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Heightmap
Heightmap
Heightmap
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A heightmap created with Terragen
The same heightmap converted to a 3D mesh and rendered with Anim8or

In computer graphics, a heightmap or heightfield is a raster image used mainly as Discrete Global Grid in secondary elevation modeling. Each pixel stores values, such as surface elevation data, for display in 3D computer graphics. A heightmap can be used in bump mapping to calculate where this 3D data would create shadow in a material, in displacement mapping to displace the actual geometric position of points over the textured surface, or for terrain where the heightmap is converted into a 3D mesh.

A heightmap contains one channel interpreted as a distance of displacement or "height" from the "floor" of a surface and sometimes visualized as luma of a grayscale image, with black representing minimum height and white representing maximum height. When the map is rendered, the designer can specify the amount of displacement for each unit of the height channel, which corresponds to the “contrast” of the image. Heightmaps can be stored by themselves in existing grayscale image formats, with or without specialized metadata, or in specialized file formats such as Daylon Leveller, GenesisIV and Terragen documents.

One may also exploit the use of individual color channels to increase detail. For example, a standard RGB 8-bit image can only show 256 values of grey and hence only 256 heights. By using colors, a greater number of heights can be stored (for a 24-bit image, 2563 = 16,777,216 heights can be represented (2564 = 4,294,967,296 if the alpha channel is also used)). This technique is especially useful where height varies slightly over a large area. Using only grey values, because the heights must be mapped to only 256 values, the rendered terrain appears flat, with "steps" in certain places.

Height map of planet earth at 2km per pixel, including oceanic bathymetry information, normalized as 8-bit grayscale

Heightmaps are commonly used in geographic information systems, where they are called digital elevation models.

Creation

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Heightmaps can be created by hand with a classical paint program or a special terrain editor. These editors visualize the terrain in 3D and allow the user to modify the surface. Normally there are tools to raise, lower, smooth or erode the terrain. Another way to create a terrain is to use a terrain generation algorithm. This can be for example a 2D simplex noise function [1] or by diffusion-limited aggregation.[2] Another method is to reconstruct heightmaps from real world data, for example using synthetic aperture radar.[3]

Use

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Heightmaps are widely used in terrain rendering software and modern video games. Heightmaps are an ideal way to store digital terrain elevations; compared to a regular polygonal mesh, they require substantially less memory for a given level of detail. Most modern 3D computer modelling programs are capable of using data from heightmaps in the form of bump, normal, or displacement maps to quickly and precisely create complex terrain and other surfaces.

In the earliest games using software rendering, the elements often represented heights of columns of voxels rendered with ray casting. In most newer games, the elements represent the height coordinate of polygons in a mesh.

Rendering software

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Generating software

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Main article: Scenery generator § Software

Trivia

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Although the terms heightmap and heightfield are often indistinguishable from each other, there is still a small difference in the terms. Heightmap comes from the mathematical term 'map' and heightfield comes from the mathematical term 'vector field'. Heightmap is the more correct description because most heightfields are not a (vector) field in mathematical terms but they are always a map (in mathematical terms as well as in the visual representation).

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Heightmap

View on Grokipedia
from Grokipedia
A heightmap, also known as a heightfield, is a two-dimensional raster image in that stores values for a surface, where each pixel's intensity—typically —encodes the at corresponding (x, y) coordinates to enable the reconstruction of three-dimensional . This representation assumes a single value per horizontal position, making it suitable for modeling landscapes without overhangs or caves. Heightmaps are extensively used in terrain rendering for video games, simulations, and geographic information systems (GIS), where they define the vertical displacement of vertices on a base to create realistic landscapes such as mountains, valleys, and plains. In game engines like Unity, heightmaps are imported as RAW files (often 16-bit for precision) to sculpt procedurally or from real-world (DEM) data, allowing tools to raise, lower, or smooth elevations efficiently. This approach is memory-efficient for large-scale environments, as it requires only a fraction of the storage compared to full polygonal meshes, while supporting level-of-detail techniques for performance optimization during rendering. Beyond , heightmaps serve as displacement maps in shader-based rendering to add fine-scale surface details without increasing counts, such as simulating cracks on rocks or fabric weaves. In techniques like , the height data perturbs texture coordinates to create depth illusions on flat surfaces, with brighter pixels indicating protrusions that occlude adjacent areas for enhanced realism. Common formats include or TIFF for storage, where white pixels denote maximum height and black minimum, often normalized to a 0-1 range for GPU processing. Heightmaps can be generated procedurally using noise functions like for synthetic terrains or derived from and scans in geospatial applications, ensuring compatibility with standards like those from the USGS for accurate elevation modeling. Their simplicity facilitates integration into real-time graphics pipelines, though limitations in representing complex topologies have led to hybrid methods combining heightmaps with or mesh-based extensions in advanced scenarios.

