As a Solutions Consultant at Brain Agriculture, my focus lies in utilizing data analytics and artificial intelligence to innovate within the agricultural sector. With over 7 years of experience, I am proficient in Python, Django, Keras, TensorFlow, and Scikit-Learn. My expertise extends to data analysis, including satellite image processing and the application of AI for predictive modeling and optimization in land use and crop health assessment.

In addition to my analytics and AI capabilities, I have a strong foundation in software development, particularly with Python and the Django framework

Professional Background

Brain Agriculture

Solutions Consultant (Jul 2022 - Present)
Senior Software Development Analyst (Oct 2021 - Jun/2022)

Big data for agribusiness financial operations.

  • Land use and land cover mapping using machine learning tools;
  • Processing of satellite images;
  • Development of geographic information systems.
  • Leads the development of Remote sensing products.
Out 2021 - Currently | Remote

Agrosatélite Applied Geotechnology Ltd.

Senior Software Development Analyst (Out 2019 - Sep/2021)
Mid-level Software Development Analyst (May 2018 - Sep/2019)
Trainee Software Development Analyst (Aug 2017 - Apr/2018)

Remote sensing and geographic intelligence for agribusiness and the environment.

  • Land use and land cover mapping using machine learning tools;
  • Processing of satellite images;
  • Development of geographic information systems.
  • Leads the research and development team.
Ago 2017 - Set 2021 | Florianópolis-SC, Brazil

Informatics Practice Center (NPI)

Software Development Intern

Design and deliver information technology and communication solutions that contribute to the development of the academic community and its partners.

  • Development of information systems using Java Spring Boot.
Jan 2016 - Dez 2016 | Quixadá-CE, Brazil

Project Participation

Screenshot of MapBiomas website
Crop Monitor

Crop monitoring system using remote sensing data such as satellite images, meteorological data and artificial intelligence.

My contribution

In this project, I served as the Tech Lead for the development of the Crop Monitor product. My role spanned from its initial conception and architectural structuring through to implementation, as well as overseeing the maintenance phase and ongoing evolution of the tool. My responsibilities included leading the technical team, defining development strategies, ensuring the solution's quality and effectiveness, and guiding the testing and refinement phases of the product.

Screenshot of MapBiomas website
Fourth National Communication of Brazil to the UNFCCC

Estimating Emissions and Removals of Pollutants in Brazil's Land Use Sector

My contribution

In this project, I managed the download and processing of satellite images to develop matrices for Greenhouse Gas emissions and removals. These matrices were essential for calculating Brazil's land use sector's Greenhouse Gas dynamics for the years 1994, 2002, 2005, 2010, and 2016, providing key insights into the country's environmental impact during these periods.

Screenshot of MapBiomas website

In this project we aimed to map land cover and use in Brazil annually from 1985 to the present.

My contribution

Between 2017 and 2021, I dedicated my efforts to a project that involved mapping Agriculture and Planted Forests across the entire Brazilian territory. This task was accomplished by employing machine learning algorithms, notably Random Forest, in conjunction with Landsat satellite imagery spanning from 1984 to the present. The project's scope included the application of advanced analytical techniques to process and interpret extensive data sets, providing a comprehensive and dynamic overview of land use changes over time within Brazil.

Code Island logo
Next Generation Mapping

This project aimed to establish the next generation of land use and land cover mapping.

My Contribution

In this project, I led the mapping of irrigation centers and urban infrastructure. Collaborating with NexGenMap partners like Planet, Mapbiomas, Google, and the Gordon and Betty Moore Foundation, we developed innovative solutions for monitoring forest loss and land use changes in Brazil. Leveraging improved satellite imagery and advanced algorithms, we achieved precise detection and mapping of deforestation, aiding in informed land management and policy decisions to conserve Brazil's tropical forests.

SIMFaz image

Farm Monitoring System

My Contribution

As the team leader, I spearheaded the development of SIMFAZ, a farm monitoring system. It provides geographic intelligence to assess environmental, social, and financial risks on rural properties. With three modules - Socioenvironmental, Property Evaluation, and Crop Monitoring - SIMFAZ offers timely insights for various clients, including banks, insurers, and agricultural companies. Using satellite imagery and climate data, it monitors compliance with socioenvironmental criteria, evaluates property values, and tracks crop performance over time.

