Exploiting Data and Human Knowledge for Predicting Wildlife Poaching
Open accessAbstract
Poaching continues to be a significant threat to the conservation of wildlife and the associated ecosystem. Estimating and predicting where the poachers have committed or would commit crimes is essential to more effective allocation of patrolling resources. The real-world data in this domain is often sparse, noisy and incomplete, consisting of a small number of positive data (poaching signs), a large number of negative data with label uncertainty, and an even larger number of unlabeled data. Fortunately, domain experts such as rangers can provide complementary information about poaching activity patterns. However, this kind of human knowledge has rarely been used in previous approaches.
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Ref. 140 used expert knowledge of park rangers to augment sparse and noisy records of poaching activity, improving predictions of poaching occurrence and enabling more efficient use of limited patrol resources in a Chinese nature reserve.
“Finally ref. 140, used expert knowledge of park rangers to augment sparse and noisy records of poaching activity, thereby improving predictions of poaching occurrence and enabling more efficient use of limited patrol resources in a Chinese nature reserve.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [140]
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- Authors
- Gurumurthy, S., Yu, L., Zhang, C., Jin, Y., Li, W., Zhang, X., & Fang, F.
- Affiliations
- Carnegie Mellon University (US)
- Shanghai Jiao Tong University (CN)
- Southeast University (CN)
- World Wild Fund for Nature, China, China
- Huang Ni He Forest Bureau, China
- ORCID
- Yu, Lantao
- Venue
- Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies
- Publisher
- ACM
- Published
- 2018-06-20
- Pages
- 1-8
- OpenAlex
- W2804801283
- Cited by
- 17
- References
- 9
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 4 · 2022: 1 · 2021: 3 · 2020: 4 · 2019: 1
- FWCI
- 34.53
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Wildlife Conservation and Criminology Analyses
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- poaching, expert knowledge, domain knowledge integration, label uncertainty, unlabeled data, wildlife conservation
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