## 4 Results
### Data Collection
#### 4.1.3 Data Retrieval  
To gather data on the world scale, we have setup a server-sided pipeline to retrieve data from different providers. This is currently operational in an alpha version, with 20,000 images, 4000 3D datasets, and 2700 POIs retrieved up to the end of 2023.  
Data retrieval comprises approaches to gather images, links to information resources, and 3D models from different sources. To retrieve legally accessible images, we selected CC-0 or CC-BY [285] licensed content only.  
For data retrieval, we used a series of server-side scripts in Python and PHP feeding into an SQL database and Unix file storage. The scripts are input by users locating them in our mobile application 4D City--the locations are resolved into placenames via Geonames [284].  
To avoid reloading already retrieved positions, each object's geocoordinates were processed into geotiles. These geotiles were compiled from the Universal Transverse Mercator (UTM) hours and minutes of each value. For instance, the geotile 5105 \(\times\) 1374 would comprise a bounding box with latitudes 51.05 to 51.06 and longitudes 13.74 to 13.75. Unless this simple approach leads to varying tile sizes, it enables easy coding and decoding of geotiles. To query multiple images, even in query services like Google Street View with one result per query, we further subdivided the coordinates within the bounding box--e.g., to a 3 \(\times\) 3 position grid--and queried each of these positions.  
Since May 2022, 12,983 positions have been collected and processed. By mid-2023, several thousand datasets were retrieved via these sources (Table 4).  
In the next step, we pre-classified retrieved images to sort out images not showing building exteriors. For this, we use a Python-based VGG-16 classifier, which was trained on a set of images derived via the retrieval pipeline. We used 4033 manually classified files belonging to two classes (showing/not showing architectural exterior). For training, 3227 files were used--including nine variants by data augmentation per file--and for validation, we used 806 files.  
The accuracy of the validation dataset is above 0.85 (Figure 3). A classifier was used to automate the step to identify architectural exteriors as a prerequisite for further processing in the pipeline. To gain a good ratio between true and false positives and negatives, we currently only include an image if the predictor certainty is 90% or higher.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Source** & **Grid** & **Number Retrieved** \\ \hline Points of interest & Triposo & 1000 m radius & 1684 \\  & Google Place Search & 3 \(\times\) 3 queries within grid & 1092 \\  & Wikipedia & 5 top results & Not cached \\ Images & Flickr & 1000 m radius & 8526 \\  & Europeana & Within grid & 3227 \\  & Mapillary & Within grid & 3961 \\  & Wikimedia Commons & 10,000 m radius & 448 \\  & Google Street View & 3 \(\times\) 3 queries & 2310 \\  & & 3 images (120\({}^{\circ}\) FoV) per position & 2310 \\
3D Models & Sketchfab & Keyword search using a location name retrieved via & 2.736 \\  & Geonames & 906 \\  & Mainz 3D & Full dataset & 64 \\  & Urban History 4D & Full dataset & 214 \\ \hline \hline \end{tabular}  
* After retrieval, a series of scripts perform the renaming of files to gain unique filenames and store the metadata in an SQL database.  
\end{table}
Table 4: Sources and number of retrieved datasets (10/2023).