Attributes of Topographic Mapping of a Fast Urbanising Area in Nigeria, Using Remote Sensing and GIS
Bibliographic record
Abstract
Aims: To produce an updated 1:25,000 topographic map of Anyigba through the application of geospatial technologies – GIS techniques, Remote Sensing data, GPS and other ancillary hardware and software. Study Design: Application of satellite imageries and GIS software for the production of updated topographic map of Anyigba Town in Nigeria. Place and Duration of Study: GIS Laboratory, Department of Geography and Planning, Kogi State University, Anyigba, Nigeria, between April and July 2012. Methodology: Satellite image processing, classification and vectorization, visually-aided interpretation, digitization and geocoding of features, using ArcGIS 9.2, ILWIS 3.3 Academia, AutoCAD 2010 and Microsoft Excel 2010 software. Results: Topographic map created through the integration of point map, contour line map, land use classification map, planimetric map, digital elevation model (DEM) and digital terrain model (DTM). The built-up area has grown in an omni-directional pattern, annexing most surrounding villages. It was discovered that Anyigba is characterized by a gentle undulating landscape architecture, with some areas of marked elevation and depression as against what was reflected in the old toposheets (248 NW and 268 SW) produced in 1973. The DEM and DTM generated from the 1973 toposheets and the satellite imageries of 2001, 2005 and 2008 showed that changes in the topography is a direct result of unplanned expansion of the built-up area. Conclusion: The method is considered relatively cheaper and time-effective for updating topographic maps in Developing Countries where resources are scarce. The study suggests periodic research to update the topographic map of Anyigba as part of contributions towards building the much required National Elevation Dataset (NED) and Geospatial Data Infrastructure (GDI) in Nigeria for updating environmental planning and management.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".