dr. R. Vargas Maretto (Raian)

Assistant Professor
ITC-EOS
Langezijds 1320
r.v.maretto@utwente.nl
+31534894712
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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

  • Earth and Planetary Sciences

    • Cerrado
    • Cartography
    • Learning
    • Biome
    • Map
    • Model
    • Vegetation

  • Computer Science

    • Models

Organisations

I am 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 a 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. My 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.

Publications

2024
Feature-guided deep learning model for mapping deprived areasIn 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024. IEEE. Filho, P. S., Tareke, B., Persello, C., Kuffer, M., Maretto, R., Abascal, A., Wang, J. & MacHado, R.https://doi.org/10.1109/MIGARS61408.2024.10544988
2023
Investigating Sar-Optical Deep Learning Data Fusion to Map the Brazilian Cerrado Vegetation with Sentinel DataIn IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 1365-1368). IEEE. Filho, P. S., Persello, C., Maretto, R. V. & MacHado, R.https://doi.org/10.1109/IGARSS52108.2023.10282190
2022
Mapping Deforestation in Cerrado Based on Hybrid Deep Learning Architecture and Medium Spatial Resolution Satellite Time Series, Article 209. Matosak, B. M., Fonseca, L. M. G., Taquary, E. C., Maretto, R. V., Bendini, H. D. N. & Adami, M.https://doi.org/10.3390/rs14010209
2021
Building polygon extraction from aerial images and digital surface models with a frame field learning framework, Article 4700, 1-21. Sun, X., Zhao, W., V. Maretto, R. & Persello, C.https://doi.org/10.3390/rs13224700Detecting clearcut deforestation employing deep learning methods and SAR time seriesIn 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 4520-4523). IEEE. Taquary, E. C., Fonseca, L. M. G., Maretto, R. V., Bendini, H. N., Matosak, B. M., Sant'Anna, S. J. S. & Mura, J. C.https://doi.org/10.1109/igarss47720.2021.9554383Exploring a deep convolutional neural network and GEOBIA for automatic recognition of Brazilian Palm Swamps (Veredas) using Sentinel-2 optical dataIn 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5401-5404). IEEE. Bendini, H. N., Fonseca, L. M. G., Maretto, R. V., Matosak, B. M., Taquary, E. C., Haidar, R. F. & Valeriano, D. d. M.https://doi.org/10.1109/igarss47720.2021.9554050Pattern recognition and remote sensing techniques applied to land use and land cover mapping in the Brazilian Savannah, 54-60. 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.https://doi.org/10.1016/j.patrec.2021.04.028Building outline extraction from aerial imagery and digital surface model with a frame field learning frameworkIn The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (pp. 487-493). International Society for Photogrammetry and Remote Sensing (ISPRS). Sun, X., Zhao, W., Maretto, R. V. & Persello, C.https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-487-2021Spatio-temporal deep learning approach to map deforestation in Amazon rainforest, 771-775. Maretto, R. V., Fonseca, L. M. G., Jacobs, N., Korting, T. S., Bendini, H. N. & Parente, L. L.https://doi.org/10.1109/LGRS.2020.2986407
2020
Applying a phenological object-based image analysis (phenobia) for agricultural land classification: A study case in the Brazilian CerradoIn 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings, Article 9323184 (pp. 1078-1081). IEEE. 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.https://doi.org/10.1109/IGARSS39084.2020.9323184

Research profiles

Affiliated study programs

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

Address

University of Twente

Langezijds (building no. 19), room 1320
Hallenweg 8
7522 NH Enschede
Netherlands

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