对比Zarr与TIFF在地理空间图像处理中的性能差异。
Performance Evaluation of Geospatial Images based on Zarr and Tiff
- 用数据分块和压缩提升存储效率，对比两种格式的读写速度。
- Zarr在大尺度数据下存储效率更高，访问速度更快。
- 适合需要高效处理海量地理数据的研究者或系统设计者。
本文评估了基于Zarr和TIFF两种数据存储格式在地理空间图像处理中的性能表现。地理空间图像广泛应用于环境监测、城市规划和灾害管理等领域。传统TIFF格式因结构简单、兼容性强而被普遍使用，但在处理大规模数据时存在性能瓶颈。Zarr是一种专为云环境设计的新格式，支持数据分块与压缩，具备良好的可扩展性。本研究通过一系列典型地理空间处理任务，比较了两种格式在存储效率、访问速度和计算性能方面的表现。基于多个地理空间数据集的分析表明，Zarr在大规模数据场景下具有显著优势，尤其在存储密度和读取速度方面优于TIFF；同时，也揭示了其在部分场景下的局限性。结果为用户根据实际需求选择合适的数据格式提供了实践依据。
原文摘要 · Abstract (English)
This evaluate the performance of geospatial image processing using two distinct data storage formats: Zarr and TIFF. Geospatial images, converted to numerous applications like environmental monitoring, urban planning, and disaster management. Traditional Tagged Image File Format is mostly used because it is simple and compatible but may lack by performance limitations while working on large datasets. Zarr is a new format designed for the cloud systems,that offers scalability and efficient storage with data chunking and compression techniques. This study compares the two formats in terms of storage efficiency, access speed, and computational performance during typical geospatial processing tasks. Through analysis on a range of geospatial datasets, this provides details about the practical advantages and limitations of each format,helping users to select the appropriate format based on their specific needs and constraints.
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