Fundamentals

Definition and Purpose

A heightmap, also referred to as a heightfield, is a two-dimensional raster or that encodes the of points on a three-dimensional surface, where each or stores a scalar value representing the at that location. This representation typically uses a format, with pixel intensities mapping to levels; for instance, in an 8-bit , values range from 0 (, indicating minimum ) to 255 (white, indicating maximum ), allowing for 256 discrete levels. The structure defines a function h:R2→Rh: \mathbb{R}^2 \to \mathbb{R}, where elevations are interpolated between grid points to form a continuous surface. The core purpose of a heightmap is to facilitate the compact storage, manipulation, and rendering of complex 3D data by applying vertical displacements to a base , such as a grid of vertices, based on the encoded height values, thereby avoiding the need for explicit storage of full volumetric or polygonal geometry. This approach significantly reduces computational overhead in real-time applications, as the underlying mesh remains planar in the horizontal dimensions while variations are handled separately. Heightmaps thus serve as a foundational tool for simulating realistic landscapes in fields like , enabling efficient visualization of large-scale environments without prohibitive memory or processing demands. The term and concept originated in during the late 1970s and early 1980s, amid efforts to model natural phenomena like through processes, with foundational work by , Fussell, and Carpenter in 1982 introducing methods for rendering fractal-based surfaces suitable for heightfield representations. Early adoption occurred in simulations for flight simulators, where heightmaps provided a practical means to depict undulating landscapes over vast areas, marking a shift from vector-based to raster-driven techniques. Key advantages of heightmaps include their small file sizes due to 2D storage and quantization, which support scalability across expansive regions like planetary surfaces; additionally, they integrate seamlessly with complementary techniques such as for surface coloration and for lighting details, enhancing visual fidelity without increasing geometric complexity.

Data Representation

Heightmaps encode data through various schemes to facilitate storage and . The most common approach uses , where intensity directly corresponds to height values, with darker shades representing lower and brighter shades indicating higher ones; this method leverages 8-bit or 16-bit depth for sufficient precision in representing subtle variations. Additionally, normalized floating-point values, typically in the range of 0 to 1 for unsigned heights or -1 to 1 for signed displacements relative to a base plane, are employed in binary or formats to handle relative efficiently during . File formats for heightmaps vary by application, balancing compression, metadata support, and compatibility. RAW binary files store uncompressed arrays of height values as simple numeric grids, often in 16-bit integer format for without headers or encoding overhead. Lossless image formats like and TIFF preserve or multichannel data with embedded metadata, such as , making them suitable for both graphics and geospatial workflows. Specialized geospatial formats include HGT, a binary grid used for (SRTM) data with fixed 1 arc-second resolution tiles named by latitude and longitude (e.g., N37W122.hgt), and (DEM) variants like those in or BIL, which support elevation matrices with accompanying headers for projection and units. The resolution of a heightmap defines the spatial detail of the , with each mapping to a specific horizontal interval; for instance, a × grid might cover a 1 km × 1 km area at 1 m per , allowing fine-grained features like ridges or valleys to be captured. Vertical scaling involves applying an exaggeration factor to height values, which stretches the z-axis relative to the x-y plane (e.g., a factor of 2 doubles apparent ) to enhance of subtle topographic differences in visualizations where natural proportions appear flat to human perception. Despite their efficiency, heightmaps have inherent limitations due to their structure, which assigns only a single height value to each (x, y) coordinate, precluding the modeling of overhangs, caves, or other non-vertical features that require full 3D volumetric data. Low-resolution heightmaps exacerbate artifacts, manifesting as jagged edges or terracing in reconstructed surfaces, particularly when fails to smooth transitions between discrete values.

