Leandro has a background in computer science, data analysis, spatial database, WebGIS development and machine learning optimization.
At Envirometrix he supports the development of geocomputation solutions based on earth observation data (Sentinel-2, Landsat, MODIS), cloud computing and automated predictive mapping. He is also member of the OpenGeoHub development team.
Phone : +55 (62) 984200253
- CIAMB / Federal University of Goiás (Environmental Sciences) Ph.D., 2019
- Getúlio Vargas Foundation (Project Management) MBA, 2017
- CIAMB / Federal University of Goiás (Environmental Sciences) M.S., 2017
- INF / Federal University of Goiás (Computer Science) B.S., 2010
- 2014–2019: assistant researcher / Federal University of Goiás, Goiânia, Brazil.
- 2014–2014: UNESCO Consultant / full-stack developer / Tourism Bureau of the Federal District, Brasília, Brazil
- 2013–2014: Formal employee / full-stack Developer / Ampla Fiscal Intelligence, Goiânia, Brazil
- 2012–2013: Formal employee / full-stack Developer / CUIA Internet Services, Goiânia, Brazil
- Machine learning
- Deep learning
- High performance computing
- Parente, Leandro et al. Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data. Remote Sensing, v. 11, n. 23, p. 2881, 2019 – https://doi.org/10.3390/rs11232881.
- Parente, Leandro et al. Assessing the pasturelands and livestock dynamics in Brazil, from 1985 to 2017: A novel approach based on high spatial resolution imagery and Google Earth Engine cloud computing. Remote Sensing of Environment, v. 232, p. 111301, 2019 – https://doi.org/10.1016/j.rse.2019.111301.
- Parente, Leandro; Ferreira, Laerte. Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016. Remote Sensing, v. 10, n. 4, p. 606, 2018 – https://doi.org/10.3390/rs10040606.
- Parente, Leandro et al. Monitoring the brazilian pasturelands: A new mapping approach based on the landsat 8 spectral and temporal domains. International Journal of Applied Earth Observation and Geoinformation, v. 62, p. 135-143, 2017 – https://doi.org/10.1016/j.jag.2017.06.003.