This article examines incorporating spatiotemporal contextual information in the hope of creating smarter cities.
When trying to contextualize my topic of drones in GIS, I find myself wondering how it differentiates from just being a tool; a sensor on a new platform. One of the possible fields of research in drone GIScience is geofencing, whereby drones are programmed to not take off in certain areas and altitudes. The article mentions how drones could be used to monitor urban areas, but are not because of (good) restrictions. To create a smart city, one needs both sophisticated monitoring systems, and equally sophisticated systrems to keep out the unwanted sensors, like drones. One of the ways in which drones could be detected and regulated is through contextual sensing. For example, police use networks of microphones that collect noise data which is then processed to listen for drones. However there is not one sound that identifies a drone, and many other machines can sound similar, like a far-away leafblower. Therefore other sensors are needed to provide context to this noise. Another way drones are sensed is through optical sensors, which could identify a distant moving object and classify it based on its flightpath. However in order to distinguish a drone from say, an eagle, you would need to contextualize the optical information with thermal sensor information.
From this article I learned some terms that can be used to classify drone technology. An interesting aspect of military drones is that the US government uses “collective sensing” in order to establish the location of a target before using “classic sensors” as termed by the author to command the drone. Collective sensing is sensor data that users do not necessarily intentionally share, like there location generated from a mobile phone call. The problem though is that they do not bother to associate this data with any contextual information from other sensors, and so frequently make bad judgement calls. I think that contextual information in this form of sensing is important, but involves more of a political shift than a shift in GIScience.
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