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dr. R. Vargas Maretto (Raian)

Assistant Professor

About Me

Raian V. Maretto is PhD in Applied Computing (2020) and MSc in Remote Sensing (2011) by the Instituto Nacional de Pesquisas Espaciais (Brazilian National Institute for Space Research, INPE), and Bachelor's in Computer Science (2008) by the Universidade Federal de Ouro Preto (Federal University of Ouro Preto, UFOP). With the main expertise in the application of Deep Learning, Machine Learning, and Data Mining methods to the analysis of geospatial data. He worked as a consultant and research assistant at INPE, in the context of the FIP (Forest Investment Program) Cerrado and the MSA (Monitoring the Amazon through Satellite Imagery) projects, developing methods based on Deep Learning to automatically map deforested areas, agriculture and vegetation types in the Brazilian Cerrado and Amazon biomes. He has more than 10 years of experience in the development of Geographic Information Systems (GIS) and Remote Sensing image processing algorithms and software, participating on large software development teams following agile software development methods and working with languages like C++, Python, Lua, R, and Java. He also has participated in the development of the following systems: TerraLib library, TerraView, TerraME, GeoDMA, and recently the DeepGeo package. Main research interests are on Remote Sensing data analysis, integration of images from different sensors and natures, computer vision, machine learning, data mining, and pattern recognition.

Expertise

Engineering & Materials Science
# Agriculture
# Deep Learning
# Deforestation
# Image Analysis
# Remote Sensing
Earth & Environmental Sciences
# Cerrado
# Deforestation
# Learning

Organisations

Publications

Recent
Filho, P. S. , Persello, C. , Maretto, R. V., & MacHado, R. (2023). Investigating Sar-Optical Deep Learning Data Fusion to Map the Brazilian Cerrado Vegetation with Sentinel Data. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 1365-1368). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July). IEEE. https://doi.org/10.1109/IGARSS52108.2023.10282190
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), Article 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 International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021). 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 International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021). IEEE. https://doi.org/10.1109/igarss47720.2021.9554383
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). Article 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). Article 9323184 IEEE. https://doi.org/10.1109/IGARSS39084.2020.9323184

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Affiliated Study Programmes

Master

Courses Academic Year  2023/2024

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.
 

Courses Academic Year  2022/2023

Contact Details

 
 

Visiting Address

University of Twente
Faculty of Geo-Information Science and Earth Observation
Langezijds (building no. 19), room 1320
Hallenweg 8
7522NH  Enschede
The Netherlands

Mailing Address

University of Twente
Faculty of Geo-Information Science and Earth Observation
Langezijds  1320
P.O. Box 217
7500 AE Enschede
The Netherlands

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University of Twente Drienerlolaan 5
7522 NB Enschede
+31 (0)53 489 9111
info@utwente.nl
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