## 2 Related Work  
Modern cities face several challenges and urban planners must develop urban forms that address them. Solutions to this problem based on ML and DL algorithms are now possible [3], thanks to the large volumes of data generated by new information and communication technologies (smartphones, GIS, IoT, sensors, etc.) and the availability of high computational power. For instance, there are works that estimate urban growth using ML approaches [8], predict building heights, based only on open-access geospatial data on urban forms, such as building footprints and street networks [9], predict building-level waste generation [4], access the urban environment quality [5], detect structure damage [6], predict traffic [7] and others. The manual analysis of street-level photography has been extensively explored as an efficient strategy for measuring urban characteristics linked with demographic or socioeconomic indicators, e.g., with respect to health-related behaviors [10], obesity [11], or pedestrian safety [12]. These \"audit tools\" allow researchers to quantify characteristics at the neighborhood level without the need to actually visit each location. Although initial attempts to automate this process were not effective enough to replace manual processing (e.g., [13]), the advent of effective, deep learning-based computer vision methods has enabled the fully automated extraction of urban indicators from street images [14]. For example in [15], the authors use a mask regional convolutional neural network (Mask R-CNN) to detect and locate trees from street images and use a monocular depth estimation method to estimate precise tree location. Another study [16] uses a faster region-convolutional neural network (Faster R-CNN) to measure street cleanliness from images that are captured by high-resolution cameras installed on vehicles. Similarly in [17], the authors use a segmentation and classification workflow to measure the presence of unhealthy food advertisements in Liverpool, UK. A significant body of work goes beyond the measurement of individual urban features, to the use of computer vision for estimating demographic and socioeconomic indicators. In a large-scale study in the USA, the authors of [18] propose to automatically analyze the make and model of cars in street-level images to successfully estimate the demographic markup of urban neighborhoods. In [19], the authors leverage deep multiple-instance learning to eliminate the requirements for manually annotated data for the direct estimation of socioeconomic indicators from cars detected in street images. In that case, cars are detected using Faster-RCNN, and a deep learning model is used for the estimation of demographic indicators. In [20], the authors use Siamese CNNs to analyze images of properties/buildings obtained from Google Street View to map gentrification over a 9-year period in Ottawa, Canada. Street-level imagery is also used as input to a multi-modal U-Net architecture in [21], to measure income, overcrowding, and environmental deprivation in urban areas. Other examples include linking blue and green spaces with geriatric depression [22], the estimation of infection incidence rate [23], and the estimation of social, environmental, and health inequalities in major UK cities [24]. Additionally, various platforms have been developed to assist in many urban challenges with the use of ML and DL techniques, such as improving the visitor experience in urban parks [25], monitoring urban street lighting infrastructure [26], classifying the type of the streets [27], predicting parking spaces [28], and urban mobility [29]. The proposed StreetScouting platform extends these efforts by introducing the architecture and implementation details of an end-to-end deep learning-based automated urban feature extraction platform including:  
1. High-rate data acquisition and pre-processing from a 360\({}^{\circ}\) camera.