<p>Turbulent structures can be observed using horizontal scans from single Doppler lidar or radar systems. Despite the ability to detect the structures manually on the images, this method would be time-consuming on large datasets, thus limiting the possibilities to perform studies of the turbulent structures properties over more than a few days. In order to overcome this problem, an automated classification method was developed, based on the observations recorded by a scanning Doppler lidar (LEOSPHERE WLS100) and installed atop a 75-m tower in Paris city centre (France) during a 2-months campaign (September-October 2014). The lidar recorded 4577 quasi-horizontal scans for which the turbulent component of the radial wind speed was determined using the velocity azimuth display method. Three turbulent structures types were identified by visual examination of the wind fields: unaligned thermals, rolls and streaks. A learning ensemble of 150 turbulent patterns was classified manually relying on in-situ and satellite data. The differences between the three types of structures were highlighted by enhancing the contrast of the images and computing four texture parameters (correlation, contrast, homogeneity and energy) that were provided to the supervised machine learning algorithm (quadratic discriminate analysis). Using the 10-fold cross validation method, the classification error was estimated to be about 9.2 % for the training ensemble and 3.3 % in particular for streaks. The trained algorithm applied to the whole scan ensemble detected turbulent structures on 54 % of the scans, among which 34 % were coherent turbulent structures (rolls, streaks).</p>