Regional adaptive DEM data generation method based on tilt model data

By combining deep learning models and CSF filtering algorithms, fully automated and high-precision DEM data generation based on skew model data is realized, fully automated and terrain adaptability problems are solved, and the efficiency and accuracy of DEM data generation are improved.

CN120472104AActive Publication Date: 2025-08-12WUDA GEOINFORMATICS CO LTD
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Patent Information

Application Number
CN202510957234.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-11
Publication Date
2025-08-12
Estimated Expiration
2045-07-11

AI Technical Summary

Technical Problem

The prior art cannot realize fully automated high-precision DEM data production based on skew model data, and traditional algorithms are difficult to meet the production requirements of different terrains in large-scale urban market scenarios.

Method used

The deep learning model and CSF filtering algorithm are used to preprocess the tilt model data, perform semantic segmentation and ground point extraction, and set adaptive parameters in combination with terrain features to realize full automatic generation of DEM data.

Benefits of technology

It improves the identification accuracy and generation efficiency of DEM data, reduces manual intervention, and meets the rapid production needs of large-scale high-precision DEM data.

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Abstract

According to the tilt model data-based area adaptive DEM data generation method provided by the invention, ground points are identified based on a deep learning model and a CSF filtering algorithm according to the tilt model data, full automation of ground point identification is realized, and the identification results of the two are fused and complemented, so that the identification precision is improved; besides, in the process of identifying the ground points by using the CSF filtering algorithm, the area range to be identified is partitioned, different CSF filtering parameters are set according to the terrain type of each block, and in the process of performing Kriging interpolation on the ground elevation points, different Kriging interpolation parameters are set according to the terrain features of each block. Adaptive parameters of different areas are set, and compared with fixed parameters adopted in different areas, the identification precision of ground points and the precision of generating terrain raster data are improved.
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Citation Information

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