1 citations · 1 across the 5 of their papers we have counts for
11 papers
Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation
Keiller Nogueira, Codrut-Andrei Diaconu, Dávid Kerekes +9
High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and often affected by noise…
Core-Set Selection for Data-efficient Land Cover Segmentation
Keiller Nogueira, Akram Zaytar, Wanli Ma +9
The increasing accessibility of remotely sensed data and their potential to support large-scale decision-making have driven the development of deep learning models for many Earth O…
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
Ribana Roscher, Marc Rußwurm, Caroline Gevaert +8
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models h…
MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture
Diogo Nunes Goncalves, Jose Marcato Junior, Pedro Zamboni +4
Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with indepe…
GMM-IL: Image Classification using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes
Penny Johnston, Keiller Nogueira, Kevin Swingler
Current deep learning classifiers, carry out supervised learning and store class discriminatory information in a set of shared network weights. These weights cannot be easily alter…
Facing the Void: Overcoming Missing Data in Multi-View Imagery
Gabriel Machado, Keiller Nogueira, Matheus Barros Pereira +1
In some scenarios, a single input image may not be enough to allow the object classification. In those cases, it is crucial to explore the complementary information extracted from…