Generation Methods

Manual Creation

Manual creation of heightmaps involves artists and designers using digital tools to directly paint or draw elevation data, providing precise control over features in two-dimensional images where intensity represents height. software, such as or Substance 3D Painter, enables this through specialized brushes that simulate sculpting elevations, allowing users to build up or erode areas by varying brush opacity and pressure to define hills, valleys, and ridges. In Substance 3D Painter, the Height channel supports HDR color formats for painting positive and negative displacements without brightness limits, facilitating detailed manual adjustments. Vector-based drawing tools, like , can also be used to create initial outlines of terrain contours, which are then rasterized into heightmaps for further editing. Additionally, spline-based methods involve drawing smooth contour lines using tools in software like or gCADPlus, which are subsequently extruded or interpolated to generate height values across the map. The typical workflow begins with rough sketches of key features, such as hills and valleys, using basic tools to outline major elevations on a canvas. Artists then iteratively refine these sketches by layering details with brushes or paths, applying manual filters—like for smoothing transitions or custom effects—to mimic natural wear without relying on automation. Finally, the resulting image is exported in standard heightmap formats, such as 16-bit , ensuring compatibility with rendering engines. This process emphasizes iterative editing, where designers test the heightmap in a 3D preview to adjust inconsistencies in real-time. Manual creation offers high artistic flexibility, enabling the design of bespoke landscapes tailored to narrative or aesthetic needs, such as fantastical worlds with exaggerated peaks or hidden chasms that procedural methods might overlook. However, it is time-intensive, often requiring weeks or months for complex terrains, and demands significant skill to avoid inconsistencies like unnatural slopes or artifacts that could disrupt visual coherence. Without supplementary aids, maintaining scale and geological realism can be challenging for non-expert users. In game level design, manual heightmap creation is particularly valuable for adjusting elevations to integrate gameplay elements, such as carving paths for player navigation or raising obstacles to influence combat dynamics, as seen in environments built for projects. This approach allows designers to prioritize player experience over generic randomness, ensuring terrains align with specific level objectives.

Procedural Generation

Procedural generation of relies on algorithmic methods to create synthetic data automatically, enabling scalable and varied landscapes without manual intervention. These approaches draw from mathematical models of processes, producing values across a grid based on pseudo-random functions that mimic geological features like hills, valleys, and plateaus. Core to this process is the use of noise functions, which generate coherent, continuous variations rather than pure randomness, ensuring realistic undulations in the . Among the foundational algorithms is , introduced in 1985 as a gradient noise function that layers coherent noise to simulate natural textures. It addresses the shortcomings of earlier random methods by providing smooth, organic transitions, making it ideal for heightmap undulations. To achieve multi-scale detail, Perlin noise is often combined with (fBm), which sums multiple octaves of noise with decreasing amplitudes to model roughness across scales, as formalized in early procedural models. The fBm height value at a point can be computed as: h(x,y)=∑i=0n−1(12)i⋅noise(2ix,2iy)h(x, y) = \sum_{i=0}^{n-1} \left( \frac{1}{2} \right)^i \cdot \text{noise}(2^i x, 2^i y) where nn is the number of octaves, lacunarity is 2 (doubling frequency per octave), and persistence is 0.5 (halving amplitude), with the noise term contributing progressively finer details. An advancement over is , developed in 2001 to improve computational efficiency, particularly in higher dimensions, by using a simpler simplicial grid that reduces directional artifacts and multiplications compared to Perlin's hypercubic lattice. This makes it suitable for generating three-dimensional heightmaps with less overhead, while maintaining the natural continuity essential for terrain. fBm can similarly incorporate for enhanced performance in multi-scale roughness simulation. Key techniques extend these noise bases to introduce complexity. Domain warping distorts the input coordinates of a noise function using another auxiliary noise layer, creating intricate features such as winding rivers or eroded canyons by folding the terrain non-uniformly. Erosion further refines heightmaps by applying physics-inspired models, like hydraulic , where simulated water flow transports sediment from high to low areas, carving realistic valleys through iterative particle or grid-based computations. Thermal variants model rock breakdown and slumping for additional geological fidelity. Seed-based randomness initializes these algorithms deterministically, allowing reproducible generation by fixing the pseudo-random sequence from a user-provided . Tunable parameters control the output characteristics. Octave count determines the layers of detail, with higher values adding finer textures at the cost of computation. Persistence governs amplitude falloff per octave, typically between 0.5 and 1, where lower values yield smoother terrain by diminishing high-frequency contributions. Lacunarity scales frequency per octave, often set to 2 for doubling detail resolution, promoting fractal-like scaling. Boundary conditions, such as periodic wrapping or mirroring, prevent visible seams in tiled heightmaps for seamless worlds. As of 2025, modern advancements integrate , particularly generative adversarial networks (GANs) trained on real digital elevation models (DEMs), to produce plausible heightmaps more rapidly than traditional noise methods alone. These models learn distributional patterns from data, enabling conditional generation from sketches or parameters, and outperform rule-based approaches in capturing rare geological motifs while reducing manual tuning.

