Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W2, 3-9, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
05 Jul 2017
A. Abdul Jabbar, I. Aicardi, N. Grasso, and M. Piras Politecnico di Torino, DIATI – Department of Environmental, Land and Infrastructure Engineering, Duca degli Abruzzi 24, 10129, Torino, Italy
Keywords: Low cost sensors, Urban area, 3D model, Raspberry system, Python, GNSS Abstract. European community is working to improve the quality of the life in each European country, in particular to increase the quality air condition and safety in each city. The quality air is daily monitored, using several ground station, which do not consider the variation of the quality during the day, evaluating only the average level. In this case, it could be interesting to have a “smart” system to acquire distributed data in continuous, even involving the citizens. On the other hand, to improve the safety level in urban area along cycle lane, road and pedestrian path, exist a lot of algorithms for visibility and safety analysis; the crucial aspect is the 3D model considered as “input” in these algorithms, which always needs to be updated.

A bike has been instrumented with two digital camera as Raspberry PI-cam. Image acquisition has been realized with a dedicated python tool, which has been implemented in the Raspberry PI system. Images have been georeferenced using a u-blox 8T, connected to Raspberry system. GNSS data has been acquired using a specific tool developed in Python, which was based on RTKLIB library. Time synchronization has been obtained with GNSS receiver. Additionally, a portable laser scanner, an air quality system and a small Inertial platform have been installed and connected with the Raspberry system.

The system has been implemented and tested to acquire data (image and air quality parameter) in a district in Turin. Also a 3D model of the investigated site has been carried. In this contribute, the assembling of the system is described, in particular the dataset acquired and the results carried out will be described. different low cost sensors, in particular digital camera and laser scanner to collect easily geospatial data in urban area.

Conference paper (PDF, 2242 KB)

Citation: Abdul Jabbar, A., Aicardi, I., Grasso, N., and Piras, M.: URBAN DATA COLLECTION USING A BIKE MOBILE SYSTEM WITH A FOSS ARCHITECTURE, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W2, 3-9,, 2017.

BibTeX EndNote Reference Manager XML