An Exploration of Some Pitfalls of Thematic Map Assessment Using the New Map Tools Resource

15185 12 83616 3 234917 document 83616 remotesensing-10-00376.pdf application/pdf 5697638c02ca2bc58b92700e270e7031 MD5 627891 2018-04-03 07:54:34 https://pure.iiasa.ac.at/id/eprint/15185/1/remotesensing-10-00376.pdf 15185 1 1 application/pdf text en public cc_by_4

remotesensing-10-00376.pdf

published 83617 2 234921 document 83617 lightbox.jpg image/png 352da316ebb44136a3d2d367f12f8934 MD5 50063 2018-04-03 08:05:03 https://pure.iiasa.ac.at/id/eprint/15185/2/lightbox.jpg 15185 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/83616 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/83616 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/83616 archive 5 disk0/00/01/51/85 2018-04-03 07:57:38 2022-10-19 05:00:54 2018-04-03 07:57:38 article show Salk C. 8556 Fritz S. 8130 0000-0003-0420-8549 See L. 8571 0000-0002-2665-7065 Dresel C. 2105 McCallum I. 1731 0000-0002-5812-9988 An Exploration of Some Pitfalls of Thematic Map Assessment Using the New Map Tools Resource pub prog_esm thematic maps; map accuracy; map comparison; overall accuracy; Cohen’s Kappa; producers accuracy; users accuracy; average mutual information A variety of metrics are commonly employed by map producers and users to assess and compare thematic maps’ quality, but their use and interpretation is inconsistent. This problem is exacerbated by a shortage of tools to allow easy calculation and comparison of metrics from different maps or as a map’s legend is changed. In this paper, we introduce a new website and a collection of R functions to facilitate map assessment. We apply these tools to illustrate some pitfalls of error metrics and point out existing and newly developed solutions to them. Some of these problems have been previously noted, but all of them are under-appreciated and persist in published literature. We show that binary and categorical metrics, including information about true-negative classifications, are inflated for rare categories, and more robust alternatives should be chosen. Most metrics are useful to compare maps only if their legends are identical. We also demonstrate that combining land-cover classes has the often-neglected consequence of apparent improvement, particularly if the combined classes are easily confused (e.g., different forest types). However, we show that the average mutual information (AMI) of a map is relatively robust to combining classes, and reflects the information that is lost in this process; we also introduce a modified AMI metric that credits only correct classifications. Finally, we introduce a method of evaluating statistical differences in the information content of competing maps, and show that this method is an improvement over other methods in more common use. We end with a series of recommendations for the meaningful use of accuracy metrics by map users and producers 2018-03 published MDPI AG 10.3390/rs10030376 262 98 276 69 202 public Remote Sensing 10 3 376 TRUE 2072-4292 FALSE yes info:eu-repo/semantics/article