Toward a Participatory Trace-Sensing Framework Using Smartphone LiDAR for Wildlife Habitat Estimation

Abstract

As damage to agriculture and forestry by wildlife intensifies, wide-area and high-frequency data collection is required to estimate habitats and movement patterns. Although citizen participatory sensing has attracted attention, identifying footprints in natural environments requires high expertise, which is a bottleneck for participation. Furthermore, automatic identification based on RGB images is unstable in real environments due to the influence of lighting and background similarity. This paper presents a conceptual design of a system for exploratory detection of wildlife footprints from imperfect 3D point clouds acquired by non-experts using LiDAR-equipped smartphones. Characteristics of this study lie in the integrated design of data acquisition support and analysis methods, targeting uneven terrain where standard assumptions of existing 3D analysis, such as flat reference planes or high-density measurement, do not hold. Specifically, we design an acquisition application that uses AR real-time feedback to support shooting behavior and ensure a certain quality of photogrammetry point clouds, and a footprint detection method based on local geometric features. By combining appropriate acquisition support and local geometric feature analysis, we present a framework that enables general participants to achieve effective automatic recognition in real environments using only smartphones, providing a technical foundation for wide-area wildlife monitoring based on participatory sensing.

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DOI:

code:references.bib

@inproceedings{bib:kojo_wildlife_persuasive2026,

author={Kojo, Tsukasa and Sasaki, Wataru and Matsuda, Yuki and Tsubouchi, Kota and Yasumoto, Keichi and Suwa, Hirohiko},

title={Toward a Participatory Trace-Sensing Framework Using Smartphone LiDAR for Wildlife Habitat Estimation},

booktitle={21st International Conference on Persuasive Technology (PERSUASIVE '26)},

pages={x--x},

year={2026},

doi={},

url={}

}

https://scrapbox.io/files/698dfa02453671c306223ded.png

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Conference

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Collaborating Organization