## 1 Introduction  
Spatial data products (e.g., cadaster, road networks, digital elevation models, land cover, fire and drought incidence maps, etc.) are of great importance for environment modeling, decision making, climate change assessment, and so on. They, therefore, have great economic aggregated value [1]. However, spatial data are not exempt from errors, so spatial data sets represent a significant source of error in any analysis that uses them as input [2]. For this reason, spatial data quality has been a major research concern in previous decades [3]. Spatial data quality has several perspectives (e.g., accessibility, usability, integrity, interoperability, etc. ), and several quality elements (completeness, logical consistency, thematic accuracy, positional accuracy, temporal accuracy) have been defined by the international standard ISO 19113 [4]. Accuracy comprises trueness and precision, as stated by ISO 5725-1 [5]. Trueness is the absence of bias, and precision is a measure of dispersion. In thematic mapping, dealing with categories, the term accuracy means trueness, that is, the degree of \"correctness\" of a map classification. A classification may be considered accurate if it provides an unbiased representation of the reality (agrees with reality) or conforms to the \"truth\". Thematic accuracy is defined by ISO 19157 [6] as the accuracy of quantitative attributes and the correctness of non-quantitative attributesand of the classifications of features and their relationships. Classification correctness is defined by the same standard as the comparison of the classes assigned to features or their attributes to a universe of discourse (e.g., ground truth or reference data) [6]. Classification correctness is the main concern in any remote-sensed-derived product (e.g., land cover, fire and drought incidence maps, etc.) and, in general, for any kind of spatial data (e.g., vector data, such as cadastral parcels, road networks, topographic databases, etc.). The evaluation of thematic accuracy is a subject of interest, proof of this being the existence of numerous references, for instance [7] for ecosystem maps, [8] for land change maps, [9] for vegetation inventory, and [10] for land cover. The main components of a thematic accuracy assessment are [11] (i) the sampling design used to select the reference sample; (ii) the response design used to obtain the reference land-cover classification for each sampling unit; and (iii) the estimation and analysis procedures. This method has been widely adopted (e.g., [12; 13]), and also extended; for example, [8] proposed a method with eight steps as a good practice for assessing the accuracy of land change, in which some steps are coincident with those used in [11]. However, for a proper classification correctness assessment, a classification scheme is also needed. A classification scheme has two critical components [10]: (i) a set of labels and (ii) a set of rules for assigning labels so that a unique assignation of classes is possible. This is because classes (labels) are mutually exclusive (there are no overlaps) and totally exhaustive with respect to the thematic universe of discourse (there are no omissions or commissions of classes). From our point of view, the two previous aspects must be considered from a more general perspective of the production processes of spatial data. From this perspective, the first thing to consider is a specification of the product (e.g., in the sense of ISO 19131, [14]). This specification should contain the classification scheme but also a specification of the level of quality required for each category (e.g., at least 90% of the classification correctness for category A) and the grade of confusion allowed between categories (e.g., at most, 5% confusion between categories A and B). These quality grades must be in accordance with the processes' voice (capacity to give some quality grade) and the user's voice (quality needs for a specific use case).