Real-World Data Acquisition

Real-world heightmaps, also known as , are derived from physical measurements using various technologies to capture elevations with high fidelity to actual landscapes. Satellite altimetry, such as , provides broad-scale data; for instance, NASA's in 2000 generated a near-global dataset covering nearly 80% of Earth's land surfaces at 30-meter resolution, using to measure surface heights. Similarly, the ASTER Global (GDEM), released in 2009 with major versions in 2011 (v2) and 2019 (v3), utilizes optical from the Advanced Spaceborne Thermal Emission and Reflection Radiometer to produce 30-meter gridded elevations worldwide. Airborne (Light Detection and Ranging) offers centimeter-level precision for detailed surveys by emitting and measuring their return times; the hh is calculated as h=[c](/page/Speedoflight)⋅t2−h = \frac{[c](/page/Speed_of_light) \cdot t}{2} - ground offset, where cc is the and tt is the round-trip of the . This time-of-flight method enables vertical accuracies better than 10 cm in optimal conditions, making ideal for high-resolution mapping over targeted areas. complements these by inferring elevations from stereo imagery, often captured by drones, where is estimated through —the relative displacement of features between overlapping taken from different viewpoints. This technique generates DEMs by mathematically reconstructing 3D from 2D pairs, suitable for both aerial and UAV platforms. Acquired data typically requires processing to form usable heightmaps, beginning with interpolation of sparse point clouds to a ; (IDW), for example, estimates elevations at grid nodes by weighting nearby measurements inversely proportional to their distance, preserving local variations. Datum adjustments are essential to align data with reference systems, such as converting from the WGS84 to mean (MSL) for hydrological applications, ensuring consistent vertical referencing across datasets. Error correction addresses artifacts like vegetation penetration in or radar shadowing, using techniques such as ground filtering to remove non-terrain returns and statistical adjustments based on auxiliary data like altimetry for vegetated or urban areas. Global datasets like SRTM and ASTER GDEM offer 30-meter resolutions with near-complete land coverage but face challenges in data voids, particularly in polar regions due to low solar illumination or persistent , and oceanic areas where signals reflect off water surfaces without penetrating to . In contrast, local surveys using USGS achieve 1-meter resolutions over the , providing dense point clouds for urban or forested terrains but limited to specific project areas. These voids necessitate infilling with multi-source merging, as seen in efforts to create gapless models for . Access to these datasets varies, with public domain releases available through platforms like USGS EarthExplorer, which distributes SRTM, ASTER, and -derived DEMs free of charge for non-commercial use. High-resolution proprietary data, such as commercial surveys, may require purchase for specialized applications. As of 2025, the Copernicus DEM has seen improvements from and satellites, including better infilling over regions like and the Spanish at 30-meter resolution, enhancing global coverage and accuracy.

