dr. J.R. Bergado (JR)

John Ray Bergado is currently a researcher/lecturer in the department of Earth Observation Science (EOS), Faculty of Geoinformation Science and Earth Observation (ITC), University of Twente (UT), Enschede, The Netherlands. He received his:

PhD degree (thesis title: "End-to-end predictive models for remote sensing application") from Faculty ITC, UT in 2020;
Master of Science degree in Geoinformation Science and Earth Observation specializing in Geoinformatics (with distinction) from Faculty ITC, UT in 2016;
and Bachelor of Science degree in Geodetic Engineering (magna cum laude) from the University of the Philippines, Diliman, Philippines in 2013.

His research interests are on the interface of machine learning, statistical learning, and deep learning methods with geospatial technologies such as remote sensing, photogrammetry, and unmanned aerial vehicles (UAV). Before rejoining ITC, UT in 2022 as a researcher/lecturer, he was an Earth Observation and Geomatics Specialist with NEO BV Netherlands from 2020 to 2022; a Research Assistant with ITC, UT from 2016 to 2020; a Visiting Research Assistant with Royal Melbourne Institute of Technology (RMIT), Melbourne, Australia from 2018 to 2019; and a Geodetic Engineer with SRDP Consulting Inc., Philippines from 2013 to 2014.

Engineering & Materials Science

# Convolutional Neural Networks
# Deep Learning
# Image Classification
# Image Fusion
# Image Resolution
# Land Use
# Satellite Imagery
# Satellites

Faculty of Geo-Information Science and Earth Observation (ITC), Department of Earth Observation Science (EOS)

Bergado, J. R. , Persello, C., Reinke, K. , & Stein, A. (2021). Predicting wildfire burns from big geodata using deep learning. Safety science, 140, [105276]. https://doi.org/10.1016/j.ssci.2021.105276

Bergado, J. R. (2020). End-to-end predictive models for remote sensing applications. [PhD Thesis - Research UT, graduation UT, Faculty of Geo-Information Science and Earth Observation, University of Twente]. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/10.3990/1.9789036550963

Bergado, J. R. , Persello, C. , & Stein, A. (2020). Land use classification using deep multitask networks. In N. Paparoditis, C. Mallet, F. Lafarge, J. Jiang, A. Shaker, H. Zhang, X. Liang, B. Osmanoglu, U. Soergel, E. Honkavaara, M. Scaioni, J. Zhang, A. Peled, L. Wu, R. Li, M. Yoshimura, K. Di, O. Altan, H. M. Abdulmuttalib, & F. S. Faruque (Eds.), Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci (B3 ed., Vol. 43, pp. 17-21). (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives). International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-17-2020

Persello, C. , Tolpekin, V. , Bergado, J. R. , & De By, R. A. (2019). Towards automated delineation of smallholder farm fields from VHR images using convolutional networks. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 3836-3839). [8897979] (International Geoscience and Remote Sensing Symposium (IGARSS)). IEEE. https://doi.org/10.1109/IGARSS.2019.8897979

Persello, C. , Tolpekin, V. A. , Bergado, J. R. , & de By, R. A. (2019). Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping. Remote sensing of environment, 231, 1-18. [111253]. https://doi.org/10.1016/j.rse.2019.111253

Bergado, J. R. , Persello, C. , & Stein, A. (2018). FuseNet: End-to-end multispectral VHR image fusion and classification. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (Vol. 2018-July, pp. 2091-2094). [8519214] IEEE. https://doi.org/10.1109/IGARSS.2018.8519214

Bergado, J. R. , Persello, C. , & Stein, A. (2018). Recurrent Multiresolution Convolutional Networks for VHR Image Classification. IEEE transactions on geoscience and remote sensing, 56(11), 6361-6374. [8388225]. https://doi.org/10.1109/TGRS.2018.2837357

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