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How to Buy
For a gallery of Manifold System Release 8.00 videos, examples and links to live Internet Map Server (IMS) web sites, please visit the Release 8.00 Gallery page. Manifold System Release 8.00, the world's best classic GIS, provides so many rich capabilities it needs a separate Gallery page.
Manifold Viewer is a read-only subset of Manifold Release 9.
Viewer provides phenomenal capability to view and to analyze almost all possible different types of data.
Manifold Viewer is built on the Manifold engine so Viewer retains Manifold parallel CPU speed and Manifold parallel SQL.
Click on thumbnail images to see a larger image. Some browsers may reduce the image to fit your browser window - click into that new window again to expand the image to full resolution.
Image at right: click on the thumbnail for a large image of a Manifold map showing the Mount St. Helens volcano crater in a one meter resolution Digital Elevation Model in ESRI grid format. The synthetic terrain is hill shaded and colored by Manifold, and shown against a web server background of Google terrain, overlaid with labels provided by the Google transparent street web server.
Terrain Elevation Raster from LiDAR Point Cloud
Manifold Release 9 and Manifold Viewer provide high performance raster interopolation from LiDAR point clouds, using a very wide and rich variety of interpolation methods. We created a sub-meter terrain elevation raster for the Colorado River / Maze region of Canyonlands National Park in Utah.
Starting with over 20 LAZ files containing LiDAR data from the Utah state online repository, we used Release 9 to merge the LiDAR point clouds into a single LiDAR vector layer containing over 88 million points. We then used the Interpolate transform template set to Kriging, using a spherical model to create a 5000 x 5000 pixel terrain elevation raster at 80 cm (0.8 meter) resolution per pixel. We then used Style to color the terrain raster by elevation, and to hill shade the raster.
We accomplished all that on a small, inexpensive, 4-core Ryzen 3 machine with no GPU installed. Release 9 is so fast it used all eight hyperthreads to fully load the CPU in a CPU-parallel computation for the Kriging interpolation.
The screenshot above shows only a portion of the resulting Terrain raster, at less than full resolution. Download the entire 5000 x 5000 raster as a georeferenced (complete with .prj sidecar file) .jpeg in the Terrain.zip file.
Elliott Map of the Gettysburg Battlefield
During the American Civil War, the Battle of Gettysburg raged for three days during July 1-3, 1863, resulting in the largest number of casualties of any battle in the war. Illustrations show the region of the battlefield near Round Top and Little Round Top. Click on the thumbnails nearby for full resolution images.
The dead were buried in temporary graves where they fell or nearby, with surveys in the weeks after the battle marking every grave. From November to early 1864 the majority of temporary battlefield graves were relocated to a new Soldiers' National Cemetery.
In 1864, S.G. Elliott created and published a detailed map based on those surveys that shows the original grave sites as well as the battlefield as it was when the battle was fought.
The pattern of graves marked in the Elliott map graphically show those places where fighting was most intense, with the greatest casualties. They also show locations where graves may still exist, since not all of the battlefield graves were located and moved to the National Cemetery.
The Elliott_Gettysburg_Map.mxb (126,475 KB) and Elliott_Gettysburg_Map_Terrain.mxb (558,396 KB) example projects (Caution: big downloads that expand when decompressed) provide a high resolution, georegistered version of the Elliott map.
115 control points were used to georegister the scanned original image into a target map consisting of many reference layers, including a terrain elevation surface created from sub-meter LiDAR data, satellite imagery layers, and contemporary maps showing features and structures still in place from the time of the battle.
Images in order:
- A portion of the original map near Round Top and Little Round Top showing control points. The source image was georegistered from a map with multiple layers, including vector layers showing roads and paths drawn on the Elliott Map.
- The georeferenced result overlaid with partial transparency on a thematically formatted, hill shaded terrain surface. Adding hill shaded relief immediately provides better comprehension of the battle's terrain. A layer with place labels shows famous locations in the battle.
- The georeferenced result with control points turned off, overlaid on grayscale terrain, with georeferenced Elliott map roads layers added. The same tool georeferences both raster and vector layers in Manifold using the same control points.
- The Style pane allows us to "knock out" pixels in between dark marks, to create a version of the georeferenced map that has transparent pixels between dark text and graphics in the Elliot map. The result is shown over thematically formated terrain elevation, with places labeled and roads.
- The transparent pixels styling of the georeferenced Elliott map is particularly useful when combined with modern web served map layers, such as OpenStreetMap in this illustration, or Google Satellite or Bing Satellite layers. When visiting or doing work in battlefield locations there is no need to guess where a grave or other feature shown on the map is located: instead, we can see the location with the Elliott map georegistered in place.
