| id |
ecaade2017_202 |
| authors |
Sollazzo, Aldo, Trento, Armando and Baseta, Efilena |
| year |
2017 |
| title |
Machinic Agency - Implementing aerial robotics and machine learning to map public space |
| source |
Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 611-618 |
| doi |
https://doi.org/10.52842/conf.ecaade.2017.2.611
|
| summary |
The research presented in this paper is focused on proposing a new digital workflow, involving unmanned aerial vehicles (UAV) and machines learning systems, in order to detect and map citizen's behaviors in the context of public spaces.Novel machinic abilities can be implemented in the understanding of the human context, decoding, through computer visions and machine learning, complex systems into intelligible outputs (Olson, 2008), mapping the relationships of our reality. In this framework, robotic and computational strategies can be implemented in order to offer a new description of public spaces, bringing to light the hidden forces and multiple layers constituting the urban habitat. The presented study focuses on the development of a methodology turning video frames collected from cameras installed on drones into large datasets used to train convolutional networks and enable machines learning systems to detect and map pedestrians in public spaces. |
| keywords |
mapping; drones; machine learning; computer vision; city |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (7,943,013 bytes) |
| references |
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