A WEBGIS TO SUPPORT GPR 3D DATA ACQUISITION: A FIRST STEP FOR THE INTEGRATION OFUNDERGROUND UTILITY NETWORKS IN 3D CITY MODELS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. For the planning and sustainable development of large cities, it is critical to accurately locate and map, in 3D, existing underground utility networks (UUN) such as pipelines, cables, ducts, and channels. An emerging non-invasive instrument for collecting underground data such as UUN is the ground-penetrating radar (GPR). Although its capabilities, handling GPR and extracting relevant information from its data are not trivial tasks. For instance, both GPR and its complimentary software stack provide very few capabilities to co-visualize GPR collected data and other sources of spatial data such as orthophotography, DEM or road maps. Furthermore, the GPR interface lacks functionalities for adding annotation, editing geometric objects or querying attributes. A new approach to support GPR survey is proposed in this paper. This approach is based on the integration of multiple sources of geospatial datasets and the use of a Web-GIS system and relevant functionalities adapted to interoperable GPR data acquisition. The Web-GIS is developed as an improved module in an existing platform called GVX. The GVX-GPR module provides an interactive visualization of multiple layers of structured spatial data, including GPR profiles. This module offers new features when compared to traditional GPR surveys such as geo-annotated points of interest for identifying spatial clues in the GPR profiles, integration of city contextual data, high definition drone and satellite pictures, as-built, and more. The paper explains the engineering approach used to design and develop the Web GIS and tests for this survey approach, mapping and recording UUN as part of 3D city model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it