Image Processing using Convolution Matrix Filters
Manifold Release 9 and Manifold Viewer have phenomenal projection capabilities, which allow easy, rotated map displays. This image provides a rotated view of the Eye of the Sahara (Richat Structure) in Africa, rotated 20 degrees so the Eye appears level. The image clearly shows why astronauts call it "the Eye", and how winds flow from right to left of the image, carrying sand from the Sahara desert (the diagonal ripples at right) and how mountains, outcroppings and the plateau around the "eye" divide the wind-blown sands.
This image was created in the free Manifold Viewer, creating a map that shows its contents in Hotine Oblique Mercator (B) projection to do the rotation, with a center lat of 21.569, a center lon of 10.375, center line azimuth of 0 and a Rectified grid angle of 20.
Viewer and Manifold Release 9 let us have as many map windows open as we want, with multiple layers appearing in different windows as many times as we want, and all those different windows can use different projections, with the contents being reprojected on the fly. This image shows the same (but styled differently) 1 GB SRTM terrain elevation data set shown in the examples below, blended using partial opacity with a Bing Satellite layer that is contrast-enhanced on the fly, which is blended in turn with a 1 GB 7x7 matrix profile Curvature surface.
The SRTM and profile Curvature surfaces are in Lat/Lon projection, and the Bing satellite raster is in Pseudo-Mercator. Despite reprojecting on-the-fly 3 GB of data in the three layers into a custom Hotine Oblique Mercator projection to rotate the display, we can see the result in real time and effortlessly pan and zoom with the mouse without lengthy delays.
Be patient when clicking on the images to get a larger image. To see the full-sized image in your browser, once it loads, click on it again to expand to full resolution.
Five Views of the Richat Structure
Once thought to be an impact crater, the Richat Structure in Mauritania, Northwest Africa, is now known to be an eroded geological dome composed of layers of sedimentary rock.
This collection of images shows how Manifold can modify, on the fly, a Bing satellite image served from Microsoft's Bing servers to enhance contrast and then to add enhancement from a 7x7 profile curvature compuation done on Space Shuttle SRTM terrain elevation data. Profile curvature brings out terrace and rims perpendicular to the slope of terrain. The images show enhancement of Bing, plus a blended image adding false color blending to emphasize terrain elevations.
We start with overviews showing Bing and then Bing as enhanced on-the-fly with better contrast and with a 7x7 matrix profile curvature computation. The last two images show addition of blend of synthetic SRTM-derived, hill shaded terrain, first with no profile curvature enhancement and then with profile curvature enhancment.
Click on a thumbnail to see the full image. Click again in your browser to zoom into the full image full size, and then pan around in the image using scroll bars. Open the thumbnails in a new browser tab or window to compare images.
Images in order: The original Bing satellite image, the Bing layer as enhanced on-the-fly for better contrast, improved contrast Bing as enhanced by profile curvature, contrast-enhanced Bing plus SRTM synthetic terrain, and finally, contrast-enhanced Bing plus SRTM terrain plus profile curvature enhancement.
Image Processing using Convolution Matrix Filters
Manifold has extensive image processing capabilities using either built-in convolution matrix filters or filters that can be specified in a fully custom way. Manifold's automatic use of GPU parallel processing for such filters allows application in seconds to remarkably large images. Click on either of the two images at right to see a much larger version of the lower image, which shows a dual-filter Prewitt edge detection filter applied using SQL to the butterfly image above.
A series of user manual topics explain how we can take built-in filters that are point-and-click Transform templates, and have Manifold show us the SQL used for those templates. We can then make simple adaptations to create whatever filters we want, including those that utilize multistep processing or which combine multiple different convolution matrices, like the Prewitt filter result illustrated. The example shown appears in the SQL Example: Process RGB Images using Matrix Filters topic.
Be patient when clicking on the images to get a larger image. To see the full-sized image in your browser, once it loads, click on it again to expand to full resolution.
Find Edges in Aerial Photos
These are big images that can take some time to load. Please be patient. Manifold Release 9 uses massively parallel GPGPU computation to speed up large calculations, such as those used for applying sophisticated convolution matrix analytics to larger images.
The images show aerial photographs in two different settings, one a typical application for urban mapping and the other as might be used to map facilities or resorts. Using the techniques illustrated in the SQL Example: Process RGB Images using Matrix Filters topic, a dual-matrix Prewitt convolution filter is applied to the sample photos to create new rasters where edges have been emphasized.
How such filters work is described in the How Matrix Filters Work topic. In this case, the SQL working behind the scenes applies dual filters to compute better edges, and does so in a few seconds even for enormous images. Those edges can then be used in subsequent processing steps for vectorization of features, object detection and other tasks. Manifold's automatic use of GPU parallelism - even for new, fully custom filters we create - can cut the time required from minutes per image to just few seconds.
