Accuracy Improvement for Italian Digital Cadastral Maps

The original implant map (OI) of Italian Cadaster was first made out of topographical measurements. These maps have never been used for public consultation, but they are the most accurate ones. The publicly accessible maps are named “visure maps” (VM) and were obtained from the original implant maps and consistently updated. However, these maps have suffered many deformations due to continuous use over time. These VM were then vectorialized at the beginning of the 2000s and have therefore undergone a further process of degradation. The goal of this work is to improve the accuracy of the Italian VM using the metric information. This can be achieved if we can precisely model the deformations, measurable by the recognition of boundaries and details still present on both maps and, after this, to apply this inverse model to the visure maps. The illustrated procedure is adaptable to other cartographies with similar problems.

The original implant map (OI) of Italian Cadaster was first made out of topographical measurements. These maps have never been used for public consultation, but they are the most accurate ones. The publicly accessible maps are named “visure maps” (VM) and were obtained from the original implant maps and consistently updated. However, these maps have suffered many deformations due to continuous use over time. These VM were then vectorialized at the beginning of the 2000s and have therefore undergone a further process of degradation. The goal of this work is to improve the accuracy of the Italian VM using the metric information. This can be achieved if we can precisely model the deformations, measurable by the recognition of boundaries and details still present on both maps and, after this, to apply this inverse model to the visure maps. The illustrated procedure is adaptable to other cartographies with similar problems.


ISSN 1121-9041

CiteScore:
2020: 3.8
CiteScore measures the average citations received per peer-reviewed document published in this title.
CiteScore values are based on citation counts in a range of four years (e.g. 2016-2019) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of these documents in these same four years (e.g. 2016 —19).
Source Normalized Impact per Paper (SNIP):
2019: 1.307
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR)
2019: o.657
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal's impact.
Journal Metrics: CiteScore: 1.0 , Source Normalized Impact per Paper (SNIP): 0.381 SCImago Journal Rank (SJR): 0.163

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