Jonne Pohjankukka
Jonne Pohjankukka
Senior researcher, University of Turku
Verified email at - Homepage
Cited by
Cited by
Estimating the prediction performance of spatial models via spatial k-fold cross validation
J Pohjankukka, T Pahikkala, P Nevalainen, J Heikkonen
International Journal of Geographical Information Science 31 (10), 2001-2019, 2017
Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization
J Toivonen, I Montoya Perez, P Movahedi, H Merisaari, M Pesola, ...
PloS one 14 (7), e0217702, 2019
Predictability of boreal forest soil bearing capacity by machine learning
J Pohjankukka, H Riihimäki, P Nevalainen, T Pahikkala, J Ala-Ilomäki, ...
Journal of Terramechanics 68, 1-8, 2016
Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology
A Salmivaara, S Launiainen, J Perttunen, P Nevalainen, J Pohjankukka, ...
Forestry: An International Journal of Forest Research 93 (5), 662-674, 2020
The spatial leave-pair-out cross-validation method for reliable AUC estimation of spatial classifiers
A Airola, J Pohjankukka, J Torppa, M Middleton, V Nykänen, J Heikkonen, ...
Data Mining and Knowledge Discovery 33, 730-747, 2019
Predicting water permeability of the soil based on open data
J Pohjankukka, P Nevalainen, T Pahikkala, E Hyvönen, P Hänninen, ...
Artificial Intelligence Applications and Innovations: 10th IFIP WG 12.5 …, 2014
Arctic soil hydraulic conductivity and soil type recognition based on aerial gamma-ray spectroscopy and topographical data
J Pohjankukka, P Nevalainen, T Pahikkala, P Hänninen, E Hyvönen, ...
2014 22nd International Conference on Pattern Recognition, 1822-1827, 2014
Effect of homogenised and pasteurised versus native cows' milk on gastrointestinal symptoms, intestinal pressure and postprandial lipid metabolism
A Nuora, T Tupasela, R Tahvonen, S Rokka, P Marnila, M Viitanen, ...
International dairy journal 79, 15-23, 2018
Comparison of estimators and feature selection procedures in forest inventory based on airborne laser scanning and digital aerial imagery
J Pohjankukka, S Tuominen, J Pitkänen, T Pahikkala, J Heikkonen
Scandinavian journal of forest research 33 (7), 681-694, 2018
New computational methods for efficient utilisation of public data
J Ala-Ilomäki, J Cohen, J Heilimo, E Hyvönen, P Hänninen, J Ikonen, ...
Geol. Surv. Finl. Rep. Investig 217, 1-55, 2015
Tree log identification using convolutional neural networks
E Holmström, A Raatevaara, J Pohjankukka, H Korpunen, J Uusitalo
Smart Agricultural Technology 4, 100201, 2023
Triangular Curvature Approximation of Surfaces-Filtering the Spurious Mode
P Nevalainen, I Jambor, J Pohjankukka, J Heikkonen, T Pahikkala
International Conference on Pattern Recognition Applications and Methods 2 …, 2017
Real-Time swimmer tracking on sparse camera array
P Nevalainen, MH Haghbayan, A Kauhanen, J Pohjankukka, MJ Laakso, ...
Pattern Recognition Applications and Methods: 5th International Conference …, 2017
Multistream convolutional neural network fusion for pixel-wise classification of peatland
F Farahnakian, L Zelioli, T Pitkänen, J Pohjankukka, M Middleton, ...
2023 26th International Conference on Information Fusion (FUSION), 1-8, 2023
Utilizing remote sensing data in forest inventory sampling via bayesian optimization
J Pohjankukka, S Tuominen, J Heikkonen
arXiv preprint arXiv:2009.08420, 2020
Parallel applications and on-chip traffic distributions: Observation, implication and modelling
TC Xu, J Pohjankukka, P Nevalainen, T Pahikkala, V Leppänen
2015 10th International Joint Conference on Software Technologies (ICSOFT) 1 …, 2015
Technical description for the peatland site type data of Finland
M Middleton, M Laatikainen, J Kivilompolo, A Harju, J Lerssi, M Valkama, ...
Geologian tutkimuskeskus, 2023
Bayesian Approach for Optimizing Forest Inventory Survey Sampling with Remote Sensing Data
J Pohjankukka, S Tuominen, J Heikkonen
Forests 13 (10), 1692, 2022
Automatic detection of root rot and resin in felled Scots pine stems using convolutional neural networks
E Holmström, H Kainulainen, A Raatevaara, J Pohjankukka, T Piri, ...
International Journal of Forest Engineering 35 (2), 153-165, 2024
Model-assisted survey sampling with Bayesian optimization
J Pohjankukka, S Tuominen, J Heikkonen
arXiv preprint arXiv:2401.14902, 2024
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