Chasing the timber trail : machine learning to reveal harvest location misrepresentation
- Author
- Shailik Sarkar, Raquib Bin Yousuf, Linhan Wang, Brian Mayer, Thomas Mortier (UGent) , Victor Deklerck, Jakub Truszkowski, John C. Simeone, Marigold Norman, Jade Saunders, Chang-Tien Lu and Naren Ramakrishnan
- Organization
- Abstract
- Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products trade, affecting livelihoods and communities across the globe. Stable isotope ratio analysis (SIRA) is rapidly becoming an important tool for determining the harvest location of traded, organic, products. The spatial pattern in stable isotope ratio values depends on factors such as atmospheric and environmental conditions and can thus be used for geographic origin identification. We present here the results of a deployed machine learning pipeline where we leverage both isotope values and atmospheric variables to determine timber harvest location. Additionally, the pipeline incorporates uncertainty estimation to facilitate the interpretation of harvest location determination for analysts. We present our experiments on a collection of oak (Quercus spp.) tree samples from its global range. Our pipeline outperforms comparable state-of-the-art models determining geographic harvest origin of commercially traded wood products, and has been used by European enforcement agencies to identify harvest location misrepresentation. We also identify opportunities for further advancement of our framework and how it can be generalized to help identify the origin of falsely labeled organic products throughout the supply chain.
- Keywords
- Stable isotope ratio analysis (SIRA), Gaussian processes, Uncertainty estimation, Multitask learning, ML Applications
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01K35MQZF6HEZDJG8EGTWCN7GH
- MLA
- Sarkar, Shailik, et al. “Chasing the Timber Trail : Machine Learning to Reveal Harvest Location Misrepresentation.” PROCEEDINGS OF THE 31ST ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING V.2, KDD 2025, Association for Computing Machinery (ACM), 2025, pp. 4796–805, doi:10.1145/3711896.3737201.
- APA
- Sarkar, S., Yousuf, R. B., Wang, L., Mayer, B., Mortier, T., Deklerck, V., … Ramakrishnan, N. (2025). Chasing the timber trail : machine learning to reveal harvest location misrepresentation. PROCEEDINGS OF THE 31ST ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING V.2, KDD 2025, 4796–4805. https://doi.org/10.1145/3711896.3737201
- Chicago author-date
- Sarkar, Shailik, Raquib Bin Yousuf, Linhan Wang, Brian Mayer, Thomas Mortier, Victor Deklerck, Jakub Truszkowski, et al. 2025. “Chasing the Timber Trail : Machine Learning to Reveal Harvest Location Misrepresentation.” In PROCEEDINGS OF THE 31ST ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING V.2, KDD 2025, 4796–4805. Association for Computing Machinery (ACM). https://doi.org/10.1145/3711896.3737201.
- Chicago author-date (all authors)
- Sarkar, Shailik, Raquib Bin Yousuf, Linhan Wang, Brian Mayer, Thomas Mortier, Victor Deklerck, Jakub Truszkowski, John C. Simeone, Marigold Norman, Jade Saunders, Chang-Tien Lu, and Naren Ramakrishnan. 2025. “Chasing the Timber Trail : Machine Learning to Reveal Harvest Location Misrepresentation.” In PROCEEDINGS OF THE 31ST ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING V.2, KDD 2025, 4796–4805. Association for Computing Machinery (ACM). doi:10.1145/3711896.3737201.
- Vancouver
- 1.Sarkar S, Yousuf RB, Wang L, Mayer B, Mortier T, Deklerck V, et al. Chasing the timber trail : machine learning to reveal harvest location misrepresentation. In: PROCEEDINGS OF THE 31ST ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING V2, KDD 2025. Association for Computing Machinery (ACM); 2025. p. 4796–805.
- IEEE
- [1]S. Sarkar et al., “Chasing the timber trail : machine learning to reveal harvest location misrepresentation,” in PROCEEDINGS OF THE 31ST ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING V.2, KDD 2025, Toronto, ON, Canada, 2025, pp. 4796–4805.
@inproceedings{01K35MQZF6HEZDJG8EGTWCN7GH,
abstract = {{Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products trade, affecting livelihoods and communities across the globe. Stable isotope ratio analysis (SIRA) is rapidly becoming an important tool for determining the harvest location of traded, organic, products. The spatial pattern in stable isotope ratio values depends on factors such as atmospheric and environmental conditions and can thus be used for geographic origin identification. We present here the results of a deployed machine learning pipeline where we leverage both isotope values and atmospheric variables to determine timber harvest location. Additionally, the pipeline incorporates uncertainty estimation to facilitate the interpretation of harvest location determination for analysts. We present our experiments on a collection of oak (Quercus spp.) tree samples from its global range. Our pipeline outperforms comparable state-of-the-art models determining geographic harvest origin of commercially traded wood products, and has been used by European enforcement agencies to identify harvest location misrepresentation. We also identify opportunities for further advancement of our framework and how it can be generalized to help identify the origin of falsely labeled organic products throughout the supply chain.}},
author = {{Sarkar, Shailik and Yousuf, Raquib Bin and Wang, Linhan and Mayer, Brian and Mortier, Thomas and Deklerck, Victor and Truszkowski, Jakub and Simeone, John C. and Norman, Marigold and Saunders, Jade and Lu, Chang-Tien and Ramakrishnan, Naren}},
booktitle = {{PROCEEDINGS OF THE 31ST ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING V.2, KDD 2025}},
isbn = {{9798400714542}},
keywords = {{Stable isotope ratio analysis (SIRA),Gaussian processes,Uncertainty estimation,Multitask learning,ML Applications}},
language = {{eng}},
location = {{Toronto, ON, Canada}},
pages = {{4796--4805}},
publisher = {{Association for Computing Machinery (ACM)}},
title = {{Chasing the timber trail : machine learning to reveal harvest location misrepresentation}},
url = {{http://doi.org/10.1145/3711896.3737201}},
year = {{2025}},
}
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