [0.31077024712075796]
  We introduce EarthScape, a novel, AI-ready multimodal dataset for surficial geologic mapping and Earth surface analysis.<n>EarthScape integrates high-resolution aerial RGB and near-infrared (NIR) imagery, digital elevation models (DEM), multi-scale DEM-derived terrain features, and hydrologic and infrastructure vector data.<n>As a living dataset with a vision for expansion, EarthScape bridges the gap between computer vision and Earth sciences.
  arXiv  Detail & Related papers  (2025-03-19T18:23:48Z)
• Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning [4.414885369283509]
  Given inputs of diverse soil characteristics and climate data, we aimed to build a model to predict accurate land emissions.<n>SDSA-KGML models achieve higher local accuracy for the specified states in the Midwest Region.
  arXiv  Detail & Related papers  (2025-02-20T18:52:24Z)
• PEACE: Empowering Geologic Map Holistic Understanding with MLLMs [64.58959634712215]
  Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface.<n>Despite their significance, current Multimodal Large Language Models (MLLMs) often fall short in geologic map understanding.<n>To quantify this gap, we construct GeoMap-Bench, the first-ever benchmark for evaluating MLLMs in geologic map understanding.
  arXiv  Detail & Related papers  (2025-01-10T18:59:42Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa [3.6826233660285395]
  locust swarms present a major threat to agriculture and food security. Our study develops an operationally-ready model for predicting locust breeding grounds.
  arXiv  Detail & Related papers  (2024-03-11T16:13:58Z)
• 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)
• Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar [14.07306593230776]
  This paper presents the first high-resolution canopy height map concurrently produced for multiple sub-national jurisdictions. The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020. We also introduce a post-processing step using a convolutional network trained on GEDI observations.
  arXiv  Detail & Related papers  (2023-04-14T15:52:57Z)
• Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification [61.44538721707377]
  We present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery. We observe significant improvements up to 25% absolute mIoU when pre-trained with our proposed method. We find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme.
  arXiv  Detail & Related papers  (2022-03-11T16:14:14Z)
• OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing [66.23463054467653]
  We present a review of recent methods based on machine learning to improve and use OpenStreetMap data. We believe that OSM can change the way we interpret remote sensing data and that the synergy with machine learning can scale participatory map making.
  arXiv  Detail & Related papers  (2020-07-13T09:58:14Z)

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.