The ongoing global phenomenon of people migrating to cities is referred to as urbanization and primarily
manifests itself in the continuous and often rapid spatial expansion of urban agglomerations. Nevertheless,
the dimension and structuring behind this process can be considered as a spatial continuum
ranging from rural to urban settlements. Accordingly, gathering a detailed global knowledge about the
size, form (e.g., compact or spread) and spatial distribution (e.g., dispersed or nucleated) of different
types of settlements represents a major issue to better understand urbanization and develop effective
mitigation, adaptation and management strategies. In such context, this paper introduces a novel procedure
specifically designed to characterize settlement properties and patterns, which can be applied at
high spatial resolution (hence being capable of accounting even for single villages and dwellings) from
the local up to continental or global scale using binary maps (i.e., figure-ground diagrams describing the
spatial distribution of built-up and non-built-up areas) derived from Earth observation (EO) products.
Starting from a binary mask depicting built-up and non-built-up areas over a given study region, the
proposed method first delineates settlement objects. Next, a spatial network is created where the nodes
correspond to the centroids of the extracted objects and the edges connect neighboring objects lying
within a prefixed Euclidean distance from each other. Suitable attributes describing the geometrical
properties of the associated objects are then computed for all the nodes and specific weights of interest
are assigned to the edges of the network. Finally, indexes modeling the relationships between different
nodes are calculated to properly characterize the relevance of different settlements within the spatial
network. Several experimental results obtained on the basis of figure-ground diagrams derived from
existing EO-based geo-information layers from local to continental level assess the capabilities of the
presented approach and demonstrate its potential to provide key information to quantitatively and
qualitatively characterize settlement properties and patterns in any spatial detail (depending on the
spatial resolution of the input data) and at arbitrary spatial scales.