Remote sensing is crucial for mapping and developing geospatial information of inaccessible areas. In particular, supervised classification or semantic segmentation of very high resolution (VHR) satellite images are used to extract key features such …
Obtaining spatially continuous, high resolution thermal images is crucial in order to effectively analyze heat-related phenomena in urban areas and the inherent high spatial and temporal variations. Spatiotemporal Fusion (STF) methods can be applied …
Remotely-sensed satellite imagery offers crucial information on the atmosphere and the local environment, providing a broader perspective for more accurate photovoltaic (PV) power prediction. This study proposes a Deep Neural Network (DNN) framework …
In the short-term aftermath of a wildfire, quick damage assessment is significant to implement efficient disaster response, but the acquisition of reliable reference data can be difficult. Remote sensing (RS) methods using satellite imagery can …
Solar energy is a promising renewable energy source, but stable generation of photovoltaic (PV) power is largely impaired by meteorological phenomena. Ground-based weather measurements are limited in their ability to fully capture the unpredictable …
The effects of climate change and global warming are taking a toll on the Earth’s environment. Natural disasters have become more frequent and destructive while weather conditions have become more unpredictable and severe. In the summer of 2018, …