Publications

2025

1. Assessing the potential of SAR images in the ForestEyes project’s citizen science campaings

   Vinícius D’Lucas Bezerra Queiroz, Hugo Resende, Fabio Augusto Faria, and 2 more authors

   In Anais do Simpósio Brasileiro de Sensoriamento Remoto (SBSR), 2025

   accepted

   Abs

   Tropical forests play a crucial role in sustaining life on Earth, however, they have experienced a significant reduction in vegetation cover due to activities such as degradation and deforestation. A recent initiative to monitor these areas is the ForestEyes project, which combines citizen science and machine learning to track changes in tropical forests. The project currently uses images from optical sensors to create citizen science campaigns. However, the presence of clouds is a limiting factor in optical images and can prevent the detection of deforested areas. Thus, this article presents a preliminary evaluation of Synthetic Aperture Radar (SAR) images from Sentinel-1 and PALSAR-2/ALOS-2 sensors, exploring their viability for citizen science campaigns in the context of the ForestEyes project. Experimental results indicate that PALSAR-2 data is more effective, as it has been particularly advantageous in identifying deforested areas.

2024

1. How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests

   Isabela Borlido, Eduardo Bouhid, Victor Sundermann, and 4 more authors

   IEEE Geoscience and Remote Sensing Letters, 2024

   Abs

   The conservation of tropical forests is a topic of significant social and ecological relevance due to their crucial role in the global ecosystem. Unfortunately, deforestation and degradation impact millions of hectares annually, requiring government or private initiatives for effective forest monitoring. However, identifying deforested regions in satellite images is challenging due to data imbalance, image resolution, low-contrast regions, and occlusion. Superpixel segmentation can overcome these drawbacks, reducing workload and preserving important image boundaries. However, most works for remote-sensing images do not exploit recent superpixel methods. In this work, we evaluate 16 superpixel methods in satellite images to support a deforestation detection system in tropical forests. We also assess the performance of superpixel methods for the target task, establishing a relationship with segmentation methodological evaluation. According to our results, ERS, GMMSP, and DISF perform best on undersegmentation error (UE), boundary recall (BR), and similarity between image and reconstruction from superpixels (SIRSs), respectively, whereas ERS has the best tradeoff with compactness index (CO) and Reg. In classification, SH, DISF, and ISF perform best on RGB, UMDA, and PCA compositions, respectively. According to our experiments, superpixel methods with better tradeoffs among delineation, homogeneity, compactness, and regularity are more suitable for identifying good superpixels for deforestation detection tasks.

2. A Satellite Band Selection Framework for Amazon Forest Deforestation Detection Task

   Eduardo Bouhid Neto, Fabio Augusto Faria, Amanda de Almeida Sales De Oliveira, and 1 more author

   In Proceedings of the Genetic and Evolutionary Computation Conference, 2024

   Abs

   The conservation of tropical forests is a topic of significant social and ecological relevance due to their crucial role in the global ecosystem. Unfortunately, deforestation and degradation impact millions of hectares annually, necessitating government or private initiatives for effective forest monitoring. This study introduces a novel framework that employs the Univariate Marginal Distribution Algorithm (UMDA) to select spectral bands from Landsat-8 satellite optical sensor, optimizing the representation of deforested areas. This selection guides a semantic segmentation architecture, DeepLabv3+, enhancing its performance. Experimental results revealed several band compositions that achieved superior balanced accuracy compared to commonly adopted combinations for deforestation detection, utilizing segment classification via a Support Vector Machine (SVM). Moreover, the optimal band compositions identified by the UMDA-based approach improved the performance of the DeepLabv3+ architecture, surpassing state-of-the-art approaches compared in this study. The observation that a few selected bands outperform the total contradicts the data-driven paradigm prevalent in the deep learning field. Therefore, this suggests an exception to the conventional wisdom that ‘more is always better’.

3. Sampling Strategies Based on Wisdom of Crowds for Amazon Deforestation Detection

   Hugo Resende, Eduardo B. Neto, Fábio A. M. Cappabianco, and 2 more authors

   In 2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2024

   Abs

   Conserving tropical forests is highly relevant socially and ecologically because of their critical role in the global ecosystem. However, the ongoing deforestation and degradation affect millions of hectares each year, necessitating government or private initiatives to ensure effective forest monitoring. In April 2019, a project based on Citizen Science and Machine Learning models called ForestEyes (FE) was launched with the aim of providing supplementary data to assist experts from government and non-profit organizations in their deforestation monitoring efforts. Recent research has shown that labeling FE project volunteers/citizen scientists helps tailor machine learning models. In this sense, we adopt the FE project to create different sampling strategies based on the wisdom of crowds to select the most suitable samples from the training set to learn an SVM technique and obtain better classification results in deforestation detection tasks. In our experiments, we can show that our strategy based on user entropy-increasing achieved the best classification results in the deforestation detection task when compared with the random sampling strategies, as well as, reducing the convergence time of the SVM technique.

4. Exploring emergent microservice evolution in elastic deployment environments

   Roberto Rodrigues-Filho, Iwens Sene, Barry Porter, and 3 more authors

   Journal of Systems and Software, 2024

   Abs

   Microservices have become an important technology to enable the dynamic composition of large-scale self-adaptive systems. Although modern microservice ecosystems provide a variety of autonomous adaptation mechanisms, when focusing on the microservice itself, they can only account for changes in the sheer increase in workload volume. On the other hand, when workload patterns change, efficient treatment requires the intervention of DevOps experts to manually evolve the internal architecture of services. Given the need to quickly adapt systems to respond to changes, solely relying on DevOps to react to workload pattern changes becomes a bottleneck for future systems. To address this issue, we advance the concept of emergent microservices, that autonomously adapt and evolve their internal architectural composition to better handle changes in the pattern of incoming requests without human intervention. We demonstrate the effectiveness of our approach by exploring this novel concept in the context of a microservice-based Smart City platform. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

5. Performance Evaluation of Dense Linear Algebra Kernels using Chameleon and StarPU on AWS

   Vinicius Pinto, João Lima, Vanderlei Munhoz, and 3 more authors

   In Anais do XXV Simpósio em Sistemas Computacionais de Alto Desempenho, 2024
6. Evolução histórica do desempenho energético de tarefas cotidianas em uma distribuição Linux

   Artur Launikas Cupelli, Fábio Nakano, Flávio Coutinho, and 1 more author

   In Anais Estendidos do XXV Simpósio em Sistemas Computacionais de Alto Desempenho, 2024
7. The road to Sustainable DevOps

   Darwish Ahmad Herati, Maria Clara Aderne, and Fabio Kon

   In International Colloquium on Energy-Efficient and Sustainable Distributed Systems (E2SDS), 2024

2023

1. CharM – Evaluating a model for characterizing service-based architectures

   Thatiane Oliveira Rosa, Eduardo Martins Guerra, Filipe Figueiredo Correia, and 1 more author

   Journal of Systems and Software, 2023