Geospatial Parcel Scoring With Language Models for Faster Site Prospecting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional site prospecting processes are time-consuming and expensive, often requiring manual analysis and rule-based workflows to identify suitable sites for development, which can miss good sites due to inflexible rules and incur significant opportunity costs.
Innovation Solution
A computing system leveraging machine learning models and language models to analyze geospatial data, generate capacity and feasibility scores, and provide map-based visualizations for rapid site selection and evaluation, allowing for iterative user input and real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and rule-based workflows are used for site prospecting, then thorough evaluation of geographic areas can be performed, but the process becomes very time-consuming and expensive
Solution Approach 1:
The system segments the large geographical area into smaller land parcels, enabling parallel processing and automated evaluation of multiple sites simultaneously. This segmentation allows the machine learning model to efficiently evaluate numerous parcels without the time constraints of manual analysis.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated machine learning models and language models. These computational systems process geospatial data, generate capacity scores, and evaluate land parcels automatically, eliminating the time-consuming nature of human-driven workflows while maintaining or improving evaluation accuracy.
2Reliability
If rule-based workflows are used to narrow down geographic areas, then consistent evaluation criteria can be applied, but the system fails to identify good sites due to pre-defined inflexible rules
Solution Approach 1:
The system transitions from fixed rule-based parameters to dynamic machine learning model parameters. The models learn optimal evaluation criteria from training data, allowing them to adapt to different site characteristics and identify suitable locations that rigid rules would miss. The capacity scores and evaluations are generated based on learned patterns rather than predetermined thresholds.
Solution Approach 2:
The evaluation system becomes dynamic through the use of machine learning models that can adjust their assessments based on the specific characteristics of each land parcel. Unlike static rule-based systems, the models can weigh different factors differently for different sites, providing both consistency through standardized modeling and flexibility through adaptive evaluation.
3Measurement precision
If extensive manual analysis is performed on large geographical areas, then comprehensive site evaluation can be achieved, but the process requires numerous site visits incurring opportunity costs
Solution Approach 1:
The system creates digital copies and representations of land parcels using geospatial data, allowing virtual evaluation of sites without physical visits. The machine learning models analyze these digital representations to generate capacity scores and determine site suitability, eliminating the need for numerous in-person site visits while maintaining comprehensive evaluation capabilities.
Solution Approach 2:
The machine learning-based system performs multiple evaluation functions simultaneously - analyzing geospatial data, generating capacity scores, evaluating land parcel suitability, and prioritizing sites for further investigation. This multi-functional approach replaces the sequential manual processes of data collection, analysis, and site visit planning, significantly improving productivity.
4Productivity
If automated computerized systems are used to record and visualize geospatial data, then data processing speed increases, but the systems lack the capability to accurately identify suitable sites
Solution Approach 1:
The patent introduces language models as intermediaries between the automated data processing system and the site identification task. These models generate natural language descriptions of land parcels based on geospatial data and capacity scores, enabling the automated system to communicate site suitability in an interpretable format that maintains both processing speed and identification accuracy.
Solution Approach 2:
The system combines multiple components - machine learning models for capacity scoring, language models for description generation, and visualization components - into a composite automated evaluation system. This composite approach leverages the strengths of each component to achieve both high processing speed and accurate site identification, overcoming the limitations of simpler automated systems.
Data Source
AI summary
Methods and systems for site prospecting includes the operations of: receiving a site request indicating a required use for a site; generating a plurality of capacity scores corresponding to a plurality of land parcels using a first machine learning model; filtering the plurality of land parcels into a subset of land parcels based on the plurality of capacity scores; and for at least one land parcel in the subset of land parcels: generating a parcel potential description using a first language model based at least in part on geographic information associated with the at least one land parcel; generating a parcel potential score using a second machine learning model based at least in part on the parcel potential description; and presenting the parcel potential description and the parcel potential score.