GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

Abstract

ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2—Validation—accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.

Semantic Earth Observation Data Cubes

“…It is sensor-agnostic in that data calibrated to at least top-of-atmosphere reflectance by optical sensors can be used to generate semantic enrichment comparable between sensors (e.g., Sentinel-2, Landsat). SIAM™'s output has been independently validated at a continental scale by [44].…”

“…Semantic EO data cubes are most powerful when combined with semantically rich yet generic interpretations because semantics differ between domains, applications, users and the targeted purpose of analysis. Closing the semantic gap when generating information from EO data is very difficult and goes beyond the focus of this paper (refer to [44] as a starting point on this topic), but even the simplest semantic enrichment better positions EO data cubes for analysis than ones containing no semantics at all. Any data-derived semantic information can be used as the basis of a semantic EO data cube, but generic semantic enrichment is highly extendible.…”

AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud–Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery—Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context

“…Based on scientific literature [13][14][15]67,82,83,124], a CV  EO-IU system is defined in operating mode if and only if it scores "high" in every index of a minimally dependent and maximally informative (mDMI) set of EO outcome and process (OP) quantitative quality indicators (Q 2 Is), to be Figure 5. Artificial intelligence (AI) for Data and Information Access Services (AI4DIAS), synonym for semantics-enabled DIAS or closed-loop EO image understanding (EO-IU) for semantic querying (EO-IU4SQ) system architecture.…”

“…Unlike the non-standard ESA EO Level 2 SCM legend adopted by the Sentinel 2 imaging sensorspecific (atmospheric, adjacency and topographic) Correction Prototype Processor (Sen2Cor), developed by ESA and distributed free-of-cost to be run on the user side [11,12], see Table 1, an alternative ESA EO Level 2 SCM legend, proposed in [13][14][15] and shown in Table 2, consists of an "augmented" fully-nested 3-level 9-class Dichotomous Phase (DP) taxonomy of land cover (LC) classes in the 4D geospatial-temporal scene-domain. It comprises: (i) a standard 3-level 8-class DP taxonomy of the Food and Agriculture Organization of the United Nations (FAO) Land Cover Classification System (LCCS) [16], see Figure 1, augmented with (ii) a thematic layer explicitly identified as class "others", synonym for class "unknown" or "rest of the world", which includes quality layers cloud and cloud-shadow.…”

“…Table 2. General-purpose, user-and application-independent ESA Level 2 SCM legend proposed in [13][14][15], consistent with the standard 3-level 8-class Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy [16]. The "augmented" standard taxonomy consists of the standard 3-level 8-class FAO LCCS-DP taxonomy (identified as classes A11 to B48) + quality layers Cloud and Cloud-shadow + class Others (Unknown) = 8 land cover (LC) classes + 2 LC classes (Cloud-shadow, Others) + 1 non-LC class (Cloud).…”