OPERATIONALIZING NEURO-SYMBOLIC GEOSPATIAL INTELLIGENCE

OPERATIONALIZING NEURO-SYMBOLIC GEOSPATIAL INTELLIGENCE | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 16 March 2026 V1 Latest version Share on OPERATIONALIZING NEURO-SYMBOLIC GEOSPATIAL INTELLIGENCE Author : Rishaank Gupta 0009-0004-8506-7972 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177368922.20310875/v1 95 views 71 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 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. Supplementary Material File (nsgis_operationalizing_paper_v2 (1).pdf) Download 385.54 KB Information & Authors Information Version history V1 Version 1 16 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords agbogbloshie deepproblog ground truth acquisition sentinel-2 tnfd leap Authors Affiliations Rishaank Gupta 0009-0004-8506-7972 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 95 views 71 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Rishaank Gupta. OPERATIONALIZING NEURO-SYMBOLIC GEOSPATIAL INTELLIGENCE. Authorea . 16 March 2026. DOI: https://doi.org/10.22541/au.177368922.20310875/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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