Applications

Computer Graphics and Rendering

In computer graphics, heightmaps serve as a fundamental tool for terrain visualization in real-time rendering pipelines, enabling efficient representation of three-dimensional landscapes on two-dimensional grids. By encoding elevation data in grayscale textures, heightmaps allow graphics engines to displace base meshes, creating the illusion of complex geometry without the computational overhead of full polygonal models. This integration is particularly prominent in game development and virtual reality applications, where interactive performance is paramount. Rendering techniques for heightmaps primarily revolve around displacement mapping, where vertex positions are adjusted based on sampled height values to generate undulating surfaces. In vertex shaders, this is achieved by sampling the heightmap texture at a vertex's texture coordinates and offsetting the position accordingly; for example, in GLSL, the transformed position can be computed as vec3 pos = basePos + vec3(0.0, texture(heightTex, texCoord).r * scale, 0.0);. This method, known as vertex displacement, efficiently deforms low-resolution grids into detailed terrains during the geometry stage of the pipeline. To enhance detail adaptively, tessellation subdivides patches dynamically on the GPU, applying displacement to newly generated vertices for smoother transitions and higher fidelity in close-up views, as demonstrated in hardware-accelerated approaches that leverage DirectX 11 or Vulkan tessellation units. Additionally, parallax occlusion mapping (POM) provides a pixel-level illusion of depth on flat-shaded surfaces by iteratively offsetting texture coordinates along the view ray using heightmap samples, simulating self-occlusion and adding subsurface relief without increasing geometry complexity; this technique traces multiple depth samples per fragment to approximate ray marching, improving visual realism for bumpy terrains at minimal cost. Heightmaps are often combined with complementary texture maps to achieve photorealistic lighting and material effects. Normal maps, which define surface tangents for specular highlights and shadows, are commonly derived from heightmaps by computing gradients via the Sobel filter: the partial derivatives ∂h/∂x and ∂h/∂y approximate slope directions, normalized to form the normal vector (n_x, n_y, 1). These normals integrate seamlessly with (PBR) workflows, where textures provide base colors modulated by the displaced geometry. For instance, in lit terrains, the combined maps enable accurate diffuse and specular responses under dynamic lights, enhancing the perceptual depth of procedural landscapes. Performance optimizations are crucial for real-time applications, as high-resolution heightmaps can strain GPU resources. Level-of-detail (LOD) systems switch between progressively coarser heightmap resolutions based on viewer distance, reducing vertex processing and draw calls; in Unity, terrain components automatically manage LOD groups for height-displaced meshes, blending seams to avoid popping artifacts. Mipmapping applies filtered downsamplings to height textures, mitigating aliasing on distant surfaces by selecting appropriate levels during sampling, which cuts texture fetch bandwidth in large scenes. Modern engines like Unreal leverage GPU acceleration through compute shaders for asynchronous heightmap updates and displacement, supporting massive worlds without frame drops. The use of heightmaps in graphics has evolved significantly since early pseudo-3D engines. In DOOM (1993), sector-based elevation simulated basic height variations without true heightmaps, laying groundwork for rendering. By 2016, employed procedural heightfields for infinite planetary terrains, displacing voxel-based meshes in real-time. As of 2025, trends emphasize real-time on height-displaced surfaces, with ray-traced denoising techniques in engines like Unreal Engine 5 applying indirect lighting to dynamically tessellated terrains, achieving cinematic quality at interactive rates through hardware-accelerated RT cores.

Geographic Information Systems

In Geographic Information Systems (GIS), heightmaps, often implemented as Digital Elevation Models (DEMs), serve as foundational raster datasets for terrain representation and , enabling the modeling of topographic surfaces to support environmental decision-making and . These models capture variations across landscapes, facilitating the integration of topographic data with other geospatial layers to analyze surface processes such as , drainage, and . By providing a continuous surface, heightmaps underpin quantitative assessments that inform planning in areas like conservation, infrastructure development, and disaster mitigation. Analytical applications of heightmaps in GIS emphasize deriving derivative surfaces for analysis. and aspect are computed from the grid to quantify steepness and orientation, respectively, with the θ calculated as θ = atan(√((∂h/∂x)^2 + (∂h/∂y)^2)), where ∂h/∂x and ∂h/∂y represent partial derivatives of h with respect to easting x and northing y; this , based on the magnitude, is widely used in tools like to identify erosion-prone areas. analysis extends this by determining line-of-sight visibility from observer points across the , accounting for obstructions to map visible areas, as implemented in Visibility tools for applications like site suitability assessment. relies on flow accumulation, where upstream contributing areas are tallied from flow direction rasters derived from the heightmap, enabling the identification of drainage basins and stream networks via thresholds on accumulation values. Heightmaps integrate seamlessly with vector data in GIS workflows, enhancing multi-layer analyses. For instance, roads or boundaries from vector shapefiles can be overlaid on DEMs in platforms like to evaluate impacts on infrastructure, such as identifying steep gradients along routes. In hydrological modeling, height-based flow direction algorithms simulate by directing water downslope from each cell, supporting predictions of pathways and sediment transport without requiring dynamic simulations. Urban planning leverages elevation thresholds from heightmaps to assess risk, classifying low-lying areas below specified contours as vulnerable zones to guide and resilient design. Standardized formats ensure interoperability of heightmap data in GIS. serves as a primary container for georeferenced rasters, embedding coordinate reference system (CRS) metadata like Universal Transverse Mercator (UTM) projections to align elevations with real-world coordinates. Global datasets exemplify this, such as the EU-DEM, a 1 arc-second (approximately 30 m) resolution model covering European , derived from satellite data for consistent continental-scale analysis. As of 2025, advancements in AI-enhanced GIS tools have improved heightmap utility through super-resolution techniques that upscale low-resolution satellite-derived DEMs. These methods, employing transformer-based networks, predict fine-scale micro-terrain features like subtle ridges from coarser inputs, achieving higher for detailed environmental modeling while reducing costs.

