# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
## 2 Materials and Methods
### Geographic Image Processing and Latitude and Longitude Determination  
When analyzing drone images, it is crucial to minimize coordinate system errors by accurately applying the images to maps. The coordinate system of the building map and drone image must be precise to ensure accurate analysis. The building's coordinate system was defined as 'Korea Central Belt 2010', with a possible error of approximately 10 cm. Meanwhile, the accuracy of the drone image could have errors of up to 50 cm using RTK GPS. The input test image resulted in a predicted output in two-pixel classes that could be used to locate and update a building's presence on the map. Before dividing the image into 10 \(\times\) 10 labeling, the TIFF image's tag was read to calculate the coordinate information using the 'Korea Central Belt 2010' coordinate system. Once the location of the roof was identified through semantic segmentation, the corresponding TIFF Cartesian coordinate system values were determined. To calculate the position of an image pixel matrix in the Cartesian coordinate system, the latitude and longitude positions of the coordinate system, WGS84, projected onto the image must be determined. Therefore, it is important to work with these two coordinate systems in a unified manner.  
The image labeling process involves assigning a roof or non-roof label to each pixel in the image, as illustrated in Figure 4. The sizes of the input and output images were M = 256 and N = 256, respectively, and they were in RGB format. The labeled images were used to train the network. When a test image is input into a trained network, the network outputs a predicted image showing which pixels are roofs and which are not.  
The image labeling process involves assigning a roof or non-roof label to each pixel in the image, as illustrated in Figure 4. Panel (a) shows the image with the corresponding coordinate system. This coordinate system is necessary because standard Cartesian and image pixel coordinates differ. Converting the image coord  
Figure 4: (**a**) Image matrix used for recognition, (**b**) ground truth showing labeled pixels, (**c**) detected roofs inferred by algorithms, and (**d**) bounding box made by detected pixels.  
coordinates requires an accurate coordinate system. Our study aimed to determine the geographical locations of the detected buildings, and hence, the detected pixel locations needed to be transformed into their corresponding geographical coordinates.  
Figure 4b shows the image with ground truth labels, indicating which pixels represent roofs and which do not. Panel (c) presents the inferred roofs obtained using the proposed algorithm. Finally, in panel (d), the bounding boxes are generated based on the inferred roofs. The geographical coordinates of these bounding boxes were extracted and compared with those of the registered buildings in the cadastral map to validate their correspondence.  
The input and output images had sizes of M = 256 and N = 256, respectively, and were represented in RGB format. These labeled images were used to train the network. Once a test image is fed into the trained network, the network predicts the location of the roof by outputting an image that indicates the pixels corresponding to the roof.