MAPunet: High-resolution snow depth mapping through U-Net pixel-wise regression

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Alejandro Betato , Hernán Díaz Rodríguez , Niamh French , Thomas James , Beatriz Remeseiro
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引用次数: 0

Abstract

Accurate snow depth prediction is essential for hydrological risk assessment, flood prediction, water resource management, and weather forecasting. While previous studies have successfully applied deep learning techniques to generate snow depth maps, many have been constrained by geographical coverage or low data resolution. This work addresses these limitations by integrating high-resolution LiDAR maps, satellite imagery, digital elevation models, and three novel time-dependent variables. Additionally, the well-known U-Net architecture has been customized to perform pixel-wise regression and accurately predict snow depth over large geographic areas. The proposed method, called MAPunet, effectively models snow depth in the mountainous region of Davos, achieving an average error of 0.62 m at a 5 m resolution. The experimental results demonstrate the potential of combining high-resolution data with advanced deep learning techniques for enhanced snow depth mapping.
MAPunet:通过U-Net逐像素回归绘制高分辨率雪深图
准确的雪深预测对于水文风险评估、洪水预测、水资源管理和天气预报至关重要。虽然以前的研究已经成功地应用了深度学习技术来生成雪深图,但许多研究都受到地理覆盖或低数据分辨率的限制。这项工作通过集成高分辨率激光雷达地图、卫星图像、数字高程模型和三个新的时间相关变量来解决这些限制。此外,众所周知的U-Net架构已被定制,以执行逐像素回归并准确预测大地理区域的雪深。这种被称为MAPunet的方法有效地模拟了达沃斯山区的雪深,在5米分辨率下实现了0.62米的平均误差。实验结果表明,将高分辨率数据与先进的深度学习技术相结合,可以增强雪深测绘。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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