This work presents an innovative image-based method for measuring wind speed and direction in urban environment using video footage. Wind dynamics are traditionally investigated at multiple spatial scales, including pollutant dispersion at the canopy level (Allwine, 2002), architectural design and outdoor comfort at the building scale (Allard &amp; Ghiaus, 2012; Holst, 2011) and the convection heat transfer coefficient <em>h</em> [Wm^&minus;2K^&minus;1] used to define the boundary conditions of numerical simulations (Oke, 2017). In 1997, Gary Settles showed that image measurement could provide non-invasive and high-resolution measurements of fluid motion. This paper presents a method for extracting anemometric data from images, validated through a proof-of-concept. We process freely accessible videos from the internet in which air masses are identified at the canopy level. Motion extraction technique is used to isolate elements of the video that are in motion. This information is fed into an optical flow algorithm that estimates an apparent velocity in [pixels/frame]. To convert the data to [km/h], the view&rsquo;s perspective is considered to ensure the conversion is accurate across the entire image. Distance mapping is performed by projecting the image onto a 3D model of the scene, and the camera's recording parameters are estimated by simulating the illumination of the scene. The anemometric data obtained are evaluated in relation to meteorological data recorded at a nearby weather station. Innovative and simple to implement, this approach provides estimates of wind speeds and directions that are both reliable and directly usable for architectural design and climate studies.