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Project # 75
Map Verification
| Team Lead: | Tiarra Payne |
| Other Team Members: | Joel PamPam | Luke Siharath |
| Faculty Advisor(s): | Allison Sullivan |
| Department: | CSE |
| Project Type: | REU |
| Title: | Map Verification |
| Abstract: | Maps are not just navigational tools. Maps are cultural, scientific, and historical records. Historical geological and cadastral maps offer insights into long-term trends in land use, urban development, and environmental change. These maps are crucial to disciplines such as archaeology, human geography, and environmental science. Maps also help researchers and policymakers understand how human and natural landscapes have evolved over time. However, maps must be accurately represented in standardized digital form before any comparative analysis can be performed. In recent years, there has been a growing interest over time series and its use to digitize and turn maps into shapelets. , or shape expressions, help extract information from otherwise “noisy” time series data sets using parameters and constraints to compare ideal shape and observed shape. The famous “nearest neighbor” algorithm has been the most used and reliable algorithm to solve time series problems due to its simplicity. The downside of this algorithm is its time and space complexity. Time series shapeletes mitigate the negatives of the nearest neighbor algorithm and provide a way to solve time series problems at much lower time and space complexity. With a use of different time saving algorithms such as Early abandon Pruning, Entropy Pruning, Efficient Distance Computation, Candidate Pruning and Fast Shapelet Discovery, we plan to accurately verify data of historical map origin and explore integration with external sources of spatial ground truth in order to automate verifying that a digital vector-based GIS map accurately captures a given historical map. |
| Poster: | View Poster #75 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |