GANCIU—Geospatial Analysis with Neural Classification and Image Understanding

Abstract

Accurate and up-to-date knowledge of land use and land cover represents one of the central challenges in spatial planning and landscape sciences. In this context, the present work introduces GANCIU (Geospatial Analysis with Neural Classification and Image Understanding), an original hybrid pipeline for the automatic extraction of man-made infrastructure from high-resolution satellite imagery. The primary methodological contribution lies in the sequential integration of four technologically heterogeneous components: a per-pixel Random Forest classifier, a guided image modulation step, edge detection via the Mumford-Shah variational functional solved through the Ambrosio-Tortorelli approximation, and final object delineation via the Segment Anything Model (SAM). Each component does not operate independently but conditions and informs the next: the RF probability map guides the modulation, which in turn directs the sensitivity of the variational step exclusively towards regions of interest; the AT edges provide spatial prompts to SAM, whose masks are finally filtered by the RF probability in an adaptive manner through a Gaussian mixture model. This progressive conditioning scheme constitutes the architectural core of GANCIU and distinguishes it from approaches that combine classification and segmentation in parallel or in purely sequential fashion without informational feedback between steps. The pipeline was applied to fourteen study areas located in Sardinia, a region characterized by a complex rural matrix and marked chromatic and geometric heterogeneity of surface types. The results reveal three distinct behavioral regimes as a function of scene difficulty: in scenes with clear radiometric contrast between infrastructure and agricultural background, the pipeline discriminates effectively at the first step; in scenes of intermediate difficulty, the AT+SAM+filter combination demonstrates a significant recovery capacity with respect to the uncertainty of the initial RF map; in scenes with dense agricultural texture, the current limitations of the training model emerge, with false positives on intensive crops representing the primary target for future development. A quantitative comparison with established methods, based on standard metrics including Precision, Recall, F1-score and Intersection over Union, is deferred to a subsequent evaluation that will extend the analysis to different geographical and sensor contexts.