Machine Operation Zones Using Soft Boundaries From Geospatial Features
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Solution Overview
Problem
Current methods for creating boundaries and subzones for machine operation are burdensome due to the challenges of working with tabular geospatial data, including redundancy, large dataset sizes, time-consuming data transfer, difficult visualization, and high storage and processing costs.
Innovation Solution
The system compresses tabular geospatial data to analyze a plot of land and generate geospatial features, which are then used to create subzones for machine operations. This approach includes generating soft boundaries by combining existing boundaries with weighted confidences, allowing for probabilistic boundaries and efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tabular geospatial data is used to create boundaries and subzones, then machine operation zones can be determined, but data redundancy and large dataset sizes increase storage and processing costs
Solution Approach 1:
The system extracts only the essential geospatial features needed for boundary definition from the complete tabular dataset. By identifying and extracting key boundary-defining points and characteristics, the system creates subzones using a reduced feature set that maintains operational accuracy while eliminating redundant data elements.
Solution Approach 2:
Instead of working with the complete detailed dataset to define boundaries, the system inverts the approach by first identifying exclusion zones and critical boundary features, then using these inverted boundary definitions to selectively process only the necessary data points. This reversal reduces the overall data processing burden while maintaining zone determination accuracy.
2Measurement precision
If complete geospatial data is processed, then accurate subzones are created, but data transfer and processing time increase
Solution Approach 1:
The system segments the geospatial data processing into distinct phases: first identifying exclusion zones, then defining subzone boundaries, and finally processing operational data within those boundaries. This segmentation allows each processing stage to work with progressively reduced datasets, maintaining boundary precision while minimizing total processing time through staged data reduction.
3Loss of information
If detailed geospatial data is visualized, then subzone characteristics are clearly understood, but visualization difficulty increases
Solution Approach 1:
The system applies local quality by visualizing different levels of detail in different regions of the geospatial data. Critical boundary areas and exclusion zones are displayed with high detail and precision, while interior subzone areas use simplified representations. This localized differentiation maintains complete subzone information while improving overall visualization ease through adaptive detail levels.
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed to determine a boundary for a work plan. An example apparatus includes circuitry to instantiate the machine-readable instructions to: detect a first attribute and a second attribute based on a characteristic of a plot of land, the first attribute corresponds to an uncertain feature in the plot of land; determine a first machine operation based on the first attribute and a second machine operation based on the second attribute; determine a first boundary around a first region, the first region including a first area of the plot of land including the first attribute; determine a second boundary around a second region, the second region including a second area of the plot of land including the second attribute; and determine a work plan based on the first boundary, the second boundary, and a relevance between the first attribute and the second attribute.