High resolution LiDAR has reshaped contours, vegetation depiction, and fairness considerations in foot orienteering
What LiDAR adds to an orienteering basemap
Airborne lidar collects dense point clouds that separate ground, vegetation, and structures, which lets mappers derive digital terrain models and digital surface models with meter level grids in many regions. From these layers they generate slope shading, curvature, and closely spaced contours that reveal subtle spurs, reentrants, and flats that older photogrammetric sources often missed. National programs and local projects commonly publish lidar with sufficient point density to capture microrelief used in competition terrain. Clubs use these data to build georeferenced basemaps that align cleanly with GNSS tracks, which reduces drafting time and improves positional consistency. The result is a starting canvas that is richer, crisper, and more internally consistent than earlier basemaps.
Workflows have evolved because lidar enables early detection of unmapped earth banks, erosion features, and subtle terraces before any field visit. Automated products accelerate drafting, but they do not replace on the ground verification, so survey remains decisive for what is kept, simplified, or discarded. The greatest practical gain is that field time can focus on things lidar does not reliably show, such as runnability nuances, rootstocks, and seasonal vegetation density. Overall, lidar shifts effort from initial exploration to targeted confirmation.
Contours from point clouds - intervals, smoothing, and generalization
ISOM sets a standard contour interval of 5 m for foot orienteering maps, with 2.5 m permitted in very flat terrain when a finer interval improves shape recognition. Mixed intervals on the same sheet are not allowed, so the chosen interval must be consistent across the mapped area. Form lines may be added sparingly to complete the terrain picture, but too many form lines can distort perceived steepness and harm legibility. Index contours and labels must be positioned so that other details are not obscured in print. These rules predate lidar but have become even more important because high resolution data can tempt over depiction. Good mapping still prioritizes clarity and standardization over raw data density.
Smoothing is an intentional step because lidar derived contours can be jagged at one meter grids and may over emphasize minor undulations. Many mappers use curvature or topographic position methods to smooth lines and then adjust them manually so that small bends and mouths of reentrants satisfy minimum sizes. This combination preserves overall shape while reducing noise that would impede fast reading.
Minimum graphical dimensions protect map readability in complex terrain. The smallest bend, the width of a reentrant mouth, and the sizes for knolls and depressions are constrained so they remain recognizable at speed. When a knoll or hollow is important but undersized, it should be symbolized with the appropriate point symbol rather than drawn to scale as a tiny contour wiggle. Contours must interplay logically with cliffs and banks to maintain a coherent three dimensional picture. These practices ensure that lidar precision supports, rather than overwhelms, decision making.
Vegetation layers - canopy height, density, and seasonal stability
Vegetation mapping from lidar commonly starts with a canopy height model generated by subtracting the ground model from the surface model. Height thresholds can provide first pass classes that correlate with white forest, light green, and darker slow run areas, but correlation is imperfect because runnability depends on understory and ground cover. Leaf off acquisitions improve ground classification yet may under represent summer foliage that slows running.
Multiple returns, intensity values, and point density patterns add context about foliage structure, but they do not directly describe bramble, deadfall, or seasonal grass height that can change quickly. In conifer stands, canopy height may be high while lower branches and slash severely reduce speed, which the data cannot reliably detect. Conversely, open birch with low canopies may run very fast despite a modest height class. Field checks remain essential to calibrate categories to local conditions.
Many successful projects adopt a two step practice. First they derive continuous metrics such as canopy height and percentile heights to spot likely boundaries, then they draw clean, generalized vegetation edges that meet minimum areas and preserve readability. Symbol sets and color screens must remain legible at chosen scales, so edges are simplified to avoid visual noise from pixel artifacts. Teams revisit green values after test running because the fastest lines may contradict automated thresholds. Seasonal stability also matters, so planners consider survey timing and the month of the event when confirming how much to mark as slow or walk. This disciplined approach reduces misclassification that could bias route choice.
Before and after - typical map edits triggered by lidar
Before example. An older forest map built from photogrammetry shows 5 m contours with broad, rounded spurs and few minor reentrants, plus sparse knoll symbols. After a full remap that adopts a 2.5 m interval across the sheet in flat terrain that justifies it, several shallow reentrants and saddle shapes appear that meet minimum graphical sizes. A handful of form lines clarify large flats without cluttering steep slopes. The updated basemap improves relocation because attackpoints on subtle spurs now read consistently with the ground. Control placement options expand without exceeding legibility limits that apply at race scale.
Another example concerns vegetation and rock detail. A scanned map shows one uniform slow run area that competitors know to be patchy and three rocky slopes drawn as a single generalized area. The lidar canopy model and intensity hint at denser patches that the survey confirms and generalizes into distinct green shapes that satisfy minimum area rules, while slope shading exposes a discontinuity that leads to a new passable cliff and refined stony ground. The after version removes two tiny rock objects that fail minimum sizes and moves a boulder cluster for positional coherence with contours. Route choices across that hillside become more predictable because the terrain picture is both richer and cleaner.
Course design implications when detail density increases
Higher contour fidelity encourages legs that reward fine compass work, precise attackpoints, and micro relocation skills, but it can also make reading harder at speed if generalization is weak. Planners test whether a leg remains fair when read at running speed with standard symbol sizes and printing quality. Short controls placed on tiny pits or shallow knolls that only lidar revealed may disadvantage those who cannot parse the clutter quickly. Better practice is to set controls on features that are unambiguous at the chosen scale while still letting fine detail influence route choice. This protects both technical challenge and fairness. Test runners provide direct evidence of whether map reading load is reasonable for the class and terrain.
Detail rich basemaps also affect leg length balancing. When many fine features are present, longer route choice legs can create variety between legs that emphasize macro decisions and legs that test execution into a solid attackpoint. Vague middling lengths that over rely on microforms tend to be less fair because small depiction choices can dominate outcomes. Mixed leg types help reduce luck and amplify skill.
Vegetation updates derived from lidar should not dictate leg design without verification. A dark green corridor that came from automated thresholds may look striking yet be passable in reality, leading to unintentionally unbalanced legs that funnel everyone through the same gap. Planners avoid such traps by cross checking with survey notes and by offering at least two viable lines where possible.
Fairness and rules that govern maps and legs
Fairness in foot orienteering is anchored in principles that require courses to be planned so that all competitors face the same conditions and that luck is minimized. Map legibility is a core requirement, and it is protected by symbol sizes, minimum dimensions, and rules for contour intervals and form line use. Event advisers and controllers check that maps conform to specifications and that planned legs do not offer hidden advantages that are not foreseeable from the printed map under race conditions. The availability of open data does not change these duties, it raises the bar for consistent application. Teams that apply lidar while honoring generalization create fairer, not easier, competitions.
Perspectives from practice - mapper and course setter on tradeoffs
Mapper perspective emphasizes disciplined selection rather than maximum extraction from data. Practitioners highlight that lidar speeds drafting and improves georeferencing, but the decisive quality gains come from respecting minimum sizes and deleting contour wiggles that fail to communicate shape at speed. Many recommend generating both raw and smoothed contour sets, inspecting disagreements, and then hand editing lines so that reentrant mouths and knolls meet specification footprints. Course setter perspective stresses foreseeability and balance when lidar raises detail density. Setters test legs with printed maps at race scale, avoid controls that hinge on minute features only detectable because of data novelty, and integrate modern data with long standing fairness principles.