## 4 UAVs and Artificial Intelligence
### AI-Empovered UAVs: Supporting Adaptation and Mitigation Strategies  
AI and machine learning techniques can enhance multiple aspects of UAV-based data vital in climate change research, ranging from data acquisition to analytics. Advanced computer vision enables UAVs to autonomously detect features, such as forest fire boundaries or flood extents, improving the monitoring efficiency [192]. In addition, processing large volumes of UAV images using deep learning supports land-cover classifications at regional scales relevant to carbon and hydrologic cycle research. Numerous studies have found that AI agents also show promise for optimizing UAV flight planning and coordination when collecting climate data [193]. Onboard AI computing can guide autonomous target tracking, sensor triggering, and dynamic obstacle avoidance to reduce the pilot workload during long UAV climate research flights [187]. Numerous studies have examined how UAVs can utilize artificial intelligence and automated control for navigation and collision avoidance. Several recent publications focused on this concept [187; 194; 195], where deep neural networks were employed as a machine learning technique to train drone systems using the data from sensors and cameras. In a study by [PERSON] et al. [187], the researchers built a dataset of over 11,500 crash scenarios to develop a deep learning model that categorized collision risks and instructed drones on how to avoid them. As the datasets grow through groups sharing drone data, deep learning is expected to progress rapidly, leading to drones with more advanced situational analysis, decision-making, and autonomous flight capabilities. AI creates additional opportunities to integrate UAVs into multivariate climate and Earth system models, assimilating aerial data streams to strengthen model predictive capabilities [196]. As climate impacts accelerate, deep neural networks fed with diverse labeled UAV training data will become invaluable for managing and understanding the climate threats. Investing in AI and UAV integration can significantly extend the value of drones for addressing interconnected environmental challenges. UAVs provide an agile means of gathering high-resolution aerial data to feed into AI-driven predictive models for climate change analysis. For instance, machine learning can help construct robust models forecasting the impacts based on recognizing patterns and correlations in the multivariate time-series data captured by UAVs [197]. Moreover, UAV surveys tracking urban vegetation health, temperatures, demographics, and land use over time can train models to forecast heat illness vulnerability at neighborhood scales under warming projections [198]. The role of neural networks in leveraging UAV datasets also shows promising potential in predicting extreme weather and hazards exacerbated by climate shifts. To refine the predictions, models can continually ingest updated UAV inputs on forest moisture, storm energetics, coastal erosion, or other indicators [197]. Real-time UAV video feeds before disasters, such as hurricanes or wildfires, can improve AI damage and loss models to guide emergency responses [199]. Capturing diverse training data through UAV monitoring enables for more a accurate AI model encoding of complex climate-environment relationships [200]. Deep learning algorithms can also help uncover localized variables and relationships driving microclimate patterns that legacy models overlook from the UAV data [201]. For cities developing adaptation plans, machine learning can help strategically optimize new green space, cool surface, and green infrastructure investments to affordably mitigate warming [202]. Moreover, AI enables a more automated and insightful analysis of UAV-derived data to track climate shifts in forest ecosystems. Repeated UAV surveys allow training AI agents to identify subtle spectral indicators of various climate hazards [81].