In recent years, the combination of deep learning and remote sensing has been a boiling
state. However, because of the difference between remote sensing images and natural …

Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital
role in many environment modeling and land-use inventories. In this study, a hybrid feature …

Land cover and its change are crucial for many environmental applications. This study
focuses on the land cover classification and change detection with multitemporal and …

Remote sensing is essential in construction management by providing valuable information
and insights throughout the project lifecycle. Due to the rapid advancement of remote …

During the 21st century, artificial intelligence methods have been broadly applied in
geosciences to simulate complex dynamic ecosystems, but the use of artificial intelligence …

Predicting future land change is crucial in anticipating societal and environmental impacts
and informing responses at different scales. We designed an integrated, high-resolution …

Detailed land cover change trajectory offers a better opportunity for understanding the
dynamic of land surface process. However, change information contained in training …

When a landslide happens, it is important to recognize the hazard-affected bodies
surrounding the landslide for the risk assessment and emergency rescue. In order to realize …

Unaddressed imbalance of multitemporal land cover (LC) data reduces deep learning (DL)
model usefulness to forecast changes. To manage geospatial data imbalance, there is a …

Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM)
architectures, have obtained successful outcomes in timeseries analysis tasks. While RNNs …