Simulation and Modeling

Heightmaps play a crucial role in dynamic simulations by providing data that enables the modeling of interactions over time. In and models, heightmaps serve as the foundational grid for cellular automata simulations, where local rules iteratively adjust cell elevations to mimic processes like hydraulic and sediment deposition, thereby evolving landscapes realistically. For instance, these models apply operators to height values, simulating water flow and material transport to generate plausible features such as valleys and riverbeds. In modeling, heightmaps act as inputs to general circulation models (GCMs), informing orographic by representing topographic forcing that lifts moist air, leading to enhanced rainfall on windward slopes. These models integrate height data to parameterize vertical motion and moisture convergence, improving predictions of patterns in mountainous regions. Additionally, in , heightmaps facilitate A* algorithms on discretized grids, where differences inform cost functions for obstacle avoidance and optimal routing across uneven , enabling autonomous in rough environments. Key techniques leverage heightmaps for physics-based interactions in simulations. Physics engines like integrate heightmaps via heightfield terrain shapes, performing through ray queries against the elevation grid to resolve vehicle or object impacts on dynamic surfaces. In virtual reality (VR) terrain walking, heightmaps drive haptic feedback systems by mapping elevation gradients to force responses on user interfaces, simulating tactile sensations of slopes and textures for immersive training. For disaster simulations, such as tsunami inundation, heightmaps are inverted to model water flow over , with elevation thresholds determining extent and velocity in hydrodynamic models. Practical examples highlight heightmaps' utility in specialized simulations. Military training simulators employ heightmaps to construct realistic battlefields, allowing trainees to navigate procedurally enhanced terrains that evolve with mission scenarios for tactical rehearsal. In automotive testing, heightmaps represent hilly environments in virtual prototypes, enabling dynamics simulations to evaluate suspension performance and stability on varied elevations. By 2025, emerging applications integrate heightmaps into urban digital twins for infrastructure management, where elevation data combines with IoT sensors to enable real-time updates and predictive modeling of impacts on systems like drainage and .

Tools and Software

Generation Software

Heightmap generation software encompasses a range of tools designed for creating, editing, and exporting elevation data in raster formats, catering to users in , GIS, and fields. These programs typically support procedural algorithms, manual editing, or integration with real-world data sources, enabling the production of 2D grayscale images where intensity represents height. Among open-source and free options, provides built-in displacement tools that allow users to apply heightmaps to subdivided planes via the Displace modifier, facilitating terrain creation and within a comprehensive environment. Similarly, offers plugins such as the OpenTopography DEM Downloader and EasyDEM for generating digital elevation models (DEMs) from survey data or global datasets like SRTM, supporting raster processing and export in formats suitable for heightmap use. For manual , serves as a versatile image editor where heightmaps are manipulated as 16-bit layers, enabling precise adjustments through brushes, filters, and transformations before export. Commercial tools like World Machine emphasize node-based procedural generation, featuring erosion modules that simulate hydraulic and thermal processes to produce realistic terrains, with support for real-time previews and exports in heightmap formats such as TIFF and EXR. Its 2020s successor, Gaea, builds on this with a non-destructive node graph for terrain design, incorporating advanced simulations for erosion and deposition, and offering batch processing for large-scale projects in VFX and games. Terragen, another photorealistic generator, allows creation from procedural noise or imported real-world data, generating heightfields via its Heightfield Generate node and supporting high-resolution outputs for film and visualization. Vue integrates heightfield terrain modeling directly into its rendering pipeline, enabling procedural landscapes with real-time adjustments, often used in film VFX for seamless terrain-to-scene workflows. For advanced procedural workflows, Houdini provides heightfield tools that enable complex, parametric terrain generation through VEX scripting and simulations, contrasting with GIMP's pixel-level manual approach by prioritizing algorithmic control and scalability. Key features across these tools include broad import/export compatibility (e.g., , RAW, ), real-time previewing for , and to handle multiple resolutions efficiently, though usability varies—simpler interfaces like suit beginners, while Houdini's depth appeals to professionals. These generation tools often integrate briefly with rendering engines for , but focus primarily on initial heightmap production.

