Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping

• URL: http://arxiv.org/abs/2105.02963v1
• Date: Sun, 2 May 2021 05:39:42 GMT
• Title: Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping
• Authors: Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia, Chenxi Lin, Zhenong Jin, Vipin Kumar
• Abstract summary: We introduce a novel architecture that incorporates the UNet structure with Bidirectional LSTM and Attention mechanism to jointly exploit the spatial and temporal nature of satellite data. We evaluate this method for mapping crops in multiple regions over the world.
• Score: 9.992909929182202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The availability of massive earth observing satellite data provide huge opportunities for land use and land cover mapping. However, such mapping effort is challenging due to the existence of various land cover classes, noisy data, and the lack of proper labels. Also, each land cover class typically has its own unique temporal pattern and can be identified only during certain periods. In this article, we introduce a novel architecture that incorporates the UNet structure with Bidirectional LSTM and Attention mechanism to jointly exploit the spatial and temporal nature of satellite data and to better identify the unique temporal patterns of each land cover. We evaluate this method for mapping crops in multiple regions over the world. We compare our method with other state-of-the-art methods both quantitatively and qualitatively on two real-world datasets which involve multiple land cover classes. We also visualise the attention weights to study its effectiveness in mitigating noise and identifying discriminative time period.

Related papers

• SpectralEarth: Training Hyperspectral Foundation Models at Scale [47.93167977587301]
  We introduce SpectralEarth, a large-scale multi-temporal dataset designed to pretrain hyperspectral foundation models. We pretrain a series of foundation models on SpectralEarth using state-of-the-art self-supervised learning (SSL) algorithms. We construct four downstream datasets for land-cover and crop-type mapping, providing benchmarks for model evaluation.
  arXiv  Detail & Related papers  (2024-08-15T22:55:59Z)
• Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping on Multispectral, Hyperspectral and High Spatial Aerial Imagery [0.0]
  In the rise of climate change, land cover mapping has become such an urgent need in environmental monitoring. This research implemented a semantic segmentation method such as Unet, Linknet, FPN, and PSPnet for categorizing vegetation, water, and others. The LinkNet model obtained high accuracy in IoU at 0.92 in all datasets, which is comparable with other mentioned techniques.
  arXiv  Detail & Related papers  (2024-06-20T11:40:12Z)
• Biological Valuation Map of Flanders: A Sentinel-2 Imagery Analysis [12.025312586542318]
  We present a densely labeled ground truth map of Flanders paired with Sentinel-2 satellite imagery. Our methodology includes a formalized dataset division and sampling method, utilizing the topographic map layout 'Kaartbladversnijdingen,' and a detailed semantic segmentation model training pipeline.
  arXiv  Detail & Related papers  (2024-01-26T22:21:39Z)
• A Self-Supervised Approach to Land Cover Segmentation [1.0878040851638]
  Land use/land cover change (LULC) maps are integral resources in earth science and agricultural research. Due to the nature of such maps, the creation of LULC maps is often constrained by the time and human resources necessary to accurately annotate satellite imagery and remote sensing data. Here, we demonstrate a self-supervised method of land cover segmentation that has no need for high-quality ground truth labels.
  arXiv  Detail & Related papers  (2023-10-27T16:37:36Z)
• 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)
• Satellite Image Time Series Analysis for Big Earth Observation Data [50.591267188664666]
  This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome.
  arXiv  Detail & Related papers  (2022-04-24T15:23:25Z)
• Activation Regression for Continuous Domain Generalization with Applications to Crop Classification [48.795866501365694]
  Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. We model geographic generalisation in medium resolution Landsat-8 satellite imagery as a continuous domain adaptation problem. We develop a dataset spatially distributed across the entire continental United States.
  arXiv  Detail & Related papers  (2022-04-14T15:41:39Z)
• 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)
• Detection of Deepfake Videos Using Long Distance Attention [73.6659488380372]
  Most existing detection methods treat the problem as a vanilla binary classification problem. In this paper, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. A spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective.
  arXiv  Detail & Related papers  (2021-06-24T08:33:32Z)
• Land Cover Mapping in Limited Labels Scenario: A Survey [13.162846466936994]
  Land cover mapping is essential for monitoring global environmental change and managing natural resources. Traditional classification models are plagued by limited training data available in existing land cover products. We provide a structured and comprehensive overview of challenges in land cover mapping and machine learning methods used to address these problems.
  arXiv  Detail & Related papers  (2021-03-03T14:33:29Z)
• Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation [4.549831511476249]
  We propose a new deep learning framework, named TASSEL, that is able to intelligently exploit the weak supervision provided by the coarse granularity labels. Our framework also produces an additional side-information that supports the model interpretability with the aim to make the black box gray.
  arXiv  Detail & Related papers  (2020-04-30T10:23:12Z)

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.