EcoSustain: computer and data science for the environment

• Grant number: 23/00811-0
• Support Opportunities: Research Projects - Thematic Grants
• Start date: April 01, 2024
• End date: March 31, 2029
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Agreement: MCTI/MC
• Principal Investigator: Antonio Jorge Gomes Abelém
• Grantee: Antonio Jorge Gomes Abelém
• Host Institution: Instituto de Ciências Exatas e Naturais. Universidade Federal do Pará (UFPA). Ministério da Educação (Brasil). Belém , SP, Brazil
• Pesquisadores principais:( Atuais ): Fabio Kon ; Fábio Moreira Costa ; Markus Endler ; Miguel Elias Mitre Campista ; Ronaldo Alves Ferreira ; Thais Vasconcelos Batista
• Pesquisadores principais:( Anteriores ): Antonio Jorge Gomes Abelém
• Associated researchers: Aldebaro Barreto da Rocha Klautau Junior ; Alessandra Cristina Corsi ; Alessandro Santiago dos Santos ; Alfredo Goldman vel Lejbman ; Álvaro Luiz Fazenda ; André Torre Neto ; Arlindo Flávio da Conceição ; Carlos Alberto Kamienski ; Daniel de Angelis Cordeiro ; Daniel Macêdo Batista ; Davi Viana dos Santos ; Diogo Machado Gonçalves ; Edmundo Roberto Mauro Madeira ; Emilio de Camargo Francesquini ; Everton Ranielly de Sousa Cavalcante ; Fabio Augusto Faria ; Flávia Noronha Dutra Ribeiro ; Francisco José da Silva e Silva ; Francisco Rolfsen Belda ; Ilan Sousa Correa ; Juliana Arriel ; Juliana Freitag Borin ; Kelly Rosa Braghetto ; Luciano Reis Coutinho ; Luis Henrique Maciel Kosmalski Costa ; Luiz Fernando Bittencourt ; Manuel Eduardo Ferreira ; Paulo Roberto Miranda Meirelles ; Pedro Henrique Cruz Caminha ; Raphael Yokoingawa de Camargo ; Renato Porfírio Ishii ; Roberto Hirata Junior ; Roberto Speicys Cardoso ; Robson Francisco da Silva Dias ; Rodrigo de Souza Couto ; Silvana Rossetto
• Associated scholarship(s): 24/18526-3 - Dynamic Management of Last-Mile On-Demand Transit: Designing Transferable Models for Deep Reinforcement Learning Algorithms, BP.MS; 24/12863-8 - Analysing and measuring the risk of eviction with multimodal machine learning, BP.DD; 24/15929-0 - A Quantum Sensing Platform for Rural Sensing, BP.IC; 24/08223-3 - Client selection in federated learning, BP.IC

Abstract

Never before has it been more urgent to restore damaged ecosystems than it is now. The UN Decade of Ecosystem Restoration aims to prevent, halt, and reverse the degradation of ecosystems on every continent and ocean. It can help eradicate poverty, combat climate change, and prevent mass extinction. Our current environmental challenges are global and relate to many problems in several areas. They include illegal deforestation, drastic reduction of biodiversity in several biomes, water and groundwater and air pollution, global warming, high energy footprint, improper waste treatment, use of fossil fuels, abusive use of harmful fertilizers and pesticides, epidemic diseases, deaths, or total loss of property due to natural disasters like floods, tropical cyclones, hurricanes, typhoons, natural fires, and many others. Due to the extensive advances in Information and Communication Technology (ICT), Data Science, and Artificial Intelligence in the last two decades, there are multiple opportunities to apply these technologies and knowledge to benefit the environment broadly. For example, Wireless Sensor Networks, Internet of Things, flying networks of drones, high-capacity sensors, data filtering and interpretation, and Data Analytics can be used to collect and process sensor data from natural resources, animals, or flora to monitor their purity or health, and automate protection and restoration processes. In addition, we can use Data Science and Machine Learning to classify events as regular and abnormal and better predict the result of ecosystem restoration or improvement measures. The ECO Sustain project aggregates a group of highly qualified, interdisciplinary researchers from some of the best Brazilian universities who will build upon their experience with conventional Computer Science, Software Engineering, Simulation, Data Analytics, Machine Learning, IoT, and Environmental Sciences to investigate, model, and devise technological solutions for creating software systems, communication protocols, networked services, machine learning models, etc. to monitor and analyze ecosystems and natural resources in real-time, as well as to ensure the effective prevention, forecasting, and mitigation of environmental degradation processes caused by humans and their lifestyle. (AU)

Scientific publications

(5)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DA SILVA JR, BRIVALDO ALVES; DE CARVALHO, ADRIANO BASTOS; CUNHA, ITALO; FRIEDMAN, TIMUR; KATZ-BASSETT, ETHAN; FERREIRA, RONALDO ALVES. Uncovering BGP Action Communities and Community Squatters in the Wild. PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS, v. 8, n. 3, p. 23-pg., 2024-12-01. (23/00812-7, 23/00811-0)

NETO, EDUARDO B.; FARIA, FABIO A.; DE OLIVEIRA, AMANDA A. S.; FAZENDA, ALVARO L.. A Satellite Band Selection Framework for Amazon Forest Deforestation Detection Task. PROCEEDINGS OF THE 2024 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2024, v. N/A, p. 9-pg., 2024-01-01. (15/24485-9, 23/00811-0, 14/50937-1, 23/00782-0, 17/25908-6, 18/23908-1)

RESENDE, HUGO; NETO, EDUARDO B.; CAPPABIANCO, FABIO A. M.; FAZENDA, ALVARO L.; FARIA, FABIO A.. Sampling Strategies based on Wisdom of Crowds for Amazon Deforestation Detection. 2024 37TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES, SIBGRAPI 2024, v. N/A, p. 6-pg., 2024-01-01. (15/24485-9, 23/00811-0, 14/50937-1, 19/26702-8, 23/00782-0, 18/23908-1)

GUIMARAES, LUCAS C. B.; COUTO, RODRIGO S.. A Performance Evaluation of Neural Networks for Botnet Detection in the Internet of Things. Journal of Network and Systems Management, v. 32, n. 4, p. 24-pg., 2024-10-01. (23/00811-0, 23/00673-7)

BORLIDO, ISABELA; BOUHID, EDUARDO; SUNDERMANN, VICTOR; RESENDE, HUGO; FAZENDA, ALVARO LUIZ; FARIA, FABIO; GUIMAR, SILVIO JAMIL F.. How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests. IEEE Geoscience and Remote Sensing Letters, v. 21, p. 5-pg., 2024-01-01. (19/26702-8, 14/50937-1, 23/00811-0, 18/23908-1, 23/00782-0, 15/24485-9, 17/25908-6)