Making maps with Small Drones – 5 Steps

Until very recently the acquisition of aerial imagery was very expensive as it could only be done by manned aircraft or satellites equipped with special cameras or sensors. The high safety risks, operational complexity and acquisition and operational costs of these mapping techniques made them completely uneconomic for small projects such as mapping a single village or agricultural holding. Enter the small civilian drone. 

Drones have significantly improved the speed and efficiency at which small areas can be safely and accurately mapped. As their prices continue to drop while their capabilities rise, drones are rapidly enabling the democratization of mapping. Walter Volkmann (President: Micro Aerial Projects L.L.C.) recently wrote an article in ICT Update on the “Five steps of making maps with small drones” which covers the basics of personal, (as opposed to corporate) mapping. To view the article, please click “here“.  See pages 24 and 25.

“Traditionally all features on a map were represented in the form of symbols whose spatial characteristics, like location, size and shape, could be mathematically defined in a spatial reference system. The underlying spatial information of features depicted in this way is referred to as vector data. Since the arrival of aerial photography, however, maps could also be made with contiguous cells, called pixels, to each of which normalised colour values are attached, just like a digital image. The data used to make a map in this way is referred to as raster data. The maps derived directly from unmanned aerial vehicles (UAV)-carried sensors are in raster form. In the classical sense, a map has to satisfy at least the following basic conditions: it has to have a scale, a north arrow and be of uniform accuracy across the mapping domain. The scale on printed maps determined its resolution as well as its accuracy. In the digital age the scale of a map can be changed by simply scrolling the wheel of your mouse. Instead of using scale to achieve desired resolution, analysts nowadays make use of the Ground Sampling Distance (GSD). The GSD represents the width and length of the area covered on the ground by one pixel on the sensor array of the camera. For any given camera, the GSD is thus a function of how high above the ground the camera is located. The accuracy of the map is in turn intrinsically linked to the GSD. For a GSD of 20 centimetres it is not possible to measure distances between discernible features more accurately than 20 centimetres. The small drone mapping workflow can be divided into five steps…”