Abstract
Abstract. The availability of thematic maps has significantly increased over the last few years. Validation of these maps is a key factor in assessing their suitability for different applications. The evaluation of the accuracy of classified data is carried out through a comparison with a reference dataset and the generation of a confusion matrix from which many quality indexes can be derived. In this work, an ad hoc free and open source Python tool was implemented to automatically compute all the matrix confusion-derived accuracy indexes proposed by literature. The tool was integrated into GRASS GIS environment and successfully applied to evaluate the quality of three high-resolution global datasets (GlobeLand30, Global Urban Footprint, Global Human Settlement Layer Built-Up Grid) in the Lombardy Region area (Italy). In addition to the most commonly used accuracy measures, e.g. overall accuracy and Kappa, the tool allowed to compute and investigate less known indexes such as the Ground Truth and the Classification Success Index. The promising tool will be further extended with spatial autocorrelation analysis functions and made available to researcher and user community.
Highlights
Thanks to the continuous advance in remote sensing and mapping technologies, the availability of land use/land cover (LULC) maps has considerably grown over the last few years
Derived from a comparison between a classified dataset and a reference one, this matrix represents the starting point from which to extract many useful indexes able to describe agreements and disagreements between the two considered datasets. These indexes span from the most commonly used overall accuracy, user’s accuracy and producer’s accuracy (Story and Congalton, 1986), to the more complex Individual Classification Success (Koukoulas and Blackburn, 2001) and Ground Truth (Türk, 1979) indexes. Since it enables the comparison of two sources of spatial information, the error matrix computation represents a key tool for Geographical Information System (GIS) software, which are the most used tool for practical processing and analysis of spatial data
As explained in the previous section, the accuracy assessment of GL30 has been performed taking into account two different classification methods based on 11 classes and 5 classes, respectively
Summary
Thanks to the continuous advance in remote sensing and mapping technologies, the availability of land use/land cover (LULC) maps has considerably grown over the last few years. Derived from a comparison between a classified dataset and a reference one, this matrix represents the starting point from which to extract many useful indexes able to describe agreements and disagreements between the two considered datasets These indexes span from the most commonly used overall accuracy, user’s accuracy and producer’s accuracy (Story and Congalton, 1986), to the more complex Individual Classification Success (Koukoulas and Blackburn, 2001) and Ground Truth (Türk, 1979) indexes. Since it enables the comparison of two sources of spatial information, the error matrix computation represents a key tool for Geographical Information System (GIS) software, which are the most used tool for practical processing and analysis of spatial data.
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