Image Processing Examples
Following are some more images, used as illustrations in the SQL Example: Process RGB Images using Matrix Filters topic.
Edge detection output.
Original satellite image.
Edge detection output.
User Manual Illustrations
The clarity, precision, and beauty of Manifold's graphics arts and cartographic capabilities shines in the over 12,500 illustrations in Manifold's user manual.
The illustration seen above was created in Manifold and appears in the Flow Direction and Accumulation topic, to help explain flow direction numbers used to code compass directions.
Manifold labels can be highly sophisticated, including text, bitmap images, and other elements. The illustration seen above was created in Manifold and appears in the Labels topic.
Manifold provides extensive watershed capabilities, enabling computation of upstream and downstream flows. The four illustrations above show different ways to view the results of upstream computations in the Upstream Areas and Lines topic.
Eleven Views of Crater Lake
A collection of images showing how Curvature convolution matrix filters can emphasize features in terrain. We use the same data and techniques illustrated in the Speed Demo with 1280 GPU Cores video. The much larger images here (be patient as the load!) spread across three monitors show detail that the video cannot show. The Crater Lake terrain elevation data set was created from detailed LiDAR scans of the Crater Lake region, plus bathmetry acquired by high resolution sonar below the surface of Crater Lake. Gaps between the terrain surface and bathymetry have been filled with green color. The underwater regions of the original terrain data have numerous vertical and horizontal artifacts due to the scanning technology used to obtain depths in the lake.
We start with overviews that show the original DEM, and then blends using a 3x3 matrix Curvature, Mean transform and a 5x5 matrix Curvature, Profile transform. The first five images are followed by zoomed in views and then detailed views. Click on a thumbnail to see the full image. Click again in your browser to zoom into the full image full size, and then pan around in the image using scroll bars. Open the thumbnails in a new browser tab or window to compare images.
The original DEM surface, hill shaded in false color. The results of a Radius 1, 3x3 matrix filter,Curvature, Mean computation on the original DEM. The original DEM enhanced by blending with the Curvature, Mean computation. Mean curvature emphasizes greater curvature in any direction. The results of a Radius 2, 5x5 matrix filter, Curvature, Profile computation on the original DEM. The original DEM enhanced by blending with the Curvature, Profile computation. Profile curvature emphasizes greater curvature perpendicular to slopes, so it accentuates terraces and rims that cross slopes. Note the more accentuated terraces in the lava flows from Wizard Island and to the left of the rim. A zoomed in view of the original DEM surface. A zoomed in view of the original DEM surface blended with the mean curvature computation. A zoomed in view of the original DEM surface blended with the profile curvature computation. Detail: the original DEM surface. Detail: the original DEM surface blended with the mean curvature computation. Detail: the original DEM surface blended with the profile curvature computation. A close up view shows how changes in curvature perpendicular to the downslope of the crater rim are enhanced.
KML Export - Parcel Lines over Google Earth
Interoperability is easy between Manifold and Google Earth, using Google's KML format.
A parcel file that shows ownership boundary lines was imported into Manifold Release 9 and then exported as a KML from Manifold and draped over terrain displayed by Google Earth. Click the image for a full resolution view.
This example was contributed by user dchall8 on the Georeference forum.
The Craters of Pluto
These are really big images that can take some time to load. Please be patient. Manifold Release 9 has immensely sophisticated terrain elevation processing capabilities, including the use of massively parallel GPGPU computation to speed up large calculations. The images show terrain elevation data for Pluto at 300 meter per pixel resolution, from the New Horizons mission. Using the technique illustrated in the Speed Demo with 1280 GPU Cores video, a hillshaded synthetic, false color terrain view was combined with two lower layers, one of which is a 7x7 convolution matrix Median filter to reduce terrain noise, while the other layer is a 5x5 convolution matrix Profile Curvature filter that brings out edge transitions in crater rims. The topmost image shows the combined image, while the lower image shows the combined effects of the Median and Profile Curvature filters. These are big images, originally spread across three monitors. To view them in full resolution, click the image after it loads and then use the scroll bars in your browser to scroll left and right, and up and down.
At right are two more images. The upper is a combined terrain elevation plus enhancements from the Median and Profile Curvature computations, but colored in more-or-less natural, true, color. The lower image is another false color display that shows terrain elevation combined with a simpler filter, a 5x5 convolution matrix Profile Curvature filter, with no noise reduction from Median processing. Wow! Just imagine... Pluto was once no more than a tiny fuzzy dot in the largest telescopes!
