Search Results for author:
Found 8 papers, 0 papers with code
A Satellite Band Selection Framework for Amazon Forest Deforestation Detection Task
The conservation of tropical forests is a topic of significant social and ecological relevance due to their crucial role in the global ecosystem.
A Framework of Landsat-8 Band Selection based on UMDA for Deforestation Detection
Furthermore, the best composition (651) found by UMDA algorithm fed the DeepLabv3+ architecture and surpassed in efficiency and effectiveness all compositions compared in this work.
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification
Aerial scene classification, which aims to semantically label remote sensing images in a set of predefined classes (e. g., agricultural, beach, and harbor), is a very challenging task in remote sensing due to high intra-class variability and the different scales and orientations of the objects present in the dataset images.
ForestEyes Project: Conception, Enhancements, and Challenges
In this sense, this work presents a Citizen Science project called ForestEyes, which uses volunteer's answers through the analysis and classification of remote sensing images to monitor deforestation regions in rainforests.
An Evolutionary Approach for Creating of Diverse Classifier Ensembles
We address this point by proposing a framework for classifier selection and fusion based on a four-step protocol called CIF-E (Classifiers, Initialization, Fitness function, and Evolutionary algorithm).
Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests
Tropical forests represent the home of many species on the planet for flora and fauna, retaining billions of tons of carbon footprint, promoting clouds and rain formation, implying a crucial role in the global ecosystem, besides representing the home to countless indigenous peoples.
The Brazilian Data at Risk in the Age of AI?
Advances in image processing and analysis as well as machine learning techniques have contributed to the use of biometric recognition systems in daily people tasks.
Mixup-based Deep Metric Learning Approaches for Incomplete Supervision
Deep learning architectures have achieved promising results in different areas (e. g., medicine, agriculture, and security).
