The study was conducted with the objective of mapping landscape cover of Nechsar National park in Ethiopia to produce spatially accurate and timely information on land use and changing pattern. Monitoring provides the planners and decision-makers with required information about the current state of its development and the nature of changes that have occurred. Remote sensing and Geographical Inf...

Abstract Text classification is a widely studied problem and has broad applications. In many real-world problems, the number of texts for training models limited, which renders these prone to overfitting. To address this problem, we propose SSL-Reg, data-dependent regularization approach based on self-supervised learning (SSL). SSL (Devlin et al., 2019a) an unsupervised that defines auxiliary t...

Graph neural networks have pushed state-of-the-arts in graph classifications recently. Typically, these methods are studied within the context of supervised end-to-end training, which necessities copious task-specific labels. However, real-world circumstances, labeled data could be limited, and there a massive corpus unlabeled data, even from unknown classes as complementary. Towards this end, ...

Abstract Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. To this end, learning optimal functions from data, known as metric learning, aims to facilitate supervised classification, particularly in high-dimensional spaces where visualization is challenging or infeasible. In particular, the Mahalanobis default choice due si...

Commercially available high-resolution satellite imagery from sensors such as IKONOS and QuickBird are important data sources for a variety of urban area applications including infrastructure feature extraction and land cover mapping. Land cover maps from medium and high-resolution imagery are typically generated through supervised spectral classification of multispectral imagery. Supervised cl...

Semi-supervised classification is an interesting idea where classification models are learned from both labeled and unlabeled data. It has several advantages over supervised classification in natural language processing domain. For instance, supervised classification exploits only labeled data that are expensive, often difficult to get, inadequate in quantity, and require human experts for anno...