The Need: Urban historians, city planners, and preservationists require efficient tools to extract building-level information from historic Sanborn Fire Insurance maps, essential for understanding urban development over time. However, manual extraction is time-consuming and impractical due to the sheer volume of maps and lack of suitable computational methods.

The Technology: Our solution presents a scalable and semi-automated framework specifically designed to extract urban information from Sanborn maps and reconstruct historic urban neighborhoods cartographically. This framework operates in three key steps: georeferencing the maps to a geographic coordinate system, employing machine learning techniques to automatically identify building footprints and properties, and creating 3D visualizations of the historic neighborhoods.

Commercial Applications:

• Urban historical research and analysis
• City planning and redevelopment projects
• Preservation efforts and heritage site management

Benefits/Advantages:

• Efficiency: Significantly reduces time and resources required for manual extraction and analysis of building-level information from Sanborn maps.
• Accuracy: Utilizes machine learning methods for precise identification of building footprints and properties, ensuring high accuracy of extracted data.
• Visualization: Facilitates the creation of immersive 3D visualizations, aiding in the interpretation and communication of historical urban landscapes.