The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-2/W12-2026
https://doi.org/10.5194/isprs-archives-XLVIII-2-W12-2026-239-2026
https://doi.org/10.5194/isprs-archives-XLVIII-2-W12-2026-239-2026
12 Feb 2026
 | 12 Feb 2026

Exploring 4D Representation of Historic Gardens: A Semantic and Multi-Source Integration Framework Using GIS and Cesium Platforms

Fangming Li, Cristiana Achille, Raffaella Laviscio, and Francesco Fassi

Keywords: Historic Garden, 4D Modelling, Semantic Data Fusion, Point Cloud, GIS, Web-based Visualization

Abstract. Historic gardens are living forms of Cultural Heritage whose spatial identity is inseparable from continuous processes of growth, decay, and maintenance. Although recent advances in laser scanning, photogrammetry, and mobile mapping systems enable highly accurate three-dimensional documentation, most digital models remain limited to static representations. The temporal dimension, essential for understanding and managing garden heritage, is rarely integrated as an intrinsic component of spatial data.

This paper explores an experimental four-dimensional integration framework for historic gardens that combines point cloud data, semantic and multi-source data fusion based on GIS, and web-based 4D visualization. Rather than aiming at a complete temporal reconstruction, the approach investigates how a single-epoch 3D survey can act as a temporal anchor for integrating historical documentation and future-oriented scenarios within a unified spatial environment. The framework is tested on the historic garden of Villa Burba (Rho, Milan, Italy). Using open-source tools, point clouds from mobile laser scanning are processed using machine learning and semantically structured in a GIS environment, where time is modelled as a relational property describing transformation processes. The integrated model is visualized in the Cesium web platform, enabling interactive exploration of spatial-temporal relationships. The results demonstrate the feasibility of a scalable and interpretable 4D framework for living landscape heritage.

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