Building Extraction Using User Behavior Data Fusion
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Solution Overview
Problem
Existing building extraction methods in image processing technology face challenges due to environmental factors, leading to poor accuracy and inability to meet usage requirements in urban planning, construction, and post-disaster reconstruction.
Innovation Solution
A method and apparatus that incorporate user behavior-associated data with remote sensing image data to generate new channel data, which is then used for accurate building extraction, utilizing techniques such as POI data, location data, and search data to enhance the extraction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only remote sensing image data is used for building extraction, then the processing is simple, but the extraction accuracy is poor due to environmental factors
Solution Approach 1:
The patent combines remote sensing image data with user behavior associated data (POI data, location data, search data) to create a multi-source data fusion approach. This merging of different data types compensates for the limitations of remote sensing images alone, particularly in distinguishing buildings from environmental factors like trees and shadows, thereby improving extraction accuracy while managing complexity through systematic data integration
Solution Approach 2:
The patent introduces 'new channel data' as an intermediary that bridges remote sensing image data and building extraction results. This new channel data, generated from user behavior data through mapping to image pixels, serves as a mediator that provides additional semantic information about building functionality and usage, enhancing the extraction process without directly modifying the original remote sensing images
2Measurement precision
If remote sensing image quality is poor due to environmental factors, then data collection is simpler, but building extraction accuracy deteriorates
Solution Approach 1:
The patent merges remote sensing image data with user behavior associated data to compensate for information loss in poor-quality images. By combining visual data from remote sensing with semantic data from user behavior sources, the system recovers information that would be lost due to environmental degradation of image quality
Solution Approach 2:
The 'new channel data' acts as an intermediary that recovers lost information by providing alternative data pathways. When remote sensing images are degraded by environmental factors, the new channel data derived from user behavior data fills in the information gaps, maintaining extraction accuracy despite image quality issues
Data Source
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AI summary
A method, an apparatus, a device for building extraction and a storage medium are provided in the present disclosure, which relates to a field of image processing technology, and the specific implementation plan is: obtaining remote sensing image data and user behavior associated data of a target area; wherein the user behavior associated data and the remote sensing image data have a spatiotemporal correlation; generating new channel data according to the user behavior associated data and the remote sensing image data; and extracting buildings in the target area according to the remote sensing image data and the new channel data. The accuracy of the building exaction is provided in the embodiments of the present disclosure.