VegRecoverAI: A deep learning-based system for automated vegetation recovery assessment and prediction with demonstration case study on gas pipeline construction

Abstract

Vegetation restoration is crucial for environmental conservation and maintaining ecosystem services. Traditional methods, such as manual inspections and expert photo interpretation, have been widely used to assess vegetation recovery but are labor-intensive, time-consuming, and prone to human bias. In contrast, modern Artificial Intelligence (AI) based methods use satellite imagery for efficient vegetation analysis, enabling large-scale monitoring with minimal human effort. This paper introduces VegRecoverAI, a comprehensive system that leverages multisource satellite data from Landsat, Sentinel-2, and PlanetScope. VegRecoverAI autonomously detects both subtle and significant vegetation changes, providing a reliable alternative to manual assessment. The system extracts NDVI time series data, detects vegetation change and uses an ensemble of forecasting models to predict future vegetation restoration. The system is demonstrated as a case study following gas pipeline construction in Italy. The results indicate that VegRecoverAI is automated and a scalable solution complementary to traditional techniques to support proactive environmental management.