This paper presents a comparative evaluation of six cloud-based platforms including ArcGIS Online, Cintoo, Flai, Pointly, Cesium ion, and Atis.cloud for historic garden conservation, applied to two contrasting case studies: the complex, large-scale Naxos Archaeological Park and the compact, formal Villa Burba in Italy. Results show that Cesium ion and Cintoo performed strongly in point cloud visualization, with Cesium ion offering responsive large-scale rendering and Cintoo supporting high-precision geometry and version control. Flai demonstrated effective AI-driven element classification, particularly in heterogeneous landscapes, while Pointly required manual refinement and showed limited adaptability to organic features. ArcGIS Online excelled in stakeholder usability and layered documentation but lacked native 3D analytics. Collaborative functions were best addressed by Cintoo and Atis.cloud. Temporal functionality, such as phase comparison or seasonal tracking, remained limited, with no platform providing fully integrated support. The study highlights the fragmented nature of current solutions and argues for a modular, garden-oriented CDE model, integrating semantic intelligence, temporal awareness, and stakeholder-specific interfaces to support adaptive, sustainability-sensitive historic garden conservation in the cloud.
Historic Garden Conservation in the Cloud: A Comparative Exploration of Strengths, Weaknesses, and Possibilities
Fangming Li;Cristiana Achille;Francesco Fassi
2025-01-01
Abstract
This paper presents a comparative evaluation of six cloud-based platforms including ArcGIS Online, Cintoo, Flai, Pointly, Cesium ion, and Atis.cloud for historic garden conservation, applied to two contrasting case studies: the complex, large-scale Naxos Archaeological Park and the compact, formal Villa Burba in Italy. Results show that Cesium ion and Cintoo performed strongly in point cloud visualization, with Cesium ion offering responsive large-scale rendering and Cintoo supporting high-precision geometry and version control. Flai demonstrated effective AI-driven element classification, particularly in heterogeneous landscapes, while Pointly required manual refinement and showed limited adaptability to organic features. ArcGIS Online excelled in stakeholder usability and layered documentation but lacked native 3D analytics. Collaborative functions were best addressed by Cintoo and Atis.cloud. Temporal functionality, such as phase comparison or seasonal tracking, remained limited, with no platform providing fully integrated support. The study highlights the fragmented nature of current solutions and argues for a modular, garden-oriented CDE model, integrating semantic intelligence, temporal awareness, and stakeholder-specific interfaces to support adaptive, sustainability-sensitive historic garden conservation in the cloud.| File | Dimensione | Formato | |
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