Engineer Surveying Geo-Spatial Information common mistakes
Engineer Surveying Geo-Spatial Information · Understand the traps and how to avoid them.
Treating a grid distance from a state plane or UTM projection as if it were the ground distance when checking a boundary or computing an area, without applying the scale factor and elevation (sea-level) correction.
Projection coordinates are convenient and look like ordinary metric values, so candidates forget that map distance equals ground distance multiplied by the combined factor. The grid-to-ground correction is small (often tens to hundreds of ppm) and easy to overlook, especially when the numbers 'look right'.
What to do instead
Before comparing any projected distance to a measured or deed distance, compute the combined factor as the product of the projection scale factor at the point and the elevation factor (R/(R+h)). Divide grid distance by the combined factor to get ground distance, and check that the resulting discrepancy is consistent with the expected magnitude. For areas, apply the factor squared.
Confusing accuracy, precision, and uncertainty when interpreting a survey specification or a GNSS/TS result; for example, quoting a 5 mm + 1 ppm RTK specification as if it guaranteed 5 mm absolute accuracy, or reporting a mean of repeated observations as if the scatter proved the mean was correct.
Everyday language uses these words interchangeably, and instrument datasheets mix random noise, systematic bias, and confidence levels. Candidates latch onto the smallest number in a specification and treat it as total error.
What to do instead
Separate the error budget explicitly: random (precision) from repeated observation scatter, systematic (bias) from calibration, centring, and atmospheric effects, and blunder detection from redundancy and misclosure checks. State the confidence level (e.g., 95%) and whether the figure is relative or absolute. Compare the specification to the actual observation model rather than to a single number.
Assuming a datum transformation is unnecessary or that 'WGS84' and a local datum such as NAD83 or a national grid are interchangeable, then mixing coordinates from different datums in one adjustment or GIS layer without transformation.
Modern realisations differ by only centimetres to a couple of metres, so a quick check on a small site can appear to agree. Software often silently defaults to a null transformation, hiding the problem until a boundary or a large-extent overlay is checked.
What to do instead
Identify the datum and realisation (including epoch) for every coordinate set before use. Apply an explicit, documented transformation with published parameters and a stated accuracy, and record it in the metadata. Where a null transformation is used, justify it against the required positional tolerance rather than assuming it is harmless.
Reading a deed or boundary description by following the bearing and distance calls only, ignoring the rules of construction (monuments over distances, natural monuments over artificial, adjoining owners' lines, area as a check) and ignoring the order of calls and any stated basis of bearings.
The mathematics is straightforward and satisfying, so candidates treat boundary retracement as a traverse-closure exercise. Deed language and hierarchy-of-evidence rules are less familiar and are often skimmed.
What to do instead
Before computing, extract the controlling elements: basis of bearings, monuments called, order of calls, qualifying words, and any area statement. Apply the hierarchy of evidence to decide which calls control, then use the geometry as corroboration. Show explicitly how the chosen evidence resolves conflicts and where the computed position differs from the record.
Choosing a map projection or coordinate system by habit (e.g., always Web Mercator or always a UTM zone) and then measuring distances, areas, or directions on it without checking distortion or the zone/hemisphere.
Default settings in GIS and web mapping tools make one projection the path of least resistance, and the visual result looks plausible. Distortion is invisible on a screen and only shows up in analysis.
What to do instead
State the analysis requirement first (distance, area, shape, direction, or a mix), then select a projection whose distortion properties match it, and quantify the distortion at the extents of the study area. For small local work, consider a low-distortion local grid; for equal-area needs, use an equal-area projection and say so in the deliverable.
Reporting a GIS overlay or spatial-query result as if it were a measurement, ignoring positional error propagation, sliver polygons, and the effect of scale and digitising error on area and length.
GIS operations return exact-looking numbers, and candidates focus on tool mechanics (clip, intersect, buffer) rather than on the uncertainty of the inputs. Slivers and topology errors are easy to dismiss as cosmetic.
What to do instead
Quantify input positional uncertainty and propagate it (e.g., buffer-based or Monte Carlo sensitivity) before quoting area or length. Check topology and resolve slivers deliberately. State the source scale, positional accuracy, and any generalisation applied, and report results with an uncertainty statement rather than as exact values.
Treating remote sensing indices or a classified image as ground truth without validating them; for example, quoting an NDVI threshold or a land-cover area without checking atmospheric correction, cloud masking, mixed pixels, or an accuracy assessment.
Indices and classifications produce attractive maps and simple numbers, and the processing chain's assumptions are buried in defaults. Validation requires extra field data that candidates may not think to request.
What to do instead
Describe the full chain: sensor, acquisition date, radiometric and atmospheric correction, cloud and shadow masking, resolution and mixed-pixel effects. Validate with an independent reference sample and report an error matrix with user's, producer's, and overall accuracy plus confidence intervals. Do not present classified area as fact without that assessment.
Ignoring metadata, lineage, and quality elements when delivering a dataset, or treating a standard such as a spatial data quality standard as paperwork rather than as a specification to test against.
Standards documents are long and abstract, and metadata is often auto-generated with empty fields. Candidates focus on producing the data and leave description and quality reporting to the end.
What to do instead
Treat the standard as a checklist at project start: define the data product specification, the quality elements and measures, and the metadata profile before collecting data. Populate metadata at each processing step (lineage) and test the deliverable against the stated quality measures, reporting conformance results rather than asserting compliance.
Answering an ethics or professional-practice question by choosing the option that satisfies the client or employer, or by sealing or certifying work outside one's competence or authority, rather than applying the statutory and ethical duty to the public.
Candidates read the scenario as a customer-service or project-management problem, and the 'helpful' answer looks right. The legal limits on who may survey, seal, or certify, and the duty to report unsafe or non-conforming work, are less familiar.
What to do instead
Identify the jurisdiction's statutory framework first: who may practise, what a seal or certification asserts, and what must be reported. Apply the hierarchy of obligations (public safety, then law and standards, then client), and choose the option that documents the issue, declines work outside competence, and refers it appropriately, even if that is less convenient.
Adopting a new technology (UAV photogrammetry, LiDAR, GNSS-RTK, or a cloud processing service) without an independent check on a known point, and assuming the vendor's stated accuracy applies to the project conditions.
New tools are marketed with best-case accuracy figures and produce dense, impressive outputs. The failure modes (GCP distribution, IMU drift, multipath, canopy penetration) are not visible in the final product.
What to do instead
Design a verification independent of the technology: check against known control or an independent measurement method, state the check's tolerance, and report the observed agreement. Document the conditions (baseline length, satellite geometry, GCP layout, flight parameters) and confirm they match the accuracy claim before relying on it.