## 2 Materials and Methods
### Preliminary Step: Graffiti Detector  
Graffiti is certainly not standardized in shape, color, or size, and thus must be distinguished from intentional signage that may exist in the background scene of an image. Figure 2: Overview of the technical approach. Prestep: develop a robust graffiti detector to exploit a graffiti database from STORM using Faster R-CNN; Step 1: collect images of target objects in a community (TOCs) periodically from ordinary citizens; Step 2: generate an orthophoto of each facade of a TOC by stitching and blending the collected images; Step 3: graffiti detection on orthophotos, including location and size of each graffiti. Region-based convolution neural network (R-CNN) provides an appropriate solution to this problem. Recent advances in vision-based object detection are driven by the successful implementation of R-CNN (for example, see [34; 35; 36]). For building a graffiti detector, the basic requirement to use R-CNN, like all supervised learning procedures, is to retain a suitable training data set with a rich and diverse set of images captured from actual graffiti. The STORM project funded by the European Union (EU) provided a suitable data set, and we used it here for building a robust graffiti detector. During the training phase of R-CNN, a step is required to extract candidate object areas, called a region proposal. CNNs operate by representing various types of graffiti scenes present in the set of region proposals with a unique feature vector. These region proposals may vary in size and scale, and therefore need to be reshaped to match the CNN input size. After reshaping, the region proposals pass through the CNN in order to generate the associated feature vectors. Then, these feature vectors are fed into the support vector machine (SVM) classifier. Lastly, the location and shape of each bounding box are refined by using a linear regressor to reduce the localization error [34]. R-CNN has evolved into a much faster algorithm called Fast R-CNN, which achieves greater efficiency by sharing expensive computations through a region-of-interest (ROI) pooling technique [35]. R-CNN has two major drawbacks that increase its computational cost. First, it requires a separate forward pass through the CNN to check if each region proposal includes an object class of interest. Then, it trains three different internal models in order: feature extraction, object classification, and bounding box regression. To address these issues, Fast R-CNN generates a general feature map (for an entire region of each image) by passing each image (not its subregion) through the CNN and shares it with the ROI pooling layer to reduce the computational time. To find the max-pooled ROI proposals, the corresponding region of object proposals (likely positions of objects on the image) on the feature map should be extracted through max-pooling. Then, the extracted regions warp into a fixed-size feature vector and are passed to the fully connected layers. In the last layer of Fast R-CNN, the SVM classifier in R-CNN is replaced with a softmax layer for object classification [37]. A linear regression layer parallel to the softmax layer is also added to localize the object by outputting the coordinates of the bounding box. In this way, Fast R-CNN can integrate all necessary stages for training CNN (CNN feature extraction, object classifier, and bounding box coordinate regressor). To develop a robust graffiti detector, we incorporated a state-of-art object detection method called a faster region-based convolutional neural network (Faster-RCNN), which is an enhanced version of Fast R-CNN in terms of computational expense. Figure 3 briefly portrays this process. In Faster R-CNN, the selective search algorithm was modeled with a fully convolutional network, called a region proposal network (RPN) [31]. RPN in Faster R-CNN made the generation of region proposals costless. RPN allowed features generated in the forward pass of CNN to be reused to produce the region proposals as well.