With the rapid expansion of urban built-up areas in recent years, it has become particularly
urgent to develop a fast, accurate and popularized urban built-up area extraction method …

The increasing use of deep learning techniques has reduced interpretation time and, ideally,
reduced interpreter bias by automatically deriving geological maps from digital outcrop …

Too often, the testing and evaluation of object detection, as well as the classification
techniques for high-resolution remote sensing imagery, are confined to clean, discretely …

Here, we demonstrate how deep neural network (DNN) detections of multiple constitutive or
component objects that are part of a larger, more complex, and encompassing feature can …

Deep convolutional neural networks (DCNN) have been the dominant methodology in the
field of computer vision over the last decade, using various architectural organizations of …

We are currently experiencing a deluge of high-resolution electroptical (HR-EO) remote
sensing images which can be leveraged for a diverse set of applications, ranging from …

Earth observation satellites offer vast opportunities for quantifying landscapes and regional
land cover composition and changes. The integration of artificial intelligence in remote …

In the last decade, several high resolution remote sensing benchmark datasets have been
developed and publicly released. These datasets, while diverse in design, lack the required …

Recently, many high-resolution remote sensing imagery (HR-RSI) datasets have been
released that have diverse characteristics, such as high inter-class and low intra-class …

Here we demonstrate how Deep Neural Network (DNN) detections of multiple constitutive or
component objects that are part of a larger, more complex, and encompassing feature can …