ReForGenAI: A Generative AI Framework for Automated Micro-Site Reforestation Planning

Reforestation is the primary and crucial solution towards the unapologetic destruction made by humans towards the natural ecological structure. Current methods are time-consuming and lack accurate spatial analysis. This paper introduces ReForGenAI, a novel system with Generative AI principles to automate the preliminary planning process and provide an actionable plan in hand. ReForGenAI accepts the longitude and latitude of a location as inputs. It leverages Google Earth Engine (GEE) to acquire the environmental data such as temperature, elevation, soil ph, soil type and climate type. A neural network trained on 480 tree species suggests the top suitable trees with a suitability score. Parallelly, it acquires the satellite and roadmap maps through Google Maps Static API for visual site context. Using computer vision techniques, the plantable area mask is identified, which excludes roads, buildings, waterbodies, and already existing trees. Plantable points are generated in the visual map using specific algorithms. A structured output provides a list of trees suitable for the location and a map that covers a radius of 175 meters with plantable tree points. This approach offers a quick, actionable plan using generative AI to address complex ecological planning tasks.