Reviewing Emerging Technologies in GIS-Based Cartography: A PRISMA-Informed Structured Review and Weak Indicator Assessment Framework

Geospatial cartography has diversified rapidly across artificial intelligence-based land use classification, environmental remote sensing, unmanned aerial vehicles (UAVs), Internet of Things (IoT) sensor networks, synthetic aperture radar (SAR), and volunteered/collaborative mapping, yet no cross-technology review has jointly assessed the comparative robustness of their performance indicators or their documented contribution to sustainable development. Using a structured two-stream protocol informed by the PRISMA 2020 reporting principles, interpreted as archived-record reconciliation rather than a complete prospective identification–screening flow, this review analyzes 122 references (1996–2026) across eight corpus organization search strata used as historical retrieval/scoring buckets rather than as equivalent technology categories, complemented by 52 empirical extraction records (coding units that are not assumed to represent 52 independent primary studies); four records overlap bibliographically, yielding 170 archived registry records (166 distinct individual bibliographic publications after post-QC identity reconciliation). Under the anchored heuristic composite framework, under the baseline weighting, 20 of 30 indicators (67%) fall below the selected S<0.40 decision threshold; the ranking is treated as descriptive because the four criteria are strongly correlated. Across alternative weighting and leave-one-dimension-out sensitivity scenarios, the Spearman rank correlations with the baseline were ρ=0.993–1.000; 18–21 of 30 indicators remained below S<0.40, with three near-threshold indicators changing classification. The baseline 20/30 count is conditional on the single-evaluator scoring rubric and study-specific cut-point; it is not an externally calibrated estimate of field-wide indicator weakness. Eighteen indicators remained below the cut-point in all eight tested scenarios. A technology family by analytical domain matrix identifies two provisional retained-evidence gaps in the full retained corpus. These are corpus-specific unrepresented cells rather than evidence of worldwide absence; fourteen further cells have only one retained source. A peer-reviewed/scholarly-only sensitivity check preserves the principal technical evidence pattern but increases empty matrix cells from two to five, indicating that gap identification is more source quality-sensitive than the main technical findings. For RQ3, no longer computes an arithmetic technical–sustainability “gap”. Within S1–S7, the mean heuristic composite evidence scores range from 0.31 to 0.63; separately, the eight sustainability contribution indicators (S23–S30) range from 0.18 to 0.33, and all remain below the baseline S<0.40 weak-evidence screening cut-point. Although both profiles use the same 0–1 F/R/M/C composite rubric, they represent non-equivalent constructs and unequal evidence pools; no cross-profile subtraction, ratio, association test, or decoupling inference is provided. The peer-reviewed 13% remote sensing coverage of global socio-ecological SDG indicators is retained only as a contextual benchmark from the prior literature. Because screening, scoring, and gap coding were performed by a single evaluator and the search was targeted rather than database-exhaustive, this review should be interpreted as a structured evidence mapping rather than as an exhaustive systematic review. Independent replication of the scoring grid is recommended before the policy-relevant use of the numerical thresholds. The review was not registered; no formal effect size meta-analysis or certainty of evidence grading was undertaken because the included technological evidence and reported metrics were heterogeneous.