Utilizing AI and Spatial Computing to Map Deforestation and Inform Regulatory Engagements
TL;DR
An integrative review of the peer-reviewed literature on artificial intelligence and spatial computing for deforestation mapping and of the governance research that examines how such systems inform regulatory engagement contributes a five-stage conceptual framework that connects detection research to governance research.
Abstract
Tropical deforestation persists despite two decades of rapid progress in satellite monitoring, and regulators increasingly depend on algorithmic mapping systems they did not build and cannot fully audit. This article presents an integrative review of the peer-reviewed literature on artificial intelligence (AI) and spatial computing for deforestation mapping, and of the governance research that examines how such systems inform regulatory engagement. Drawing on verified studies spanning remote-sensing science, geospatial platform engineering, and environmental governance, the review pursues three questions: which AI methods and spatial-computing infrastructures underpin operational deforestation mapping; through which mechanisms mapped information reaches regulatory processes; and under which conditions algorithmic monitoring changes regulatory outcomes rather than merely documenting loss. The synthesis shows that deep learning and cloud-based geospatial platforms have made near-real-time, high-resolution forest disturbance detection operational at continental scale, while the regulatory effect of these capabilities is conditional on institutional capacity, legal definitions that rarely match what satellites measure, and the distribution of compliance burdens. The article contributes a five-stage conceptual framework, from observation infrastructure through AI inference and platform dissemination to regulatory engagement mechanisms and accountability conditions, that connects detection research to governance research. The framework clarifies why technically accurate systems can fail as regulatory instruments and identifies design choices that improve the fit between alerts and enforcement, due diligence, and community monitoring. Implications are drawn for regulators implementing due-diligence legislation, for national monitoring programmes, and for the research agenda on accountable algorithmic environmental monitoring.