With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

During the past decades, multiple remote sensing data sources, including nighttime light
images, high spatial resolution multispectral satellite images, unmanned drone images, and …

We are currently living in the era of big data. The volume of collected or archived geospatial
data for land use and land cover (LULC) map** including remotely sensed satellite …

Assigning detailed use categories to buildings is a challenging and relevant task in urban
land use classification with applications in urban planning, digital city modelling and …

The acceleration of urban imagery data analysis, driven by computer vision (CV), has
created noteworthy opportunities for urban studies and planning. Data on street …

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

Few-shot remote sensing image scene classification (FS-RSISC) aims at classifying remote
sensing images with only a few labeled samples. The main challenges lie in small interclass …

Cellular automata (CA) model is a useful tool for simulating spatiotemporal changes of land-
use evolution. However, previous CA models have usually ignored the inhomogeneous and …

The cognition of up-to-date urban land-use patterns has important guiding significance for
gras** the current status of urban development, promoting sustainable urban planning …

Accurate identification of urban land-use patterns is essential to rational optimization of
urban structure. By combining the external physical characteristics of city parcels obtained …