Geographic spaces is a vast and complex system involving multiple elements and nonlinear
interactions of these elements, and rich in geographical phenomena, processes and …

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 cover classification in mining areas (LCMA) is essential for the environmental
assessment of mines and plays a crucial role in their sustainable development. The shapes …

While deep learning models have been extensively applied to land-use land-cover (LULC)
problems, it is still a relatively new and emerging topic for separating and classifying wetland …

Urban informal settlements (UIS) are high-density population areas with low urban
infrastructure standards. UIS classification, which automates identifying UIS, is of great …

The availability of the sheer volume of Copernicus Sentinel-2 imagery has created new
opportunities for exploiting deep learning methods for land use land cover (LULC) image …

Urban informal settlements (UISs) are high-density population settlements with low
standards of living and supply. UIS semantic segmentation, which identifies pixels …

Map** population distribution at fine spatial scales is significant and fundamental for
resource utilization, assessment of city disaster, environmental regulation, and urbanization …

Nowadays vehicle type classification is a fundamental part of intelligent transportation
systems (ITSs) and is widely used in various applications like traffic flow monitoring, security …

The classification of urban land-use information has become the underlying database for a
variety of applications including urban planning and administration. The lack of datasets and …