## 3 UAV Applications in Climate Change Research
### Data Collection and Processing Platforms  
As outlined above, UAVs provide ample opportunities for a more accurate and rapid monitoring and assessment of numerous climate change challenges in the built environment. The existing literature reveals various frameworks to structure UAV operations and data collection workflows. [PERSON] et al. [175] proposed a comprehensive multi-stage model for UAV-based sensing encompassing: (i) defining the operational scope and objectives, (ii) flight planning and route design, (iii) UAV deployment for planned data acquisition, (iv) sensor data gathering, (v) the processing and analysis of the collected data, and (vi) interpretation and communication of analysis results. Similar staged methodologies aim to provide end-to-end guidance for executing UAV remote sensing missions from initial planning through data extraction and interpretation methods. A systematic workflow is critical for effectively leveraging UAV capabilities across diverse monitoring and inspection applications. This multistep framework can be aggregated into three primary stages: (1) mission planning, (2) data acquisition, and (3) data output or processing. Figure 11 illustrates the key parameters associated with each stage and the average time spent. The mission planning step involves flight planning based on the sensing objectives. Key mission parameters, such as altitude, speed, overlap, and routing, are determined to ensure the desired area is covered at an appropriate resolution. This frames key mission parameters, such as sensor payload, flight duration, altitudes, and required resolution. Sensor payload selection is also critical to gather relevant information, whether RGB, multispectral, thermal, or LiDAR systems. With the flight mapped out, the UAV is deployed to autonomously follow the programmed path and trigger sensor recording [176]. After mission scoping, the flight planning process includes the design of detailed flight plans specifying automated flight routes, sensor triggering locations, altitudes, and speed. The flight parameters ensure full coverage at the desired resolutions, orientations, and overlap percentages between each captured image. Additionally, mission planning includes data collection parameters, such as sensor calibrations and ground control point (GCP) design. The mission planning time ranges from hours to weeks depending on the mission scale and complexity, representing around 5% of the overall data acquisition process [176]. Thorough planning is crucial for successful data acquisition. Figure 11: Typical data acquisition and processing frameworks and output needs for different UAV application categories. The data acquisition stage encompasses UAV deployment, in-field pre-flight checks, data capture flights, recording sensor data, and calibration of GCPs. The UAV-certified pilot operates UAVs according to the mission plan. Test flights validate the aircraft performance before conducting the full flights. The UAV then autonomously navigates the planned routes using GPS, while recording imagery, video, LiDAR, or other sensor data. The total flight time depends on the sampling scale, from less than an hour for a small site to days for large areas. Sensor and telemetry data are retrieved post-flight for cleaning and processing, which is a process that can take up to 20% of the total process time. In the data processing stage, the collected aerial data enter processing workflows to generate analysis-ready outputs. As shown in Figure 11, the processing steps vary by data type, but often involve photogrammetry, geospatial referencing, 3D modeling, topography modeling, feature detection and classification, and/or index calculation. For example, overlapping UAV images are stitched into orthomosaic maps and digital surface models through structure-from-motion algorithms. LiDAR point clouds also undergo classification and terrain modeling. Specialized software streamlines the processing; however, a skilled analysis is required to ensure the data quality.