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UID:pretalx-nss-convention-2026-9YBURH@talks.caving.dev
DTSTART;TZID=EST:20260710T163500
DTEND;TZID=EST:20260710T170000
DESCRIPTION:Surface expression analysis of LiDAR elevation data has emerged
  as a powerful prospecting tool for cavers\, offering the ability to ident
 ify candidate entrances and karst features before committing to fieldwork.
  However\, the accuracy of automated detection methods has remained incons
 istent\, and the technical barrier to performing this analysis has kept it
  out of reach for most grottos and individual explorers.\n\nWe present two
  advances addressing both challenges. First\, a detection pipeline calibra
 ted against over 11\,000 verified cave entrances across Tennessee and Alab
 ama\, with scoring weights derived empirically from how well each method r
 anks known caves rather than set by intuition. Validated results show 75% 
 of known entrances detected within 50m of a generated candidate\, with app
 roximately 59% appearing in the top 10 ranked results for their search are
 a. We discuss where the pipeline succeeds\, where it fails\, and what the 
 failure modes reveal about the limits of terrain-based detection across di
 fferent geologies and entrance types.\n\nSecond\, these methods have been 
 packaged into a web-based platform that requires no GIS software\, no prog
 ramming knowledge\, and no data acquisition skills. A caver draws a search
  area on a map and receives a ranked list of GPS coordinates to investigat
 e. This removes the primary barrier that has kept LiDAR prospecting a nich
 e capability\, and opens it to the broader caving community.\n\nTogether\,
  these advances represent a step toward making LiDAR-based cave prospectin
 g both more reliable and more democratic.
DTSTAMP:20260826T232319Z
LOCATION:205
SUMMARY:Automated Cave Detection from LiDAR: Improved Methods and Broader A
 ccess - 
URL:https://talks.caving.dev/nss-convention-2026/talk/9YBURH/
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