Tehran, the bustling capital of Iran, is internationally recognized as one of the urban areas most susceptible to natural hazards. Among the various threats, landslides constitute a significant danger to infrastructure, buildings and the inhabitants. The general exposure to hazards initiated the creation of multiple landslide susceptibility maps, especially for the mountainous terrain north of the city. These maps are pivotal for urban planning and disaster mitigation efforts.

Despite the high relevance of hazard and susceptibility maps in the study area, a systematic comparison of these susceptibility maps has not been undertaken so far, mainly due to the lack of accessible data. Primarily available maps exist in the form of published images, precluding detailed, pixel-level analyses that could reveal insights into their relative accuracy and effectiveness.

Addressing the data accessibility challenge, the current study introduces an innovative approach for extracting quantitative information from all published maps in the area of interest. The applied method leverages a modified k-means clustering algorithm, traditionally limited by its sensitivity to initial cluster centres and less suited for colour quantization. However, our proposed approach showcases reliability when applied to thematic maps characterized by monochromatic colour schemes.

Our research undertook a detailed comparison of 14 landslide susceptibility maps, all intersecting the northern area of Tehran, and encompassing various scales. The comparative analysis proved a significant discordance between the published maps. It can be observed that map performance is predominantly influenced by factors such as data resolution, methodological approaches, and parameter selection, rather than by the sheer number of parameters. Through this comparative assessment, we have identified critical parameters that greatly influence landslide susceptibility predictions.

A striking conclusion of the present study is the absence of a singularly superior methodology amongst the numerous scientific approaches assessed. Although all methods are established and reputable within the scientific community, our results demonstrate that they yield clear discrepancies when applied to the context of Tehran's landscape. In the context of landslide hazard evaluation, susceptibility mapping constitutes the foundational element within our Integrated Disaster Risk Management (IDRM) framework. Consequently, our findings highlight significant challenges concerning the practical implementation of these maps.

In conclusion, the present study points to complex problems of creating accurate landslide susceptibility maps and identified significant discrepancies that can arise from methodological variations. These findings demonstrate the urgent need for further research to deepen our understanding of landslide susceptibility mapping. It is mandatory that future studies continue to refine these techniques to enhance their predictive power, reduce uncertainties, and, ultimately, support the resilience of urban areas and societies. This is one of the main tasks in the project: “Geovisual analysis, evaluation and monitoring of geohazards and their related landforms” as part of the BMBF INCREASE research program (Förder-Nr. 01DK20101H).