{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:35:23Z","timestamp":1775583323585,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871304"],"award-info":[{"award-number":["41871304"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Viewshed analysis is a terrain visibility computation method based on the digital elevation model (DEM). With the rapid growth of remote sensing and data collection technologies, the volume of large-scale raster DEM data has reached a great size (ZB). However, the data storage and GIS analysis based on such large-scale digital data volume become extra difficult. The usually distributed approaches based on Apache Hadoop and Spark can efficiently handle the viewshed analysis computation of large-scale DEM data, but there are still bottleneck and precision problems. In this article, we present a multi-level distributed XDraw (ML-XDraw) algorithm with Apache Spark to handle the viewshed analysis of large DEM data. The ML-XDraw algorithm mainly consists of 3 parts: (1) designing the XDraw algorithm into a multi-level distributed computing process, (2) introducing a multi-level data decomposition strategy to solve the calculating bottleneck problem of the cluster\u2019s executor, and (3) proposing a boundary approximate calculation strategy to solve the precision loss problem in calculation near the boundary. Experiments show that the ML-XDraw algorithm adequately addresses the above problems and achieves better speed-up and accuracy as the volume of raster DEM data increases drastically.<\/jats:p>","DOI":"10.3390\/rs15030761","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T10:19:28Z","timestamp":1675073968000},"page":"761","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Multi-Level Distributed Computing Approach to XDraw Viewshed Analysis Using Apache Spark"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4231-5049","authenticated-orcid":false,"given":"Junduo","family":"Dong","sequence":"first","affiliation":[{"name":"School of Geography Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5381-1024","authenticated-orcid":false,"given":"Jianbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geography Information Engineering, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","unstructured":"Larsen, M.V. 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