In many application domains, remotely sensed (RS) data are essential for disaster
monitoring, climate forecasting, and remote surveillance. It perceives and gathers …

Abstract Land Use Land Cover (LULC) classification is pivotal to sustainable environment
and natural resource management. It is critical in planning, monitoring, and management …

Convolutional neural network (CNN) comprises the most common and extensively used
network in the field of deep learning (DL). The design of CNNs was influenced by neurons …

Land-area classification (LAC) research offers a promising avenue to address the intricacies
of urban planning, agricultural zoning, and environmental monitoring, with a specific focus …

For proper planning of urban infrastructures such as road networks, pipelines, and other
linear engineering structures, it is necessary to construct precise land use and land cover …

In this article, we present an approach to land-use and land-cover (LULC) mapping from
multispectral satellite images using deep learning methods. The terms satellite image …

Our aim is to propose a new deep learning framework approach which uses an ensemble of
convolutional neural network (CNN) for land use-land cover mapping. Every CNN layer was …

Classification tasks on land cover (LC) mapping are challenging due to the complex and
heterogeneous characteristics of remote sensing images (RSIs). Current LC classifications …

Land Use and Land Cover (LULC) maps serve as an important component for natural
resource monitoring. They convey insights into the utilization patterns of the Earth's land …

As one of the important components of Earth observation technology, land use and land
cover (LULC) image classification plays an essential role. It uses remote sensing techniques …