Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning

Keyphrases

• Reinforcement Learning 100%
• Vector Data 100%
• Historical Maps 100%
• Georeferencing 100%
• Automatic Alignment 100%
• Geographic Features 60%
• Training Data 40%
• Precise Location 40%
• Scanned Maps 40%
• Spatial Data 20%
• Railroad 20%
• Production Scale 20%
• Raster 20%
• Geographic Information 20%
• Approximate Location 20%
• Preprocessing Techniques 20%
• Extracting Information 20%
• Paper Model 20%
• Alignment Algorithm 20%
• Specific Features 20%
• Alignment Problem 20%
• Waterline 20%
• Digital Map 20%
• Efficient Search 20%
• Feature Representation 20%
• Feature Signatures 20%
• Date Production 20%
• Auxiliary Data 20%
• Convolutional Neural Network 20%
• Road Intersection 20%
• Matching Features 20%
• Spatial Reference System 20%
• Reinforcement Learning Framework 20%

Computer Science

• Reinforcement Learning 100%
• Training Data 66%
• Precise Location 66%
• Annotation 33%
• Preprocessing Step 33%
• Learning Framework 33%
• Extracting Information 33%
• Geographic Data 33%
• Convolutional Neural Network 33%
• Experimental Result 33%
• Reference System 33%
• Scale Production 33%