Developing regions are often characterized by large areas that are poorly reachable or explored. The mapping of these regions and the census of roaming populations in these areas are often difficult and sporadic.

In this paper we put forward an approach to aid area surveying which relies on autonomous drone mobility. In particular we illustrate the two main components of the approach. An efficient on-device object detection component, built on Convolutional Neural Networks, capable of detecting human settlements and animals on the ground with acceptable performance (latency and accuracy) and a path planning component, informed by the object identification module, which exploits Artificial Potential Fields to dynamically adapt the flight in order to gather useful information of the environment, while keeping optimal flight paths. We report some initial performance results of the on board visual perception module and describe our experimental platform based on a fixed-wing aircraft.