Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …

Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …

Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover map**, urban change detection …

Convolutional neural networks (CNNs) are powerful in extracting local information but lack
the ability to model long-range dependencies. In contrast, the transformer relies on …

This article presents a transformer and convolutional neural network (CNN) hybrid deep
neural network for semantic segmentation of very high resolution (VHR) remote sensing …

The fully convolutional network (FCN) with an encoder-decoder architecture has been the
standard paradigm for semantic segmentation. The encoder-decoder architecture utilizes an …

Scene understanding of high resolution aerial images is of great importance for the task of
automated monitoring in various remote sensing applications. Due to the large within-class …

The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in
almost every application (eg, agriculture, forestry, and mining) that needs observed …

Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …

Accurate crop type map** provides numerous benefits for a deeper understanding of food
systems and yield prediction. Ever-increasing big data, easy access to high-resolution …