Automation and Optimization

This case study investigates how AI, machine learning, sensor networks and drones detect and respond to real-time forest events, especially in the form of wildfires.

Smart forest projects using artificial intelligence, wireless sensor networks, and UAVs to detect and manage real-time forest events especially in the form of forest fires demonstrate how distinct decisions are made about what to automate and optimize—and that these are not de-politicized decisions, but rather emerge as differently configured political engagements that span machine logics, data analytics, situated forests practices, policy, and environmental change.

As the processes of artificial intelligence change through ongoing engagement with these environments, different social–political encounters emerge as they are informed by digital operations. These practices also register within a securitization of environments, where the tracking and tracing operations of drones ¹ and the distinct regimes of local, national, or planetary governance, security, and control that these technologies generate ² become central to the management of environmental change.