An Automatic Extraction Method for Hatched Residential Areas in Raster Maps Based on Multi-Scale Feature Fusion
Bibliographic Data
| ID | 22032071 |
|---|---|
| Authors | Jianhua Wu (0000-0001-6423-1762, Jiangxi Normal University), Jiaqi Xiong (0009-0004-6626-5914, Jiangxi Normal University, corresponding author), Yu Zhao (0000-0001-9446-644X, Jiangxi Normal University), Xiang Hu (0000-0001-5310-010X, Jiangxi Normal University) |
| Year | 2021 |
| Volume | 10 |
| Issue | 12 |
| Pages | 831 |
| Publication date | 2021-12-10 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10120831 |
| OpenAlex | W4200411625 |
| Language | EN |
| References cited | 13 |
Extracting the residential areas from digital raster maps is beneficial for research on land use change analysis and land quality assessment. In traditional methods for extracting residential areas in raster maps, parameters must be set manually; these methods also suffer from low extraction accuracy and inefficiency. Therefore, we have proposed an automatic method for extracting the hatched residential areas from raster maps based on a multi-scale U-Net and fully connected conditional random fields. The experimental results showed that the model that was based on a multi-scale U-Net with fully connected conditional random fields achieved scores of 97.05% in Dice, 94.26% in Intersection over Union, 94.92% in recall, 93.52% in precision and 99.52% in accuracy. Compared to the FCN-8s, the five metrics increased by 1.47%, 2.72%, 1.07%, 4.56% and 0.26%, respectively and compared to the U-Net, they increased by 0.84%, 1.56%, 3.00%, 0.65% and 0.13%, respectively. Our method also outperformed the Gabor filter-based algorithm in the number of identified objects and the accuracy of object contour locations. Furthermore, we were able to extract all of the hatched residential areas from a sheet of raster map. These results demonstrate that our method has high accuracy in object recognition and contour position, thereby providing a new method with strong potential for the extraction of hatched residential areas
Cartography · Computer vision · Conditional random field · Geography · Raster graphics · Computer Science · Land Use and Ecosystem Services · Remote Sensing and Land Use · Remote-Sensing Image Classification · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |