## 4 Results and Analysis
### Extraction Results of Building Information  
#### 4.1.1 Extraction Results of Building Footprint  
In the urban area, remote sensing image data and AOI data are used as sample datasets, and training datasets, validation datasets, and test datasets are formed. AOI data covering various building types were selected and combined with the image data to make the labeled dataset, and the labeled dataset is about 500 pairs; an example of the labeled dataset is shown in Figure 5.  
As the number of training iterations increases, the training and verification conclusions of the U-net model tend to be stable, and the model can converge and be generalized. The changes in accuracy with the number of iterations during the U-net model training are shown in Figure 6. The accuracy of the model gradually increases after 100 iterations of training and finally stabilizes at about 74% in the training set. As the number of iterations \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Data** & **Source and Year** & **Description** \\ \hline Medium- and high-resolution remote sensing data & ([[https://www.tianditu.gov.cn/](https://www.tianditu.gov.cn/)]([https://www.tianditu.gov.cn/](https://www.tianditu.gov.cn/))) (2020) & Load online images for building information inspection and verification. \\ \hline High-resolution remote sensing data & ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/))) (2019) & Acquisition of 0.45 m resolution remote sensing images for building footprint identification. \\ \hline GlobeLand30 land cover data & ([[http://www.globalend30.org/](http://www.globalend30.org/)]([http://www.globalend30.org/](http://www.globalend30.org/))) (2020) & 30 m resolution for calibrating land cover data accuracy. \\ \hline Global land cover product with fine classification system at 30 m & ([[http://www.aircas.ac.cn/](http://www.aircas.ac.cn/)]([http://www.aircas.ac.cn/](http://www.aircas.ac.cn/))) (1990–2020) & 30 m resolution multi-temporal data for building age analysis. \\ \hline Street view & ([[https://map.baidu.com/](https://map.baidu.com/)]([https://map.baidu.com/](https://map.baidu.com/))) (2020–2022) & Various types of street photos to assist in manual judgment of building information. \\ \hline Deep web data & ([[https://lbs.amap.com/](https://lbs.amap.com/)]([https://lbs.amap.com/](https://lbs.amap.com/))) (2020–2022) & Multi-source AOI and POI data are pre-processed and applied to building seismic information extraction. \\ \hline All kinds of statistical yearbooks and survey bulletins & ([[http://www.tangshan.gov.cn/](http://www.tangshan.gov.cn/)]([http://www.tangshan.gov.cn/](http://www.tangshan.gov.cn/))) (2020) & Data from city statistical yearbooks and other related documents are applied to building information feature inspection. \\ \hline National standards, industry standards & ([[https://openstd.samr.gov.cn/bzgk/gb/](https://openstd.samr.gov.cn/bzgk/gb/)]([https://openstd.samr.gov.cn/bzgk/gb/](https://openstd.samr.gov.cn/bzgk/gb/))) & The criteria are applied to the judgment of building information. \\ \hline Basic geographic information data & ([[https://www.webmap.cn/](https://www.webmap.cn/)]([https://www.webmap.cn/](https://www.webmap.cn/))) (2021) & Includes data such as roads, railways, water systems, and lakes at all levels for delineating urban areas. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data source for this article. increases, the accuracy of the validation set also rises steadily and stabilizes at about 71%. Therefore, the accuracy of the model is determined to be about 71%, which proves that the model recognition effect is good.