Rendering and Processing Tools

Graphics engines like and provide robust systems for importing, editing, and rendering heightmaps in real-time environments. Unity's Terrain system supports importing heightmaps in RAW format (typically 16-bit ) to generate terrain geometry, with built-in Level of Detail (LOD) management that dynamically adjusts complexity based on distance to optimize rendering performance. This allows developers to visualize large-scale terrains efficiently, integrating height data with splat maps for texturing and during gameplay or simulation. Unreal Engine's Landscape tool enables spline-based editing of heightmaps, where users can draw and deform using spline curves to create roads, rivers, or contours that automatically adjust underlying height values for seamless integration. For runtime processing, the system supports via materials, allowing dynamic height modifications during execution through vertex shaders or scripting, which is useful for procedural alterations in interactive applications. Post-processing tools extend heightmap refinement beyond initial creation. Substance Designer facilitates procedural texturing directly on heightmaps using nodes like Height Blend, which merges two grayscale height inputs to produce a combined heightmap and mask, enabling layered adjustments such as blending features with opacity and contrast controls for realistic surface detailing. Mudbox, a digital sculpting application, allows fine-grained adjustments to height-based models through intuitive tools that modify vertices, followed by exporting displacement maps (equivalent to heightmaps) in formats compatible with rendering pipelines for high-fidelity polishing. Specialized processors cater to GIS-oriented workflows. offers reprojection of heightmap data (such as DEMs) to align with different coordinate systems via , ensuring compatibility across spatial datasets without manual rendering. It includes filtering tools for grids, such as removal and pit filling, alongside hydraulic through hydro-flattening and to model flow and smoothing effects. As of 2025, trends in heightmap rendering emphasize cloud-based collaboration, with enabling real-time sharing of USD-formatted assets, allowing teams to process and visualize heightmaps across distributed workflows using RTX-accelerated rendering.

