一种与地图服务结合的栅格瓦片计算模型

随着遥感影像数据的快速增长,对于栅格数据高效的信息处理和价值挖掘方式提出了更大的挑战,传统地图服务聚焦于内容的共享与可视化,缺乏对影像实时分析处理功能。本研究以地图服务的形式实现了对栅格瓦片数据实时分析处理能力,将云优化的GeoTIFF（Cloud Optimized GeoTIFF,COG）作为数据组织方式,设计了分布式协同预取策略,实现了栅格瓦片数据的冷热加载,优化了从云端读取影像数据的效率。在栅格瓦片数据高效加载的基础下,提出了一种基于表达式的栅格瓦片处理模型,通过对表达式转换建模为计算工作流,在地图服务的请求中实现对栅格瓦片的实时处理,对存储在云端的海量遥感数据进行快速分析,实现原始数据到数据产品的直接可视化转换。针对全量数据参与的场景,使用合适的重采样数据进行简化计算,以满足地图服务的实时性。使用了NDVI、地物分类、植被覆盖度三类不同复杂度模型,在地图服务中对Landsat 8影像进行了实时计算分析。实验结果表明,该处理模型能对栅格瓦片进行有效分析,且能进行分布式扩展,在高并发场景下能够提供稳定的地图服务能力,适应各层级尺度的计算,对未来地图服务的发展提供了一种新思路。

A Raster Tile Calculation Model Combined with Map Service

With the rapid growth of remote sensing data, greater challenges arise in raster data efficient processing and value mining. Traditional map services focus on content sharing and visualization, but lacking real-time image analysis and processing functions. In this study, the real-time analysis and processing capabilities of raster tile data are realized in the form of map service. The cloud optimized GeoTIFF (Cloud Optimized GeoTIFF, COG) is used as the data organization method. The distributed collaborative prefetching strategy is designed to realize the raster tile loading in a cold or hot way, which optimizes the efficiency of reading image data from the cloud. Based on the efficient raster tile data loading, an expression-based raster tile processing model is proposed. By converting the expression into a calculation workflow, the raster tile is processed in the request of the map service in real time. The massive remote sensing data stored in the cloud is quickly analyzed to realize the direct visual conversion from raw data to products. For scenarios where full data are involved, use appropriate resampling data to simplify calculations to meet the real-time performance of map services. Three types of different complexity models, NDVI, ground object classification, and fractional vegetation cover, are used to perform real-time calculation and analysis on Landsat 8 images in the map service. Experimental results show that the processing model can effectively analyze raster tiles, and can be extended in a distributed manner. It can provide stable map service capabilities in high-concurrency scenarios, adapt to calculations at various levels and scales, and contribute a new idea to the future development of map service.