The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we propose a framework for historical city reconstruction. By integrating procedural modeling techniques and machine learning models within a Geographic Information System (GIS) framework, our pipeline allows for effective management of spatial data and the generation of detailed 3D models. We developed an open-source Python module that fills gaps in 2D GIS datasets and directly generates 3D models up to LOD 2.1 from GIS files. The use of the CityJSON format ensures interoperability and accommodates the specific needs of historical models. A practical case study using footprints of the Old City of Jerusalem between 1840 and 1940 demonstrates the creation, completion, and 3D representation of the dataset, highlighting the versatility and effectiveness of our approach. This research contributes to the accessibility and accuracy of historical city models, providing tools for the generation of informative 3D models. By incorporating machine learning models and maintaining the dynamic nature of the models, we ensure the possibility of supporting ongoing updates and refinement based on newly acquired data. Our procedural modeling methodology offers a streamlined and open-source solution for historical city reconstruction, eliminating the need for additional software and increasing the usability and practicality of the process.

The advancement of computational tools for cartometric analysis has opened new avenues for the identification and understanding of stemmatic relationships between historical maps through the analysis of their planimetric distortions. The 19th-century Western cartographic depiction of Jerusalem serves as an ideal case study in this context. The challenges of conducting comprehensive onsite surveys—due to limited time and local knowledge—combined with the fascination surrounding the area’s representation, resulted in a proliferation of maps marked by frequent errors, distortions, and extensive copying. How can planimetric similarities and differences between maps be measured, and what insights can be derived from these comparisons? This paper introduces a methodology aimed at detecting and segmenting regions of local planimetric similarity across maps, corresponding to the portions that were either copied between them or derived from a common source. To detect these areas, the ground control points from the georeferencing process are employed to deform a common lattice grid for each map. These grids, triangulated to maintain shape rigidity, can be partitioned under conditions of geometric similarity, allowing for the segmentation and clustering of locally similar regions that represent shared areas between the maps. By integrating this segmentation with a filter on the intensity of distortion, the areas of the grid that are almost non-deformed, and thus not relevant for the study, can be excluded. To showcase the support this methodology offers for close reading, it is applied to the maps in the dataset depicting the Russian Compound. The methodology serves as a tool to assist in constructing the genealogy of the area’s representation and uncovering new historical insights. A larger dataset of 50 maps from the 19th century is then used to identify all the local predecessors of a given map, showcasing another application of the methodology, particularly when working with extensive collections of maps. These findings highlight the potential of computational cartometry to uncover hidden layers of cartographic knowledge and to advance the digital genealogy of map collections.

This study explores the 4D reconstruction of part of the monumental heritage of St. Mark’s Square in Venice, contextualizing the modeling of its evolution in the broader field of digital historiography. The presented methodology is based on comparing the plausibility of proposed 3D models with current point cloud and historiographical hypotheses that have been compared to the problem of planimetric and volumetric simulation of buildings. Methods include integrating 3D modeling with temporal data to simulate architectural and urban evolution, providing a dynamic and problematic visualization of historical changes and identifying discordant historiographical assumptions. Aligning 3D model reconstructions with the current point cloud provides a better understanding of the plausibility of reconstructions proposed by historiography while identifying potential errors and inconsistencies in some historical documentary sources on which reconstructive approaches are often based. For the reconstructions proposed here, the paper provides an assessment of the level of uncertainty based on some recent notions related to quantifying average uncertainty. Although preliminary, these results provide a basis for testing hypotheses about Venice’s architectural past. Moreover, they open up a new, more generic perspective for the analysis of architectural–urban transformations, based on the notion of planimetric and volumetric constraints, and their comparison with current point cloud records. Using this methodology, we are able to make some solid points with respect to reconstructive hypotheses of the past and indicate which historiographical theories are more plausible than others.