The Evolution of Zoning Data Extraction: Navigating NLP and LLMs

Tackling the Intricacies of Zoning Data

In the realm of zoning data extraction, where precision is paramount, Deepblocks stands committed to navigating the complexities of legal terminology and intricate regulations. The challenge is to ensure accuracy in an area where even minor errors can lead to substantial legal and financial consequences.

The Journey from Manual Digitization to Automation

Our endeavor to digitize zoning data commenced in 2018, evolving from a labor-intensive manual process to a significantly automated workflow. This journey from digitizing 30 cities over three years to processing data from over 700 cities today, with 200 fully digitized, illustrates our commitment to innovation and efficiency.

The Role of Traditional NLP Models

Historically, our reliance on traditional NLP models has been grounded in their ability to accurately parse through zoning data via rule-based mechanisms. The challenge lies in generating enough structured data to ensure the models' reliability, with our team dedicating considerable efforts to this task.

Embracing the Era of Large Language Models

The advent of Large Language Models like GPT-4 introduces a new dimension of potential, coupled with challenges. While LLMs excel in adapting to varied data inputs, their propensity to produce unfounded outputs and take data selection shortcuts poses risks in contexts where accuracy is critical.

Balancing NLP and LLMs in Zoning Extraction

Despite initial reservations, Deepblocks is now pioneering the simultaneous training of both NLP and LLM models, leveraging our extensive repository of structured zoning data. This dual approach allows us to explore the strengths and limitations of each model type.

Beyond Data Extraction: Predicting Urban Growth

Our exploration extends to utilizing these models for forecasting urban growth patterns, aiming to minimize investment risks and envisage city development scenarios that foster economic growth. This forward-thinking strategy has the potential to transform urban planning and real estate development, offering insights and efficiencies previously beyond reach.

Author Olivia Ramos
Founder and CEO of Deepblocks, holds master's degrees in Architecture from Columbia University and Real Estate Development from the University of Miami. Her achievements before Deepblocks include designing Big Data navigation software for the Department of Defense's DARPA Innovation House and graduating from Singularity University's Global Solutions and Accelerator programs.