Generative Model Geospatial Workflow Automation

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Solution Overview

Problem

Designing complex geospatial workflows can be time-consuming and resource-intensive, requiring expertise from geospatial data scientists or software engineers, and these workflows often lack scalability beyond their original purpose.

Innovation Solution

The implementation of a method that automatically generates multimodal geospatial workflows using sequences of generative models (e.g., LLMs) to identify geospatial tasks, high-level actions, responsive datasets, and data manipulation instructions, enabling the assembly and execution of responsive data that fulfills geospatial tasks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If expert-designed complex geospatial workflows are used to accomplish complex geospatial tasks, then task completion accuracy is improved, but time consumption and resource requirements increase significantly

Engineering Contradiction:

Improvetask completion accuracyVSAvoidworkflow design time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-training generative models on extensive geospatial workflow data and dataset metadata before actual workflow generation. This pre-computation enables the model to quickly generate accurate workflows without requiring expert intervention during actual task execution, resolving the contradiction between accuracy and time consumption

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables self-service by allowing non-experts to generate complex geospatial workflows through natural language inputs. The generative model automatically understands task requirements and generates appropriate workflows without human expertise, eliminating the need for specialized knowledge while maintaining workflow quality

Inventive Principle:

Principle #25Self-service

2Reliability

If expert-designed complex geospatial workflows are used to accomplish complex geospatial tasks, then task completion accuracy is improved, but resource consumption increases

Engineering Contradiction:

Improvetask completion accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system segments the workflow generation process into distinct stages: natural language understanding, task decomposition, workflow generation, and validation. Each stage is handled by specialized components of the generative model, improving efficiency and reducing overall resource consumption while maintaining accuracy

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts model parameters such as temperature and top-k sampling based on task complexity and resource availability. This allows the model to optimize between accuracy and resource consumption by using more deterministic settings for simple tasks and more creative settings for complex tasks

Inventive Principle:

Principle #35Parameter changes

3Reliability

If complex geospatial workflows are designed for specific tasks, then task-specific performance is improved, but scalability to other tasks decreases

Engineering Contradiction:

Improvetask-specific performanceVSAvoidworkflow scalability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system implements universality by designing a single generative model that can handle multiple types of geospatial tasks through natural language inputs. The model learns general patterns from diverse training data and can adapt to new task types without requiring separate specialized workflows, enabling both task-specific performance and broad scalability

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If manual workflow design by experts is used, then workflow quality and accuracy are improved, but ease of operation decreases

Engineering Contradiction:

Improveworkflow qualityVSAvoidworkflow accessibility

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system introduces an intermediary generative model that translates between natural language inputs and complex workflow specifications. This intermediary layer allows users to interact with the system using simple language while the model handles the complexity of generating high-quality workflows, bridging the gap between user capability and workflow quality requirements

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250077566A1Generation and implementation of geospatial workflows

Publication Date: 2025.03.06 X DEVELOPMENT LLC

AI summary

Implementations are described herein for automatically generating multimodal geospatial workflows for accomplishing geospatial tasks. In various implementations, a natural language request may be processed based on generative model(s) such as LLM(s) to generate workflow output tokens that identify high-level actions for completing a geospatial task conveyed in the natural language request. First data indicative of the high-level actions may be processed using one or more of the generative models to generate dataset output tokens that identify responsive dataset(s) that likely contain data responsive to the geospatial task. Second data indicative of both the high-level actions and the responsive dataset(s) may be processed based on one or more of the generative models to generate data manipulation output tokens that identify data manipulation instructions for assembling data from the responsive dataset(s) into a response that fulfills the geospatial task.