Tropical Wood Species Recognition: A Dataset of Macroscopic Images

Daniel Alejandro Cano Saenz, Carlos Felipe Ordoñez Urbano, Holman Raul Gaitan Mesa and Rubiel Vargas-Cañas ()
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Daniel Alejandro Cano Saenz: Physics Department, Universidad del Cauca, Popayan 190002, Colombia
Carlos Felipe Ordoñez Urbano: Physics Department, Universidad del Cauca, Popayan 190002, Colombia
Holman Raul Gaitan Mesa: Environmental Management and Forest Governance, Regional Autonomous Corporation of Cauca, Popayan 190002, Colombia
Rubiel Vargas-Cañas: Physics Department, Universidad del Cauca, Popayan 190002, Colombia

Data, 2022, vol. 7, issue 8, 1-7

Abstract: Forests are of incalculable value due to the ecosystem services they provide to humanity such as carbon storage, climate regulation and participation in the hydrological cycle. The threat to forests grows as the population increases and the activities that are carried out in it, such as: cattle rearing, illegal trafficking, deforestation and harvesting. Moreover, the environmental authorities do not have sufficient capacity to exercise strict control over wood production due to the vast variety of timber species within the countries, the lack of tools to verify timber species in the supply chain and the limited available and labelled digital data of the forest species. This paper presents a set of digital macroscopic images of eleven tropical forest species, which can be used as support at checkpoints, to carry out studies and research based on macroscopic analysis of cross-sectional images of tree species such as: dendrology, forestry, as well as algorithms of artificial intelligence. Images were acquired in wood warehouses with a digital magnifying glass following a protocol used by the Colombian Ministry of Environment, as well as the USA Forest Services and the International Association of Wood Anatomists. The dataset contains more than 8000 images with resolution of 640 × 480 pixels which includes 3.9 microns per pixel, and an area of (2.5 × 1.9) square millimeters where the anatomical features are exposed. The dataset presents great usability for academics and researchers in the forestry sector, wood anatomists and personnel who work with computational models, without neglecting forest surveillance institutions such as regional autonomous corporations and the Ministry of the Environment.

Keywords: commercial timber species; dendrology; forest species identification; timber recognition; tree log’s identification; wood classification (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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