Alertas image
COAMO's Traceability Portal

System for traceability of the soy production chain.

  • Traceability
  • Socioenvironmental
  • Deforestation
  • Soybean
Code Island logo
Survey of sugarcane irrigation and fertirrigation in Brazil
  • Remote Sensing
  • Geoprocessing
  • Agriculture
  • Irrigation
  • Google Earth Engine
Evapotranspiration image
Estimates of Actual Evapotranspiration by Remote Sensing in Brazil
  • Remote Sensing
  • Geoprocessing
  • Evapotranspiration
  • Google Earth Engine
Capa da Publicação about polos de irrigação
National poles of irrigated agriculture
  • Remote Sensing
  • Irrigation
  • Geoprocessing
  • Google Earth Engine
SEEBop App image
SEEBop App

Application developed for the National Water Agency to estimate actual evapotranspiration for Brazil.

  • Deforestation Alerts
  • Satellite Images
  • Remote Sensing
  • Geoprocessing
Alertas image
Alertas App

Satellite image visualization system for areas with deforestation alerts in Brazil.

  • Deforestation Alerts
  • Satellite Images
  • Remote Sensing
  • Geoprocessing

Nutritional care system for the academic community.

  • Nutrition
  • Health
Alertas image
Dias de Sorte

Automatic lottery game generator.

  • Loterias
  • Matemática
  • Estatísticas
  • Probabilidade



HTML5 logo HTML5
CSS3 logo CSS3
Bootstrap logo Bootstrap
Javascript logo Javascript
Jquery logo jQuery
Angular logo Angular


Python logoPython
Django logoDjango
PpostgresSQL logoPostgreSQL
MongoDB logoMongoDB

Geoprocessing and Data Science

Google Earth Engine logo Google Earth Engine
Tensorflow logo TensorFlow
Scikit-Learn logo Scikit-learn

Build Tools

Docker logoDocker
Jenkins logoJenkins


GitHub logoGithub
BitBucket logo BitBucket
Jira logo Jira

Educational background


Reconstructing three decades of land use and land cover changes in brazilian biomes with landsat archive and earth engine

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were ...

DOI: 10.3390/rs12172735

Date: 2020-08-25

Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning

The availability of freshwater is becoming a global concern. Because agricultural consumption has been increasing steadily, the mapping of irrigated areas is key for supporting the monitoring of land use and better management of available water resources. In this paper, we propose a method to automatically detect and map center pivot irrigation systems using U-Net, an image segmentation convolutional neural network architecture, applied to a constellation of PlanetScope images from the Cerrado biome of Brazil. Our objective is to provide a fast and accurate alternative to map center pivot irrigation systems with very high spatial and temporal resolution imagery. We implemented a modified U-Net architecture using the TensorFlow library and trained it on the Google cloud platform with a dataset built from more than 42,000 very high spatial resolution PlanetScope images acquired between August 2017 and November 2018. The U-Net implementation achieved a precision of 99% and a recall of 88% to detect and map center pivot irrigation systems in our study area. This method, proposed to detect and map center pivot irrigation systems, has the potential to be scaled to larger areas and improve the monitoring of freshwater use by agricultural activities.

DOI: 10.3390/rs12030558

Date: 2020-02-07

Construção de mosaicos temporais normalizados de imagens planet

The compact multispectral satellite constellation, Planet, collects daily images with a spatial resolution of 3 to 4 m. These images have the potential to contribute significantly to the improvement of land use and land cover mapping accuracy and to enhance the detection and monitoring capabilities of anthropogenic interferences over natural landscapes. A fundamental step for the ef ective use of Planet´s images is the detection and removal of clouds, shades and other types of atmospheric noise, plus the radiometric normalisation of such dataset. In this article, we briefly present the Nextgen Map Project, as well as, share its advances towards the generation of normalised cloud-free mosaics. In here, the Google Earth Engine algorithm is detailed presented, and the mosaic final repository is shared. Finally, we discuss the next steps to overcome some of the limitations that have not yet been addressed in this phase of the project.

DOI: 10.29327/xix-sbsr.a1

Date: 2019-04-17