Jonne Pohjankukka

Estimating the prediction performance of spatial models via spatial k-fold cross validation

Jonne Pohjankukka, Tapio Pahikkala, Paavo Nevalainen et al. · International Journal of Geographical Information Systems · 2017 · 171 citations · Full text

Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization

Jussi Toivonen, Ileana Montoya Perez, Parisa Movahedi et al. · PLoS ONE · 2019 · 111 citations · Full text

Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology

Aura Salmivaara, Samuli Launiainen, Jari Perttunen et al. · Forestry An International Journal of Forest Research · 2020 · 47 citations

Engineering, Machine Learning, Forest Biometrics +23

Predictability of boreal forest soil bearing capacity by machine learning

Jonne Pohjankukka, Henri Riihimäki, Paavo Nevalainen et al. · Journal of Terramechanics · 2016 · 27 citations

Precision Agriculture, Engineering, Machine Learning +11

The spatial leave-pair-out cross-validation method for reliable AUC estimation of spatial classifiers

Antti Airola, Jonne Pohjankukka, Johanna Torppa et al. · Data Mining and Knowledge Discovery · 2018 · 22 citations · Full text

Engineering, Machine Learning, Geomorphology +20