Workflow for the Generation of Expert-Derived Training and Validation Data: A View to Global Scale Habitat Mapping
Workflow for the Generation of Expert-Derived Training and Validation Data: A View to Global Scale Habitat Mapping
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生成专家训练和验证数据的工作流程：全球规模栖息地测绘的视角
DOI：
10.3389/fmars.2021.643381
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发表时间：
2021
影响因子：
13.5
通讯作者：
S. Phinn
中科院分区：
文献类型：
--
作者：
C. Roelfsema;M. Lyons;N. Murray;E. Kovacs;E. Kennedy;Kathryn Markey;Rodney Borrego;Alexandra Ordoñez Alvarez;Chantel Say;Paul Tudman;M. Roe;J. Wolff;Dimosthenis Traganos;G. Asner;Brianna Bambic;Brian Free;H. Fox;Zoe Lieb;S. Phinn
在过去的十年里，我们在全球范围内完整、重复地绘制自然环境地图的能力大大提高了。这些进步来自于提供一系列在线全球卫星图像档案和全球尺度处理能力，以及改进的空间和时间分辨率卫星图像。由于缺乏协调的财政和人力资源，以及在全球空间范围内整理参考数据集的标准化方法，从我们称之为“参考数据集”的数据中准确训练和验证这些全球比例尺制图程序的能力具有挑战性。在这里，我们提出了一种专家驱动的方法，用于在全球范围内生成训练和验证数据，以绘制世界珊瑚礁的地图。首先将全球珊瑚礁划分为大致的生物地理区域，然后编制每个区域的参考数据集，其中包括现有的不同精度的点数据或地图。这些参考数据集是根据新的实地调查、对已发表调查的文献审查以及珊瑚礁监测和管理机构提供的个别资料汇编而成的。参考数据覆盖在每个区域的高空间分辨率卫星图像马赛克上（3.7 m × 3.7 m像素；Planet Dove）。此外，30到40个卫星图像瓦片；（20公里× 20公里），其中可获得参考数据和/或专家知识，并涵盖了具有代表性的生境范围。卫星图像块被分割成可解释的像素组，这些像素组通过专家解译手动标记为映射类别。标记的片段用于生成点来训练映射模型，并验证或评估准确性。我们提出的桌面参考数据创建工作流程扩展并扩展了传统的专家驱动解释方法，用于手动栖息地绘图和地图训练/验证。虽然我们的方法广泛适用于任何环境，但我们将参考数据创建方法应用于全球珊瑚礁测绘的背景下。透明的培训和验证过程对可用性至关重要，因为大数据为管理人员和科学家提供了更多机会，使他们能够将全球地图产品用于科学和保护脆弱和快速变化的生态系统。
Our ability to completely and repeatedly map natural environments at a global scale have increased significantly over the past decade. These advances are from delivery of a range of on-line global satellite image archives and global-scale processing capabilities, along with improved spatial and temporal resolution satellite imagery. The ability to accurately train and validate these global scale-mapping programs from what we will call “reference data sets” is challenging due to a lack of coordinated financial and personnel resourcing, and standardized methods to collate reference datasets at global spatial extents. Here, we present an expert-driven approach for generating training and validation data on a global scale, with the view to mapping the world’s coral reefs. Global reefs were first stratified into approximate biogeographic regions, then per region reference data sets were compiled that include existing point data or maps at various levels of accuracy. These reference data sets were compiled from new field surveys, literature review of published surveys, and from individually sourced contributions from the coral reef monitoring and management agencies. Reference data were overlaid on high spatial resolution satellite image mosaics (3.7 m × 3.7 m pixels; Planet Dove) for each region. Additionally, thirty to forty satellite image tiles; 20 km × 20 km) were selected for which reference data and/or expert knowledge was available and which covered a representative range of habitats. The satellite image tiles were segmented into interpretable groups of pixels which were manually labeled with a mapping category via expert interpretation. The labeled segments were used to generate points to train the mapping models, and to validate or assess accuracy. The workflow for desktop reference data creation that we present expands and up-scales traditional approaches of expert-driven interpretation for both manual habitat mapping and map training/validation. We apply the reference data creation methods in the context of global coral reef mapping, though our approach is broadly applicable to any environment. Transparent processes for training and validation are critical for usability as big data provide more opportunities for managers and scientists to use global mapping products for science and conservation of vulnerable and rapidly changing ecosystems.