Most machine learning tasks can be categorized into classification or regression problems.
Regression and classification models are normally used to extract useful geographic …

Properties of spatially explicit data are often ignored or inadequately handled in machine
learning for spatial domains of application. At the same time, resources that would identify …

In this study, a classification and performance evaluation framework for the recognition of
urban patterns in medium (Landsat ETM, TM and MSS) and very high resolution (WorldView …

Machine learning offers the potential for effective and efficient classification of remotely
sensed imagery. The strengths of machine learning include the capacity to handle data of …

With the advancement of remote sensing technology, wide usage of GPS devices in vehicles
and cell phones, popularity of mobile applications, crowd sourcing, and geographic …

Urban functional zone mapping is essential for providing deeper insights into urban
morphology and improving urban planning. The emergence of Volunteered Geographic …

In recent years, it has been widely agreed that spatial features derived from textural,
structural, and object-based methods are important information sources to complement …

Urbanization often occurs in an unplanned and uneven manner, resulting in profound
changes in patterns of land cover and land use. Understanding these changes is …

Urban morphology is characterized by a complex and variable coexistence of diverse,
spatially and spectrally heterogeneous objects. Built-up areas are among the most rapidly …

Urbanization is a fundamental trend of the past two centuries, shaping many dimensions of
the modern world. To guide this phenomenon and support growth of cities that are …