AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNING

12 citations as recorded by crossref.

1. A Survey of Label Placements in Visualizations Y. Fan et al. https://doi.org/10.3724/SP.J.1089.2023.19913
2. Automatic Annotation of Map Point Features Based on Deep Learning ResNet Models Y. Zhang et al. https://doi.org/10.3390/ijgi14020088
3. Multiple Geographical Feature Label Placement Based on Multiple Candidate Positions in Two Degrees of Freedom Space J. Deng et al. https://doi.org/10.1109/ACCESS.2021.3120289
4. A point-feature label placement algorithm based on spatial data mining W. Cao et al. https://doi.org/10.3934/mbe.2023542
5. A SegNet‐Based Approach for Road Label Placement Integrating Geometric and Textual Information H. Yu et al. https://doi.org/10.1111/tgis.70080
6. Construct boundaries and place labels for multi-class scatterplots Z. Li et al. https://doi.org/10.1007/s12650-021-00791-x
7. Label Placement Challenges in City Wayfinding Map Production—Identification and Possible Solutions L. Harrie et al. https://doi.org/10.1007/s41651-022-00115-z
8. Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethics Y. Kang et al. https://doi.org/10.1080/15230406.2023.2295943
9. The effect of map label language on the visual search of cartographic point symbols P. Cybulski & V. Krassanakis https://doi.org/10.1080/15230406.2021.2007419
10. An MPI-based parallel genetic algorithm for multiple geographical feature label placement based on the hybrid of fixed-sliding models M. Lessani et al. https://doi.org/10.1080/10095020.2024.2313326
11. A Novel Parallel Algorithm with Map Segmentation for Multiple Geographical Feature Label Placement Problem M. Lessani et al. https://doi.org/10.3390/ijgi10120826
12. A feasibility study of applying generative deep learning models for map labeling R. Oucheikh & L. Harrie https://doi.org/10.1080/15230406.2023.2291051