Nature-related financial risks are increasingly central to global reporting frameworks, yet most small and informal businesses in developing countries remain invisible to existing data systems. Because these enterprises lack formal records and defined map locations, their environmental impact cannot be assessed or incorporated into large-scale risk models. Current AI and satellite-based methods are insufficient for this task: purely connectionist approaches require large labeled datasets that do not exist for informal industrial settings, while language models lack the structured reasoning required for financial compliance. This paper proposes a neuro-symbolic framework in which satellite imagery is combined with a logic-based industrial knowledge graph to infer the activity types and environmental risk profiles of informal enterprises in unmapped urban areas. The approach leverages symbolic rules to compensate for data scarcity while producing transparent, auditable reasoning traces suitable for TNFD LEAP compliance. A secondary application is identified: the same spatial analysis that locates industrial clusters for financial disclosure purposes simultaneously maps environmental exposure zones relevant to maternal health surveillance in climate-vulnerable cities. Rather than presenting experimental results, this work defines the research gap, proposes an operational framework architecture, and outlines a three-phase research agenda

Nature-related financial risks are becoming an important part of global reporting, but most small and informal businesses in developing countries remain almost invisible in existing data systems. Because these businesses do not have proper records or clear locations on maps, it becomes difficult to understand their environmental impact or include them in large-scale assessments. Current AI models and satellite-based methods also struggle because they either need too much labeled data or cannot reason clearly about what activities are happening on the ground. This paper suggests a new direction using a neuro-symbolic approach, where satellite images are combined with logic-based rules to infer what kinds of informal industrial activities might be present in unmapped areas. The goal is to create a practical system that can identify environmental risks, support nature-related disclosures, and also improve awareness of health risks in areas affected by heat and pollution. Instead of presenting experimental results, this work outlines the research gap and proposes a framework that could guide future studies on informal economies, environmental monitoring, and public health.

The original preprint by the same author (Gupta, 2025) identified a genuine and underserved research frontier: the application of neuro-symbolic artificial intelligence to satellite-based inference of informal industrial activity for the purpose of generating TNFD-compliant naturerelated financial disclosures in the Global South. That work diagnosed the problem space accurately but stopped short of the operational architecture its title implied. This paper delivers what the preprint promised. We present a fully specified Neuro-Symbolic Geospatial Inference System (NSGIS) comprising: (1) a multi-sensor fusion pipeline combining Sentinel-2 optical, ECOSTRESS thermal infrared, and Sentinel-1 SAR imagery to resolve the thermal detection gap; (2) a semantically structured Industrial Activity Knowledge Graph (IAKG) encoding 47 informal industry archetypes across six activity domains, designed for regional adaptation; (3) a differentiable logic layer implemented in DeepProbLog that propagates uncertainty from neural feature extraction through symbolic rule evaluation to produce calibrated posterior activity probabilities; (4) a confidence-threshold protocol that translates probabilistic outputs into TNFD LEAP-compatible reporting tiers, resolving the tension between stochastic ML inference and deterministic compliance requirements; (5) a participatory Ground Truth Acquisition Framework (GTAF) using community mapping, NGO partnerships, and stratified field validation to break the label-scarcity circularity; and (6) a Data Governance and Ethics Framework (DGEF) addressing the political economy of informal-sector surveillance, community consent, data access controls, and adverse-classification redress mechanisms. The maternal health co-benefit hypothesis of the original preprint is re-framed here as a properly scoped secondary research question with its own validation methodology, linking industrial cluster identification to maternal health vulnerability indices via spatial correlation analysis in Greater Accra and Dhaka. Together, these elements constitute not a framework proposal but an implementable system architecture ready for prototype development.