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Author (up) Schmidt-Colberg, A.; Löffler-Dauth, L. pdf  doi
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  Title A Human-Centric Evaluation Dataset for Automated Early Wildfire Detection from a Causal Perspective Type Conference Article
  Year 2023 Publication Proceedings of the 20th International ISCRAM Conference Abbreviated Journal Iscram 2023  
  Volume Issue Pages 933-943  
  Keywords Wildfire Detection; Supervised Learning; Causality; Evaluation  
  Abstract Insight into performance ability is crucial for successfully implementing AI solutions in real-world applications. Unanticipated input can lead to false positives (FP) and false negatives (FN), potentially resulting in false alarms in fire detection scenarios. Literature on fire detection models shows varying levels of complexity and explicability in evaluation practices; little supplementary information on performance ability outside of accuracy scores is provided. We advocate for a standardized evaluation dataset that prioritizes the end-user perspective in assessing performance capabilities. This leads us to ask what an evaluation dataset needs to constitute to enable a non-expert to determine the adequacy of a model's performance capabilities for their specific use case. We propose using data augmentation techniques that simulate interventions to remove the connection to the original target label, providing interpretable counterfactual explanations into a model's predictions.  
  Address Fraunhofer FOKUS  
  Corporate Author Thesis  
  Publisher University of Nebraska at Omaha Place of Publication Omaha, USA Editor Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi  
  Language English Summary Language Original Title  
  Series Editor Hosssein Baharmand Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition 1  
  ISSN ISBN Medium  
  Track AI for Crisis Management Expedition Conference  
  Notes http://dx.doi.org/10.59297/KHML7113 Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2577  
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