CONTEXTS.py (): A supervised contextual post-classification method to access multiple dimensions of complex geospatial objects.

The qualitative dimensions of visible features in space can be captured by connecting spatial configurations arranged in a variety of different ways to diverse . By conceptual spaces, we intend mental concepts describing specific spatial configurations present in a geographical area, defined by the contextual relationships among their constitutive elements. This paper presents a new supervised post-classification method allowing the extraction of semantically complex spatial objects from a single image of the Earth as, for instance, diverse conceptual spaces referring to multiple dimensions of land use (temporal, cultural, social, etc.

A rapid and simple method for the extraction of biogenic silica (BSi) in phytolith-poor sediments and soils.

Phytoliths can be used to reconstruct human-nature dynamics over the long term (from decennial to centennial and millennial time scales) and may capture activities that cannot be reconstructed through other proxies. Phytoliths consist of fossil biogenic silica (BSi), formed in plant organs and then released into the soil with plant decay. When working in environmental contexts where the phytolith signal is highly diluted, as is the case in environments with a long history of land use, animal-plant interactions and open woody environments, the extraction of phytoliths remains a challenge.