## 1 Introduction  
Rising temperatures and extreme heat events have been recorded across the globe in recent decades [1]. Urban areas specifically, home to a majority of the world's population, have experienced higher temperatures caused by the morphology of the built environment [2]. In the United States, average daytime surface temperatures in urban areas are up to 4 \({}^{\circ}\)C higher than those in outlying areas [2], and in extreme cases, surface nighttime temperatures can be up to 10 \({}^{\circ}\)C higher compared to the rural countryside [3]. The high probability of future extreme heat events is also documented in many studies [4; 5; 6]. It has been demonstrated that cities are experiencing faster warming rates than rural areas [7]. Research has indicated that more casualties have resulted from heat waves than other hazardous weather events such as hurricanes, floods, and tornadoes combined [8]. An important part of understanding the urban thermal environment is how the surrounding landscape features and processes influence the magnitude of heat. Proposed components critical to map urban heat include cloud cover, vegetation, wind speed, the albedo of impervious surface, and road traffic intensity [9]. Other urban characteristics such as surface roughness [10], pavement composition [11] and anthropogenic heat flux generated by cultural activities [12] have also been shown to be associated with higher urban temperatures. Yet it remains challenging to collect data to represent urban thermal environments cost-effectively, with high accuracy and detail, and in a timely manner. Studies on temperature and urban environments using remote sensing technology have been common over the last couple of decades [13; 14; 15]. While leveraging satellite imagery provides the most affordable way to collect data covering large areas, most of the free satellite datasets suffer from limited spatial resolutions (e.g., 30 m for Landsat, 250 m for MODIS), which are unable to capture the temperature patterns and intricate structures of complex urban matrices at a fine scale. To address this shortcoming, the application of lower-cost mobile thermal sensors that can be mounted on vehicles including cars [13; 16; 17; 18], motorbikes [19], or bicycles [18] has become popular. These sensors offer the benefit of enabling very high-resolution temperature data collection, thus allowing for a better understanding of small variations of the temperature found within an urban area [20]. On the other hand, the increasing availability of other very-high-resolution geospatial data, such as aerial photos and Light Detection and Ranging (LiDAR) data, enables detailed examination of urban morphometric features. These datasets have been employed to extract various environmental features like tree canopy shape [11], tree shade [15], vegetation types [21], residential lawn area, land cover types, and building structure (height or volume) [22] to explore their relationship with urban thermal patterns. Specifically, mobile thermal sensors were used to map detailed temperature patterns in various urban settings, including Portland, Oregon [17]; Washington D.C; Baltimore, Maryland; and Richmond, Virginia [18]. Their studies revealed vegetation and building metrics derived from LiDAR, building footprint vector files, and the Sentinel-2 satellite imagery could effectively predict the collected air temperatures. Among all the models they have tested, including multiple linear regression, classification regression tree combined with multiple linear regression, and the random forest (RF) machine learning algorithm, the RF algorithm proved to be the most robust. This finding has also been confirmed by other similar studies in Londrina, Brazil (-575,000 population) [23] and Nanjing city, China (-8.2 million population) [24]. While recent progress has been made in understanding urban heat spatial heterogeneity, more research in a variety of urban settings would be useful.