Geospatial Platform Habitat Classification via Machine Learning
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Solution Overview
Problem
Conventional methods for land assessment are inefficient and resource-intensive, particularly when assessing large or remote parcels of land, as they often require manual surveys and frequent re-assessments to account for environmental changes.
Innovation Solution
A geospatial platform utilizing machine learning techniques to analyze, plan, and monitor land by processing data points such as aerial images, satellite images, and digital surface model images, providing accurate and efficient assessments of land use, land cover, and habitat classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual surveys are used for land assessment, then detailed land information can be obtained, but the process becomes resource-intensive and inefficient
Solution Approach 1:
The patent replaces manual mechanical surveying with automated image processing systems. Machine learning models analyze satellite and aerial images to automatically classify land cover, detect changes, and generate assessments, eliminating the need for physical field surveys while maintaining or improving accuracy and significantly increasing efficiency
Solution Approach 2:
The system creates digital copies of land surfaces through satellite and aerial imagery. These image copies are then processed by machine learning algorithms to extract land assessment information, allowing multiple assessments to be performed on the same digital copy without requiring repeated physical surveys
2Reliability
If frequent re-assessments are conducted to account for environmental changes, then up-to-date land information is available, but the resource consumption increases
Solution Approach 1:
The system enables continuous automated monitoring by processing new satellite and aerial images as they become available. Machine learning models continuously detect land cover changes and update assessments without interruption, ensuring information remains current while using automated processes that consume fewer resources than repeated manual surveys
Solution Approach 2:
The machine learning system performs self-service by automatically detecting changes in land cover from new images and generating updated assessments without human intervention. The system monitors itself and continuously improves its detection capabilities, reducing the need for additional resources while maintaining reliable, up-to-date information
Data Source
AI summary
Example systems, methods, and non-transitory computer readable media are directed to determining a geographic location to be assessed; obtaining information associated with the geographic location, the information including at least one data point of the geographic location; classifying one or more habitats within the geographic location based on at least one machine learning model that processes the at least one data point of the geographic location; determining at least one respective metric for the one or more classified habitats based at least in part on the at least one data point of the geographic location; and providing an interface that includes at least a map of the geographic location and the at least one respective metric for the one or more classified habitats, the one or more classified habitats are visually segmented in the map by habitat type.