SmartWoodID: Smart classification of Congolese timbers: deep learning techniques for enforcing forest conservation
| dc.contributor.author | De Blaere, R. | |
| dc.contributor.author | Van den Bulcke, J. | |
| dc.contributor.author | Verwaeren, J. | |
| dc.contributor.author | Hubau, W. | |
| dc.contributor.author | Beeckman, H. | |
| dc.coverage.spatial | Africa - Central | |
| dc.coverage.spatial | Africa | |
| dc.coverage.spatial | Congo, The Democratic Republic of the | |
| dc.date | 2021 | |
| dc.date.accessioned | 2025-03-05T14:24:05Z | |
| dc.date.available | 2025-03-05T14:24:05Z | |
| dc.identifier.uri | https://orfeo.belnet.be/handle/internal/13682 | |
| dc.description | A substantial part of the timber traded each year is still illegal. Illegal logging is the most profitable biodiversity crime. It involves a high risk of irreversible damage to forests since it often implicates overexploitation of highly sought after, sometimes protected, species. This is especially pertinent for tropical species, as it is estimated that 30-90% of the tropical timber volume is harvested illegally (Deklerck et al., 2020; Hirschberger, 2008; Hoare, 2015; Vlam et al., 2018). Timber regulations are already active (CITES, FLEGT, EUTR , Amendment to the U.S. Lacey Act), but implementation and enforcement are a challenge. Wood identification is crucial in the enforcement process when it comes to verify whether the shipment corresponds with the products mentioned on the accompanying documents. For this reason, there is a growing demand for timber identification tools that can be applied by law enforcement officers. SmartwoodID aims at improving both identification success and speed by non-experts. The project aims at automating part of the wood identification process by applying artificial intelligence techniques for the analysis of wood anatomical images of timber species of the Democratic Republic of the Congo. The project focusses on 970 Congolese timbers to create a database with high-resolution scans of the endgrain surface along with expert wood anatomical descriptions. The study material comes from the Tervuren Xylarium. This because said database offers the most complete collection of reference material for the development of wood classification and identification approaches for Congolese species, comprising more than 2000 woody species from the DRC (timber trees, small trees, shrubs, dwarf shrubs and lianas). The resulting database is used to make an illustrated key for wood identification. The project also takes advantage of the power of modern deep learning approaches. The scans and anatomical descriptions will therefore serve as annotated training data to develop a machine learning assisted illustrated key for wood identification. | |
| dc.language | eng | |
| dc.publisher | Ghent University | |
| dc.title | SmartWoodID: Smart classification of Congolese timbers: deep learning techniques for enforcing forest conservation | |
| dc.type | Conference | |
| dc.subject.frascati | Biological sciences | |
| dc.subject.frascati | Computer and information sciences | |
| dc.audience | Scientific | |
| dc.subject.free | Wood biology | |
| dc.source.title | GAPSYM14: Africa and its ecologies: people and nature in the age of climate change | |
| Orfeo.peerreviewed | Yes | |
| dc.identifier.rmca | 6851 |
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