A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features

• URL: http://arxiv.org/abs/2503.07230v1
• Date: Mon, 10 Mar 2025 12:15:35 GMT
• Title: A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features
• Authors: Luigi Russo, Antonietta Sorriso, Silvia Liberata Ullo, Paolo Gamba,
• Abstract summary: The study focuses on three distinct regions - Amazonia, Africa, and Siberia - and evaluates the model performance across diverse ecoregions within these areas.<n>The results demonstrate the effectiveness and the capabilities of the proposed methodology in achieving overall accuracy (O.A.) values, even in regions with limited training data.
• Score: 1.907072234794597
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Land Cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. In this work, a novel approach combining a transformer-based Swin-Unet architecture with seasonal synthesized spatio-temporal images has been employed to classify LC types using spatio-temporal features extracted from Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data, organized into seasonal clusters. The study focuses on three distinct regions - Amazonia, Africa, and Siberia - and evaluates the model performance across diverse ecoregions within these areas. By utilizing seasonal feature sequences instead of dense temporal sequences, notable performance improvements have been achieved, especially in regions with temporal data gaps like Siberia, where S1 data distribution is uneven and non-uniform. The results demonstrate the effectiveness and the generalization capabilities of the proposed methodology in achieving high overall accuracy (O.A.) values, even in regions with limited training data.

Related papers

• Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition [63.55828203989405]
  We introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds.<n>Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures.<n>We propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training.
  arXiv  Detail & Related papers  (2025-06-26T11:53:59Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [55.09326865401653]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• 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)
• Pan-Arctic Permafrost Landform and Human-built Infrastructure Feature Detection with Vision Transformers and Location Embeddings [1.2895931807247418]
  Vision Transformers (ViTs) offer advantages in capturing long-range dependencies and global context via attention mechanisms.<n>ViTs support pretraining via self-supervised learning-addressing the common limitation of labeled data in Arctic feature detection.<n>This work investigates: (1) the suitability of pre-trained ViTs as feature extractors for high-resolution Arctic remote sensing tasks, and (2) the benefit of combining image and location embeddings.
  arXiv  Detail & Related papers  (2025-06-03T13:34:01Z)
• Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation [67.23953699167274]
  Self-supervised learning (SSL) has enabled the development of vision foundation models for Earth Observation (EO) In EO, this challenge is amplified by the redundancy and heavy-tailed distributions common in satellite imagery. We propose a dynamic dataset pruning strategy designed to improve SSL pre-training by maximizing dataset diversity and balance.
  arXiv  Detail & Related papers  (2025-04-09T15:13:26Z)
• PSDNorm: Test-Time Temporal Normalization for Deep Learning on EEG Signals [63.05435596565677]
  PSDNorm is a layer that leverages Monge mapping and temporal context to normalize feature maps in deep learning models.<n> PSDNorm achieves state-of-the-art performance at test time on datasets not seen during training.<n> PSDNorm provides a significant improvement in robustness, achieving markedly higher F1 scores for the 20% hardest subjects.
  arXiv  Detail & Related papers  (2025-03-06T16:20:25Z)
• Reconstructing Spatiotemporal Data with C-VAEs [49.1574468325115]
  Conditional continuous representation of moving regions is commonly used. In this work, we explore the capabilities of Conditional Varitemporal Autoencoder (C-VAE) models to generate realistic representations of regions' evolution.
  arXiv  Detail & Related papers  (2023-07-12T15:34:10Z)
• SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation [8.636014676778682]
  Open data is frequently released spatially aggregated, usually to comply with privacy policies. But coarse, heterogeneous aggregations complicate coherent learning and integration for downstream AI/ML systems. We propose an overarching model named Structurally-Aware Recurrent Network (SARN), which integrates structurally-aware spatial attention layers into the Gated Recurrent Unit (GRU) model. For scenarios with limited historical training data, we show that a model pre-trained on one city variable can be fine-tuned for another city variable using only a few hundred samples.
  arXiv  Detail & Related papers  (2023-06-09T21:01:29Z)
• 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)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery [74.82821342249039]
  We present SatMAE, a pre-training framework for temporal or multi-spectral satellite imagery based on Masked Autoencoder (MAE) To leverage temporal information, we include a temporal embedding along with independently masking image patches across time.
  arXiv  Detail & Related papers  (2022-07-17T01:35:29Z)
• Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification: A survey [0.0]
  Temporal CNNs have been employed for SITS classification tasks with encouraging results. This paper seeks to survey this method against a plethora of other contemporary methods for SITS classification to validate the existing findings in recent literature. Experiments are carried out on two benchmark SITS datasets with the results demonstrating that Temporal CNNs display a superior performance to the comparative benchmark algorithms.
  arXiv  Detail & Related papers  (2022-04-13T14:08:14Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization [30.203072945001136]
  In this work, a general architecture is first formulated probabilistically to extract domain invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMUSeasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision.
  arXiv  Detail & Related papers  (2020-09-16T14:43:22Z)
• Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset [1.0494061710470493]
  We explore the performance of DCNNs on semantic segmentation using spaceborne polarimetric synthetic aperture radar (PolSAR) datasets. The semantic segmentation task using PolSAR data can be categorized as weakly supervised learning when the characteristics of SAR data and data annotating procedures are factored in. Three DCNN models, including SegNet, U-Net, and LinkNet, are implemented next.
  arXiv  Detail & Related papers  (2020-03-30T17:32:49Z)

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.