Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery
Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery
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DOI：
10.3390/rs14112719
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发表时间：
2022-06
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影响因子：
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通讯作者：
E. Manos;C. Witharana;M. Udawalpola;Amit Hasan;A. Liljedahl
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作者：
E. Manos;C. Witharana;M. Udawalpola;Amit Hasan;A. Liljedahl
快速的全球变暖正在催化北极大范围的永久冻土退化，导致破坏性的地面下沉，破坏地面的稳定和变形。因此，在永久冻土上建造的人工基础设施目前面临着结构失效的主要风险。试图研究这一问题的风险评估框架假定有关基础设施的位置和范围的准确信息是已知的。然而，缺乏完整、高质量、统一的已建成基础设施的地理空间数据集，这些数据集随时可用于此类科学研究。虽然利用图像绘制地图可以填补这一知识空白，但北极单个结构的小面积和广阔的地理范围需要大量非常高空间分辨率的遥感图像。要将这些“大”图像数据转换为“科学就绪”的信息，需要由先进的计算机视觉算法驱动的高度自动化的图像分析管道。尽管如此，以前的精细分辨率研究一直局限于对局部受限尺度上的要素进行手动数字化。因此，这项探索性研究是对亚米级空间分辨率卫星图像的全自动分析的第一次调查，用于自动检测北极建造的基础设施。我们的任务是U-Net，一个基于深度学习的语义分割模型，从阿拉斯加乌特恰格维克和普拉德霍湾的商业卫星图像中对不同的基础设施类型(住宅、商业、公共和工业建筑以及道路)进行分类。我们还进行了一个系统的实验，以了解当标记的训练数据有限时，图像增强如何影响模型的性能。当应用优化的增强方法时，U-net的平均F1得分为0.83。总体而言，我们的实验结果表明，基于U-Net的工作流是一种很有前途的北极建筑基础设施自动检测方法，与现有的优化工作流(如Maple)相结合，可以扩展到绘制跨越泛北极的多种基础设施类型。
Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is currently at major risk of structural failure. Risk assessment frameworks that attempt to study this issue assume that precise information on the location and extent of infrastructure is known. However, complete, high-quality, uniform geospatial datasets of built infrastructure that are readily available for such scientific studies are lacking. While imagery-enabled mapping can fill this knowledge gap, the small size of individual structures and vast geographical extent of the Arctic necessitate large volumes of very high spatial resolution remote sensing imagery. Transforming this ‘big’ imagery data into ‘science-ready’ information demands highly automated image analysis pipelines driven by advanced computer vision algorithms. Despite this, previous fine resolution studies have been limited to manual digitization of features on locally confined scales. Therefore, this exploratory study serves as the first investigation into fully automated analysis of sub-meter spatial resolution satellite imagery for automated detection of Arctic built infrastructure. We tasked the U-Net, a deep learning-based semantic segmentation model, with classifying different infrastructure types (residential, commercial, public, and industrial buildings, as well as roads) from commercial satellite imagery of Utqiagvik and Prudhoe Bay, Alaska. We also conducted a systematic experiment to understand how image augmentation can impact model performance when labeled training data is limited. When optimal augmentation methods were applied, the U-Net achieved an average F1 score of 0.83. Overall, our experimental findings show that the U-Net-based workflow is a promising method for automated Arctic built infrastructure detection that, combined with existing optimized workflows, such as MAPLE, could be expanded to map a multitude of infrastructure types spanning the pan-Arctic.