  Unmanned Aerial Vehicles (UAVs) equipped with high-resolution sensors enable
extensive data collection from previously inaccessible areas at a remarkable
spatio-temporal scale, promising to revolutionize fields such as precision
agriculture and infrastructure inspection. To fully exploit their potential,
developing autonomy algorithms for planning and perception is crucial. This
dissertation focuses on developing planning and perception algorithms tailored
to UAVs used in monitoring applications.
  In the first part, we address object monitoring and its associated planning
challenges. Object monitoring involves continuous observation, tracking, and
analysis of specific objects. We tackle the problem of visual reconstruction
where the goal is to maximize visual coverage of an object in an unknown
environment efficiently. Leveraging shape prediction deep learning models, we
optimize planning for quick information gathering. Extending this to multi-UAV
systems, we create efficient paths around objects based on reconstructed 3D
models, crucial for close-up inspections aimed at detecting changes.
  Next, we explore inspection scenarios where an object has changed or no prior
information is available, focusing on infrastructure inspection. We validate
our planning algorithms through real-world experiments and high-fidelity
simulations, integrating defect detection seamlessly into the process.
  In the second part, we shift focus to monitoring entire environments,
distinct from object-specific monitoring. Here, the goal is to maximize
coverage to understand spatio-temporal changes. We investigate slow-changing
environments like vegetative growth estimation and fast-changing environments
such as wildfire management. For wildfires, we employ informative path planning
to validate and localize fires early, utilizing LSTM networks for enhanced
early detection.
