Freshwater resources on islands are critically important, motivating the study of surface freshwater extraction. However, optical remote sensing methods face challenges from cloud cover, coastal background noise, and difficulty in distinguishing saltwater in seawater intrusion zones. To address these issues, we propose...
Two improved models enhanced the accuracy of landslide identification and resistance to interference, demonstrating potential for landslide monitoring and emergency response.
This study proposes a hybrid transfer learning-based framework for seasonal classification using satellite images collected from 81 provinces of Türkiye and shows that both the choice of pretrained feature extractor and the classifier affect seasonal classification performance.
Accurately assessing soil moisture is indispensable for agriculture, water resources management, and hydrology studies. The priority of these forecasts is even higher, especially in areas with different climatic conditions and terrain characteristics. Most satellite-based approaches currently in use are not able to eff...
Accurate mapping of surface water bodies is essential for dam monitoring, irrigation planning, and sustainable water resource management. Conventional methods based on spectral indices, such as NDWI, can face challenges in complex environments due to spectral ambiguity, seasonal variability, and urban or vegetated surf...
Automatic classification of military aircraft in satellite imagery is a challenging problem with a high risk of error due to the limited pixel area of targets, variations in image resolution and illumination conditions, background complexity, and strong visual similarity among classes. In this study, a deep learning ap...
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