Abstract. PRODES and DETER project together turned 33 years-old with an undeniably contribution to the state-of-art in mapping and monitoring tropical deforestation in Brazil. Monitoring systems all over the world have taken advantage of big data repositories of remote sensing data as they are becoming freely available together with artificial intelligence. Thus, considering the advent of new generation remote sensing data hubs, online platforms of big data that can fill in spatial and temporal resolutions gaps in current deforestation mapping, this work aims to present recent innovations at INPE´s deforestation monitoring systems in Brazil and how they are gauging new realms of technological levels. Recent innovations at INPE´s monitoring systems are: 1) the development of TerraBrasilis platform of data access and analysis; 2) the adoption of new sensors and cloud detection strategies; 3) the complementary use of multi-sensor images; 4) the complementary adoption of SAR C-band images using cloud data to sample and process Sentinel-1. Future innovations are: 1) development of a Brazilian data cube to be used in deep learning techniques of image classification; 2) Routine uncertainty analysis of PRODES data. Automatization might fasten mapping process, but the real challenge is to succeed in automatization maintaining data quality and historical series. The hyper-availability of remote sensing data, the initiative of a Brazilian Data Cube and promising machine learning techniques applied to land cover change detection, allowed INPE to reinforce its central role in tropical forest monitoring.

Recently conducted scientific studies prove that there is a relationship between the information published on Social Networks and the variations in the prices of assets traded on the Brazilian Stock Exchange (BOVESPA). In these studies, Natural Language Processing (PLN) techniques are used for the treatment of textual data that enable the understanding of human language by machines, which, enriched with the historical information of the assets, generate indications for decision-making in the stock market negotiations. The relationships between the Social Network Twitter and Bovespa are approached through the use of PLN in the Social Network database with Word Embedding, performing a dichotomous classification for decision making, not taking into account the practices of greater returns with the earnings of the variations of asset in the small intervals between the day. The purpose of this work is to create a model for decision making in the financial market supported by messages related to BOVESPA on the Twitter, handled by Natural Language Processing (PLN) techniques. At this point, complete sentences are used for Word Embedding vectorization and classified with a Recurrent Neural Network (LSTM) to indicate trades in the BOVESPA mini-index asset with performance governed by the market trend plus the Word Embedding classification, aggregated into 5, 15 and 30 minutes, for actions in the sequence of minutes of day trade operations. The experiments carried out in this work demonstrated the validity of the hypothesis that messages from a social network can support decisions in the financial market, allowing to obtain profits in this domain.