Monitoring Human-Wildlife Interactions in National Parks with Crowdsourced Data and Deep Learning

Bing Pan (), Virinchi Savanapelli, Abhishek Shukla and Junjun Yin
Additional contact information
Bing Pan: The Pennsylvania State University
Virinchi Savanapelli: Indian Institute of Technology
Abhishek Shukla: Indian Institute of Technology
Junjun Yin: The Pennsylvania State University

A chapter in Information and Communication Technologies in Tourism 2022, 2022, pp 492-497 from Springer

Abstract: Abstract This short paper summarizes the first research stage for applying deep learning techniques to capture human-wildlife interactions in national parks from crowd-sourced data. The results from objection detection, image captioning, and distance calculation are reported. We were able to categorize animal types, summarize visitor behaviors in the pictures, and calculate distances between visitors and animals with different levels of accuracy. Future development will focus on getting more training data and field experiments to collect ground truth on animal types and distances to animals. More in-depth manual coding is needed to categorize visitor behavior into acceptable and unacceptable ones.

Keywords: National parks; Human-wildlife interactions; Deep learning; Artificial intelligence; Crowdsourcing (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-94751-4_46

Ordering information: This item can be ordered from
http://www.springer.com/9783030947514

DOI: 10.1007/978-3-030-94751-4_46

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().