Rural landscapes, such as Italian Inner areas, hold rich cultural, ecological, and heritage values. Yet, these peculiar landscapes are characterised by isolation, demographic decline, and limited access to essential services. These conditions present a unique challenge for landscape valuation and traditional assessment methods based on their spatial characteristics. Spatial analysis provides both conceptual and operational tools to navigate the complexity of landscapes. However, current approaches still face significant methodological and theoretical challenges in effectively capturing and representing inner areas’ tangible and intangible values. The heterogeneous nature of existing spatial approaches makes it difficult to directly compare results, while the integration of perceptual data remains difficult due to the limitations of current GIS tools and models. These challenges highlight the need for more comprehensive assessment frameworks capable of overcoming existing limitations and providing a holistic understanding of landscape values.

To address these gaps, this study conducts a comparative analysis of three key landscape valuation frameworks–Ecosystem Services (ES), Landscape Services (LS) and Landscape Character Assessment (LCA). Through a semi-structured literature review, this contribution explores how these frameworks assess landscape values, and examines their respective criteria. Results show that ES and LS frameworks primarily value landscapes based on the benefits they provide to people, while LCA emphasises qualitative aspects such as perception and identity, recognising the intrinsic value of landscapes beyond their functional use. The analysis highlights critical gaps in current approaches, including their predominantly anthropocentric perspective and limited integration of multiple values into decision-making processes. We need for a more inclusive and spatially explicit valuation framework that places landscapes, especially in marginalised areas, at the centre of valuation processes and recognises their multiple, interconnected values.