{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/detection-of-structural-change-in-geographic","title":"Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years","arxiv_id":"1803.11125","date":"2018-03-29","proceeding":null,"authors":["John M. Wandeto","Henry O. Nyongesa","Birgitta Dresp-Langley"],"abstract":"Time-series of satellite images may reveal important data about changes in\nenvironmental conditions and natural or urban landscape structures that are of\npotential interest to citizens, historians, or policymakers. We applied a fast\nmethod of image analysis using Self Organized Maps (SOM) and, more\nspecifically, the quantization error (QE), for the visualization of critical\nchanges in satellite images of Las Vegas, generated across the years 1984-2008,\na period of major restructuration of the urban landscape. As shown in our\nprevious work, the QE from the SOM output is a reliable measure of variability\nin local image contents. In the present work, we use statistical trend analysis\nto show how the QE from SOM run on specific geographic regions of interest\nextracted from satellite images can be exploited to detect both the magnitude\nand the direction of structural change across time at a glance. Significantly\ncorrelated demographic data for the same reference time period are highlighted.\nThe approach is fast and reliable, and can be implemented for the rapid\ndetection of potentially critical changes in time series of large bodies of\nimage data.","url_abs":"http://arxiv.org/abs/1803.11125v1","url_pdf":"http://arxiv.org/pdf/1803.11125v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"detection-of-structural-change-in-geographic","repo_url":"https://github.com/shrra/minisom","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[{"method_slug":"som","method_name":"SOM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}