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UID:pretalx-foss4gnl-2025-3VX3MY@talks.staging.osgeo.org
DTSTART;TZID=CET:20250703T115000
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DESCRIPTION:This presentation summarizes a thesis study which developed and
  compared Random Forest models for fish habitat mapping. The case study fo
 cused on a South Indian wildlife sanctuary inhabited by Humpback Mahseers\
 , a critically endangered fish species. \n\nThe workflow relied on open-so
 urce R packages\, and preprocessing included Sentinel-1 data for riverbed 
 delineation. Models were trained on PlanetScope (PS) spectral and textural
  features\, using training samples derived from UAV imagery and local expe
 rt knowledge. Three Random Forest models were trained on one river stretch
  and tested on two others\, using: (1) all PS bands and textures\, (2) onl
 y textures on all PS bands\, (3) red and near-infrared bands and textures.
  Validation data was derived from Google Earth Pro Airbus imagery. \n\nThe
  model trained only on textures achieved the best performance\, with 0.56 
 recall and precision\, and 0.89 overall accuracy. This research shows a re
 plicable and low-cost mapping workflow which provides local experts with a
 ccessible tools to interpret habitat maps and relate them to how endangere
 d species use them. While PS data was available in this study for research
  and educational use\, integrating additional open-source data - or identi
 fying suitable open alternatives - could enhance conservation efforts in d
 ata-scarce and resource-limited environments.
DTSTAMP:20260516T080907Z
LOCATION:Quantum 2
SUMMARY:A Case Study on Habitat Mapping for Endangered Fish Species - Franc
 esca Drăguț
URL:https://talks.staging.osgeo.org/foss4gnl-2025/talk/3VX3MY/
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