References

  1. https://dicom.nema.org/Dicom/News/January2024/docs/sups/sup240-slides.pdf
  2. https://docs.unity3d.com/6000.2/Documentation/Manual/terrain-Heightmaps.html
  3. https://learnopengl.com/Guest-Articles/2021/Tessellation/Height-map
  4. https://docs.unity3d.com/6000.2/Documentation/Manual/StandardShaderMaterialParameterHeightMap.html
  5. https://www.usgs.gov/faqs/what-a-digital-elevation-model-dem
  6. https://www.cs.purdue.edu/cgvlab/www/resources/papers/Galin-Computer_Graphics_Forum-2019-A_Review_of_Digital_Terrain_Modeling.pdf
  7. https://dl.acm.org/doi/10.1145/358523.358553
  8. https://ohiostate.pressbooks.pub/graphicshistory/chapter/13-6-other-approaches/
  9. https://gis.stackexchange.com/questions/206537/geotiff-to-16-bit-tiff-png-or-bmp-image-for-heightmap
  10. https://gdal.org/en/stable/drivers/raster/srtmhgt.html
  11. https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm
  12. https://www.worldofleveldesign.com/categories/ue4/landscape-heightmap-guide.php
  13. https://mountaincartography.icaci.org/activities/workshops/zakopane_poland/presentations/Prochazka.pdf
  14. https://www.cse.ust.hk/~huamin/cgi03.pdf
  15. https://www.gamedev.net/blogs/entry/1631441-increasing-the-resolution-of-heightmaps/
  16. https://helpx.adobe.com/substance-3d-painter/painting/advanced-channel-painting/height-map-painting.html
  17. https://www.e-education.psu.edu/natureofgeoinfo/c7_p6.html
  18. https://www.kgs.ku.edu/Tis/surf3/s3Cont2.html
  19. https://dev.epicgames.com/documentation/en-us/unreal-engine/creating-and-using-custom-heightmaps-and-layers-in-unreal-engine
  20. https://onlinelibrary.wiley.com/doi/10.1155/2010/360458
  21. https://www.classes.cs.uchicago.edu/archive/2015/fall/23700-1/final-project/MusgraveTerrain00.pdf
  22. https://www.cs.drexel.edu/~david/Classes/Papers/p287-perlin.pdf
  23. https://mrl.cs.nyu.edu/~perlin/paper445.pdf
  24. https://cgvr.cs.uni-bremen.de/teaching/cg_literatur/simplexnoise.pdf
  25. https://www.cs.purdue.edu/cgvlab/www/resources/papers/Benes-2002-Visual_simulation_of_hydraulic_erosion.pdf
  26. https://diglib.eg.org/bitstreams/184c50f6-f7d5-4fce-b44a-15afd6507d1e/download
  27. https://dl.acm.org/doi/fullHtml/10.1145/3582437.3587183
  28. https://www.earthdata.nasa.gov/data/instruments/srtm
  29. https://asterweb.jpl.nasa.gov/gdem.asp
  30. https://pmc.ncbi.nlm.nih.gov/articles/PMC11360157/
  31. https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/photogrammetry
  32. https://scispace.com/pdf/dem-generation-based-on-commercial-uav-photogrammetry-data-3xy98rfflb.pdf
  33. https://rosap.ntl.bts.gov/view/dot/23265/dot_23265_DS1.pdf
  34. https://lpdaac.usgs.gov/documents/1273/NASADEM_User_Guide_V11.pdf
  35. https://repository.library.noaa.gov/view/noaa/48453/noaa_48453_DS1.pdf
  36. https://www.researchgate.net/publication/361986074_A_Method_for_SRTM_DEM_Elevation_Error_Correction_in_Forested_Areas_Using_ICESat-2_Data_and_Vegetation_Classification_Data
  37. https://www.pgc.umn.edu/guides/stereo-derived-elevation-models/pgc-dem-products-arcticdem-rema-and-earthdem/
  38. https://pubs.usgs.gov/tm/11/b07/tm11-b7.pdf
  39. https://www.sciencedirect.com/science/article/abs/pii/S092427162200034X
  40. https://earthexplorer.usgs.gov/
  41. https://opentopography.org/news/updated-copernicus-30m-DEM-available
  42. https://developer.nvidia.com/gpugems/gpugems2/part-i-geometric-complexity/chapter-8-pixel-displacement-mapping-distance-functions
  43. https://ieeexplore.ieee.org/document/7021710/
  44. https://learnopengl.com/Advanced-Lighting/Parallax-Mapping
  45. https://developer.arm.com/documentation/102696/latest/Texture-mipmapping
  46. https://desktop.arcgis.com/en/arcmap/latest/analyze/commonly-used-tools/surface-creation-and-analysis.htm
  47. https://spatialanalysisonline.com/HTML/gradient__slope_and_aspect.htm
  48. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/viewshed.htm
  49. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/how-flow-accumulation-works.htm
  50. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/how-watershed-works.htm
  51. https://pro.arcgis.com/en/pro-app/latest/help/mapping/layer-properties/layers.htm
  52. https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/how-flow-direction-works.htm
  53. https://www.scirp.org/journal/paperinformation?paperid=131418
  54. https://ec.europa.eu/eurostat/web/gisco/geodata/digital-elevation-model/eu-dem
  55. https://www.sciencedirect.com/science/article/pii/S1569843223003205
  56. https://www.geoconnexion.com/in-depth/novel-application-of-ai-provides-the-most-accurate-model-of-the-earths-surface-publicly-available
  57. https://downloads.hci.informatik.uni-wuerzburg.de/Ziegler2020aa.pdf
  58. https://www.isprs.org/proceedings/xxxvii/congress/6b_pdf/04.pdf
  59. https://gmd.copernicus.org/articles/15/2063/2022/
  60. https://www.eoas.ubc.ca/~mjelline/453website/eosc453/E_prints/AnnRev.33.092203.122541.pdf
  61. https://www.sciencedirect.com/science/article/pii/S2405896323001441
  62. https://arxiv.org/abs/2209.07252
  63. https://pybullet.org/Bullet/BulletFull/classbtHeightfieldTerrainShape.html
  64. https://www.ijeat.org/wp-content/uploads/papers/v5i1/A4292105115.pdf
  65. https://www.sciencedirect.com/science/article/pii/S0029801825007322
  66. https://nctr.pmel.noaa.gov/sim.html
  67. https://www.mathworks.com/help/nav/ug/offroad-planning-on-digital-elevation-models.html
  68. https://www.mdpi.com/2624-6511/8/5/175
  69. https://www.cogitatiopress.com/urbanplanning/article/download/10109/4602
  70. https://www.world-machine.com/
  71. https://plugins.qgis.org/plugins/OpenTopography-DEM-Downloader/
  72. https://planetside.co.uk/wiki/index.php/Heightfield_Generate
  73. https://dev.epicgames.com/documentation/en-us/unreal-engine/landscape-splines-in-unreal-engine
  74. https://dev.epicgames.com/documentation/en-us/unreal-engine/landscape-technical-guide-in-unreal-engine
  75. https://helpx.adobe.com/substance-3d-designer/substance-compositing-graphs/nodes-reference-for-substance-compositing-graphs/node-library/material-filters/effects-material/height-blend.html
  76. https://www.autodesk.com/products/mudbox/overview
  77. https://download.autodesk.com/global/docs/mudbox2013/en_us/files/GUID-A602E288-A771-4225-B67B-6034CCFDF916.htm
  78. https://www.bluemarblegeo.com/global-mapper/
  79. https://www.bluemarblegeo.com/blog/hydro-flattening-when-creating-a-dem-in-global-mapper/
  80. https://www.nvidia.com/en-us/omniverse/
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