Expertises
Engineering & Materials Science
# Agriculture
# Deep Learning
# Deforestation
# Image Analysis
# Remote Sensing
Earth & Environmental Sciences
# Cerrado
# Deforestation
# Learning
Verbonden aan
Publicaties
Recent
Matosak, B. M., Fonseca, L. M. G., Taquary, E. C.
, Maretto, R. V., Bendini, H. D. N., & Adami, M. (2022).
Mapping Deforestation in Cerrado Based on Hybrid Deep Learning Architecture and Medium Spatial Resolution Satellite Time Series.
Remote sensing,
14(1), [209].
https://doi.org/10.3390/rs14010209
Bendini, H. N., Fonseca, L. M. G.
, Maretto, R. V., Matosak, B. M., Taquary, E. C., Haidar, R. F., & Valeriano, D. D. M. (2021).
Exploring a deep convolutional neural network and GEOBIA for automatic recognition of Brazilian Palm Swamps (Veredas) using Sentinel-2 optical data. In
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 5401-5404). IEEE.
https://doi.org/10.1109/igarss47720.2021.9554050
Taquary, E. C., Fonseca, L. M. G.
, Maretto, R. V., Bendini, H. N., Matosak, B. M., Sant'Anna, S. J. S., & Mura, J. C. (2021).
Detecting clearcut deforestation employing deep learning methods and SAR time series. In
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 4520-4523). IEEE.
https://doi.org/10.1109/igarss47720.2021.9554383
Sun, X.
, Zhao, W.
, V. Maretto, R.
, & Persello, C. (2021).
Building polygon extraction from aerial images and digital surface models with a frame field learning framework.
Remote sensing,
13(22), 1-21. [4700].
https://doi.org/10.3390/rs13224700
Fonseca, L. M. G., Körting, T. S., Bendini, H. N., Girolamo Neto, C. D., Neves, A. K., Soares, A. R., Taquary, E. C.
, & Maretto, R. V. (2021).
Pattern recognition and remote sensing techniques applied to land use and land cover mapping in the Brazilian Savannah.
Pattern recognition letters,
148, 54-60.
https://doi.org/10.1016/j.patrec.2021.04.028
Sun, X.
, Zhao, W.
, Maretto, R. V.
, & Persello, C. (2021).
Building outline extraction from aerial imagery and digital surface model with a frame field learning framework. In N. Paparoditis, C. Mallet, F. Lafarge, M. Y. Yang, A. Yilmaz, J. D. Wegner, F. Remondino, T. Fuse, & I. Toschi (Eds.),
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (B2-2021 ed., Vol. 43, pp. 487-493). (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-B2-2021-487-2021
Maretto, R. V., Fonseca, L. M. G., Jacobs, N., Korting, T. S., Bendini, H. N., & Parente, L. L. (2021).
Spatio-temporal deep learning approach to map deforestation in Amazon rainforest.
IEEE geoscience and remote sensing letters,
18(5), 771-775.
https://doi.org/10.1109/LGRS.2020.2986407
Matosak, B. M.
, Maretto, R. V., Korting, T. S., Adami, M., & Fonseca, L. M. G. (2020).
Mapping deforested areas in the Cerrado Biome through recurrent neural networks. In
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 1389-1392). [9324019] IEEE.
https://doi.org/10.1109/IGARSS39084.2020.9324019
Bendini, H. N., Fonseca, L. M. G., Soares, A. R., Rufin, P., Schwieder, M., Rodrigues, M. A.
, Maretto, R. V., Korting, T. S., Leitao, P. J., Sanches, I. D. A., & Hostert, P. (2020).
Applying a phenological object-based image analysis (phenobia) for agricultural land classification: A study case in the Brazilian Cerrado. In
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 1078-1081). [9323184] IEEE.
https://doi.org/10.1109/IGARSS39084.2020.9323184
Maretto, R. (Author), Korting, T. S. (Author), & Fonseca, L. M. G. (Author). (2019).
An extensible and easy-to-use Toolbox for Deep Learning based analysis of Remote Sensing Images. Software, GitHub.
https://github.com/rvmaretto/deepgeo
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Verbonden aan Opleidingen
Master
Vakken Collegejaar 2022/2023
Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.
Vakken Collegejaar 2021/2022
Contactgegevens
Bezoekadres
Universiteit Twente
Faculty of Geo-Information Science and Earth Observation
ITC
(gebouwnr. 75), kamer 2-136
Hengelosestraat 99
7514AE Enschede
Postadres
Universiteit Twente
Faculty of Geo-Information Science and Earth Observation
ITC
2-136
Postbus 217
7500 AE Enschede