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Fingerprint Dive into the research topics where Diego Marcos Gonzalez is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 4 Similar Researchers
Neural networks Engineering & Materials Science
Animals Engineering & Materials Science
Unmanned aerial vehicles (UAV) Engineering & Materials Science
Curricula Engineering & Materials Science
Antennas Engineering & Materials Science
train Earth & Environmental Sciences
Mammals Engineering & Materials Science
field method Earth & Environmental Sciences

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Research Output 2018 2019

  • 5 Conference contribution
  • 4 Paper
  • 2 Article
  • 1 internal PhD, WU

Best practices to train deep models on imbalanced datasets—a case study on animal detection in aerial imagery

Kellenberger, B., Marcos, D. & Tuia, D., 1 Jan 2019, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. Brefeld, U., Marascu, A., Pinelli, F., Curry, E., MacNamee, B., Hurley, N., Daly, E. & Berlingerio, M. (eds.). Springer Verlag, p. 630-634 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11053 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Best Practice

Injecting spatial priors in Earth observation with machine vision

Gonzalez, D., 2019, Wageningen: Wageningen University. 130 p.

Research output: Thesisinternal PhD, WUAcademic

Open Access
Open Access
Neural networks
2 Citations (Scopus)

Correcting Misaligned Rural Building Annotations in Open Street Map Using Convolutional Neural Networks Evidence

Vargas-Munoz, J. E., Marcos, D., Lobry, S., dos Santos, J. A., Falcao, A. X. & Tuia, D., 5 Nov 2018, 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE Xplore, p. 1284-1287

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

field method
developing world

Deep learning based methods for building segmentation from remote sensing data

Lobry, S., Marcos Gonzalez, D., Vargas Munoz, J., Kellenberger, B. A., Srivastava, S. & Tuia, D., 2018. 4 p.

Research output: Contribution to conferencePaperAcademic

Projects 2017 2019