SwePub
Sök i SwePub databas

  Extended search

Träfflista för sökning "onr:"swepub:oai:DiVA.org:lnu-126400" "

Search: onr:"swepub:oai:DiVA.org:lnu-126400"

  • Result 1-1 of 1
Sort/group result
   
EnumerationReferenceCoverFind
1.
  • Aksoy, Samet, et al. (author)
  • Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches
  • 2023
  • In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. - : IEEE. - 9798350320107 - 9798350320091 - 9798350331745 ; , s. 2661-2664
  • Conference paper (peer-reviewed)abstract
    • This paper presents the first results of the ongoing development of new forest mapping methods for the Swedish national forest mapping case using Airborne Laser Scanning (ALS) data, utilizing the recent findings in machine learning (ML) and Artificial Intelligence (AI) techniques. We used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) as ML models. In addition, Neural networks (NN) based approaches were utilized in this study. ALS derived features were used to estimate the stem volume (V), above-ground biomass (AGB), basal area (B), tree height (H), stem diameter (D), and forest stand age (A). XGBoost ML algorithm outperformed RF 1 % to 3 % in the R² metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.
  •  
Skapa referenser, mejla, bekava och länka
  • Result 1-1 of 1

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view