Extracting Toponyms from Maps of Jerusalem: Difference between revisions
Jump to navigation
Jump to search
Line 49: | Line 49: | ||
| align="center" |Week 6 | | align="center" |Week 6 | ||
| | | | ||
* Port MapKurator into Windows-based Python | * Port MapKurator's Spotter tool and model weights into Windows-based Python. | ||
* Select two (later four) maps to | * Select two (later four) maps to use when implementing, evaluating, and fine-tuning MapKurator's model. | ||
| align="center" | ✓ | | align="center" | ✓ | ||
|- | |- | ||
Line 63: | Line 63: | ||
| | | | ||
* Create ground truth labels for second map. | * Create ground truth labels for second map. | ||
* Implement 1:1-matched precision and recall via IoU (geometry) and normalized Levenshtein (text) | * Implement 1:1-matched precision and recall via IoU (geometry) and normalized Levenshtein (text). | ||
* Calculate baseline accuracy statistics. | |||
| align="center" |✓ | | align="center" |✓ | ||
|- | |- | ||
Line 69: | Line 70: | ||
| align="center" |Week 9 | | align="center" |Week 9 | ||
| | | | ||
* Implement multi-layer pyramid | * Implement multi-layer pyramid application of MapKurator's Spotter. | ||
| align="center" |✓ | | align="center" |✓ | ||
|- | |- | ||
Line 76: | Line 77: | ||
| | | | ||
* Create ground truth labels for third map. | * Create ground truth labels for third map. | ||
* Implement toponym rectification and amalgamation | * Implement toponym rectification and amalgamation on pyramid-derived toponyms. | ||
| align="center" |✓ | | align="center" |✓ | ||
|- | |- | ||
Line 82: | Line 83: | ||
| align="center" |Week 11 | | align="center" |Week 11 | ||
| | | | ||
* Calculate accuracy statistics | * Calculate pyramid accuracy statistics. | ||
* Fine-tune toponym rectification and amalgamation. | |||
* Deliver Midterm presentation. | * Deliver Midterm presentation. | ||
| align="center" | ✓ | | align="center" | ✓ | ||
Line 91: | Line 93: | ||
* Launch Wiki. | * Launch Wiki. | ||
* Group words into toponyms via polygon size and location. | * Group words into toponyms via polygon size and location. | ||
* Apply NLP tools to correct toponyms. | * Apply NLP tools to correct toponyms based on MapKurator strategy. | ||
| align="center" | | | align="center" | | ||
|- | |- |
Revision as of 13:47, 5 December 2023
Introduction & Motivation
1. Simple Muqarnas: the ceilings have plane surfaces only
[[]] |
Methodology
Create the shapes in 2D
Transform the shapes in 3D
The Arc from the Method of Masons
The 3D Projection
Create the 2D plan and the 3D volume
- Step 1:
- Step 2:
In practice
Project Timeline
Timeframe | Task | Completion |
---|---|---|
Week 4 |
|
✓ |
Week 5 |
|
✓ |
Week 6 |
|
✓ |
Week 7 |
|
✓ |
Week 8 |
|
✓ |
Week 9 |
|
✓ |
Week 10 |
|
✓ |
Week 11 |
|
✓ |
Week 12 |
|
|
Week 13 |
|
|
Week 14 |
|
Results
Limitations
Future work
Github Repository
References
Literature
- Kim, Jina, et al. "The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps." arXiv preprint arXiv:2306.17059 (2023).
- Li, Zekun, et al. "An automatic approach for generating rich, linked geo-metadata from historical map images." Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020