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UID:pretalx-foss4g-2022-JSC7EV@talks.staging.osgeo.org
DTSTART;TZID=CET:20220825T174500
DTEND;TZID=CET:20220825T181500
DESCRIPTION:Three dimensional representations of surface terrain and struct
 ure is essential for a range of widespread applications and forms a base d
 ataset that underlies many decision making processes. A few examples inclu
 de land use planning\, areal overview\, operational analysis\, emergency h
 andling\, route and transport planning\, geographical and meteorological m
 odelling etc. Recently\, the Norwegian Government and the Norwegian Mappin
 g Authority tasked the acquisition of high resolution Light Detection and 
 Ranging (LIDAR) data covering the entire mainland with a minimum of 2 poin
 t measurements per meter. In addition\, all aerial lidar acquisitions that
  were tasked by the government since the early 2000s are also publically a
 vailable for download. In this work using FOSS\, we discuss the height acc
 uracy of ground classified datasets (i.e. Digital Terrain Models\, Digital
  Surface Models) with varying original acquisition ground point densities.
  We create classification pipelines that allow us to calculate derivative 
 products such as a “normalized” vegetation density and further compare
  these over time. This work in progress discusses our experience with open
  source tools on open source data and some of the challenges we encountere
 d scaling our methods for big data.
DTSTAMP:20260405T074050Z
LOCATION:Room Verde
SUMMARY:Lidar classification\, accuracy and change detection using the Norw
 egian open lidar data archive. - Christopher Nuth
URL:https://talks.staging.osgeo.org/foss4g-2022/talk/JSC7EV/
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