A Framework for the Integration of Remote Sensing Systems for 3D Urban Mapping
Why this work is in the frame
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Bibliographic record
Abstract
As one of the fields of civil and architecture engineering, 3D urban mapping plays avital role in various applications such as urban planning, surveillance, virtual reality,virtual tourism, and military training. In this regard, the interest in 3D urban mappingtechnologies is rapidly increasing within the surveying and photogrammetriccommunity. Such an interest is also motivated by the advent of new technologies, whichenable accurate and practical 3D urban mapping. In other words, the proliferation ofdirect geo-referencing, digital imaging system (including medium-format digitalcamera) and LiDAR (Light Detection And Ranging) provide the respective researchbody with the potential to satisfy the detail level and complexity needed by the aboveapplications. Hence, there must be a framework for integrating these different kinds ofsensors. The proposed framework in this paper consists of three main components: 1)Quality Assurance/Quality Control; 2) Co-registration; and 3) Element Matching. Morespecifically, quality assurance of the mapping process and quality control of delivereddata/products are the first components of the proposed framework. Quality assuranceencompasses management activities to ensure that a process, item, or service is of thequality needed by the user. The key activity in the quality assurance is the systemcalibration procedure. After the calibration of the involved systems, quality controlprocedures determine whether the desired quality has been achieved through internaland external evaluation. As the second component of the framework, a registrationprocedure is conducted to ensure that the datasets from different systems are georeferencedwith respect to a common reference frame. After the registration procedure is completed, matching between different information from different systems is carried outto derive realistic 3D urban mapping that takes advantage of the synergisticcharacteristics of the available datasets. For example, the spectral information from adigital imaging system can be related to the positional information from LiDAR. Thepaper will illustrate the main components and the necessary activities of the proposedframework with the help of a real dataset.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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