Aiym Orynbaikyzy

TL;DR: For many years, crop type classification and monitoring has been an important data source for agricultural monitoring and food security assessment studies as discussed by the authors, and reliable and accurate crop classification maps are an important source of information.

TL;DR: The results showed that, in contrast to most other studies, SAR datasets outperform optical datasets, and the optical-SAR combination outperformed single sensor predictions and no significant difference was recorded between feature stacking and decision fusion.

TL;DR: In this article , the effects of different input datasets, i.e., only optical, only Synthetic Aperture Radar (SAR), and optical-SAR data combination, and the impact of spatial feature selection were systematically tested to identify the optimal approach that shows the highest accuracy in the transfer region.

TL;DR: In this article , a semi-automated knowledge-based region growing procedure that utilizes Synthetic Aperture Radar (SAR) data, from Sentinel-1, and optical data (from Sentinel-2) for mapping land surface changes after volcanic eruptions is presented.

TL;DR: Wang et al. as discussed by the authors proposed a deep neural network-based anomaly detection strategy that combines supervised and unsupervised learning without a priori knowledge about failure time, which can automatically determine the time of failure occurrence using time series coherence values.