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The city management is increasingly supported by information technologies, leading to paradigms such as smart cities, wheredecision-makers, companies and citizens are continuously interconnected. 3D modelling turns of great relevance when the city has tobe managed making use of geospatial databases or Geographic Information Systems. On the other hand, laser scanning technologyhas experienced a significant growth in the last years, and particularly, terrestrial mobile laser scanning platforms are being more andmore used with inventory purposes in both cities and road environments. Consequently, large datasets are available to produce thegeometric basis for the city model; however, this data is not directly exploitable by management systems constraining theimplementation of the technology for such applications.  This paper presents a new algorithm for the automatic detection of zebra crossing. The algorithm is divided in three main steps: roadsegmentation (based on a PCA analysis of the points contained in each cycle of collected by a mobile laser system), rasterization(conversion of the point cloud to a raster image coloured as a function of intensity data), and zebra crossing detection (using theHough Transform and logical constrains for line classification). After evaluating different datasets collected in three cities located inNorthwest Spain (comprising 25 strips with 30 visible zebra crossings) a completeness of 83% was achieved

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

SIMPLE APPROACHES TO IMPROVE THE AUTOMATIC INVENTORY OF ZEBRA CROSSING FROM MLS DATA