Evaluating the Efficacy of Cut-and-Paste Data Augmentation in Semantic Segmentation for Satellite Imagery

arXiv:2404.05693v1 Announce Type: cross
Abstract: Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the advancements brought about by Deep Neural Networks (DNNs), their performance in segmentation tasks is hindered by challenges such as limited availability of labeled data, class imbalance and the inherent variability and complexity of satellite images. In order to mitigate those issues, our study explores …

abstract arxiv augmentation classification cs.cv cs.lg data eess.iv environmental monitoring networks neural networks performance pixel planning satellite segmentation semantic tasks type urban urban planning