Color Infrared (CIR) Imagery from Four Band NAIP Images
Use the free Manifold viewer to create spectacular color Infrared (CIR) displays from freely downloadable, four-band, 60 cm resolution, National Agricultural Imagery Program (NAIP) images. The example shows suburbs of Redding, California, with channels and display bands rearranged to show the near-Infrared channel as the Red output for a classic CIR display. Viewer and Manifold Release 9 instantly remap bands even of 450 MB images like these. The river at the bottom of the view is the Sacramento river. This is a larger image from the data set used in the Example: Display an NAIP Four Band Image as Color Infrared (CIR) topic.
Merged SRTM Terrain Elevation Tiles
SRTM terrain elevation data acquired by space shuttle missions covers many parts of the world in great detail. Unfortunately, SRTM data is published by USGS as tiles a few megabytes in size covering relatively small areas. Manifold's Merge Images capability, as illustrated in the Example: Merge Images topic, lets us merge dozens or hundreds of tiles to create a single, seamless terrain elevation raster image.
The first thumbnail above shows a Manifold map that spreads over three monitors, showing the Italian alps overlaid with a Google transparent streets layer to provide context. Missing pixels where SRTM data has gaps in the coverage are drawn as transparent pixels, allowing a background Google satellite image layer to show through.
The much larger thumbnail shows a very large Manifold map covering more of the Alps, as we might see if we were using a display with several ultra-high resolution, large monitors. To keep image downloads smaller, a JPEG with some compression has been used: the actual Manifold screen is sharper.
National Map Elevation from a WMS Server
These are really big images that can take some time to load. Please be patient. Manifold Release 9 can automatically fetch terrain elevation data from a WMS server and then Style that data with a fully custom look. The illustrations show a map with a terrain elevation layer from the National Map that Release 9 has fetched from US Government servers, together with layers providing the locations of cities, state boundaries, and with labels layers providing state names and city names. The two big images show the incredible detail of the elevation data, allowing a view of the Appalachian mountain ridges in Pennsylvania like you've never seen them before. These are big images, originally spread across three monitors. To view them in full resolution, click the image after it loads and then use the scroll bars in your browser to scroll left and right, and up and down.
The smaller image shows the region surrounding Massachusetts. It includes a background layer that uses Bing. In all three examples, the terrain elevation data streamed in from the WMS server has been thematically formatted and hillshaded on the fly. The data value used for the ocean has been rendered in transparent color, so any layers below (like Bing in the smaller image example) will show through.
LiDAR Relief / Google Hybrid
Contributed by forum user dchall8, the shaded relief image was created in Manifold Release 9 using 1 meter LiDAR terrain elevation DEMS obtained in a 2015 flyover. The Merge Images dialog was used to combine 256 separate LiDAR data sets into a single, seamless LiDAR DEM in only 30 seconds. The seamless LiDAR DEM could then be styled, hill shaded, and combined using transparency with a Google satellite layer.
"The results, especially with hill shading, are spectacular. Otherwise flat looking Google Earth imagery comes alive. See attached pics. I am seeing features I never noticed because now they are geographically set off from the flatness. I can use the telephone poles in Google Earth images like a sundial to tune up the azimuth and altitude of the shading to match Google's shadows. This is really cool." - Forum post with the above images.
Switzerland and the Alps in Antique Style
This antique Swiss style of cartography utilizes Imhof effects, where a layer of sfumato haze at lower altitudes reduces detail so that higher altitude relief becomes more prominent. The map uses partially transparent layers with a base of satellite photographic imagery, palette-colored and hill-shaded GTOPO30 terrain elevation data, multiple sfumato haze layers using a mix of interpolated transparency and colors, and then a variety of area border layers, vector "knock-out" layers to emphasize Switzerland, and labels. Country borders and names are taken from the US Department of State GIS data web site. Download a similar scene as a PDF that was "printed" using the FreePDF driver from a Manifold layout.
Contours for the Entire World
Manifold Release 9 takes just a few seconds to create vector contours from huge raster files, like these examples showing contours for Space Shuttle SRTM elevation data for the entire world. 9 can create contours as area objects at given interval ranges or as contour line objects. The close up shows the lowest elevation on Earth, a trench in the Pacific ocean. The larger view shows the entire Earth with the contour area for the continental shelf colored in a light blue color. The map windows are huge, extending over three monitors, yet Manifold pans and zooms them and redisplays instantly.
Contours for Mount Saint Helens
Release 9 automatically handles variations in raster data, such as extensive use of invisible pixels or NULLs in the data. The illustration shows contour areas thematically colored that were created from a terrain elevation data set of the cataclysmic results of the Mount St. Helens eruption. The contour areas are overlaid on a Google terrain map served from a web server, and overlaid on top of the contours is a transparent street layer, also from a Google server. Manifold makes it easy to create such stunning compositions in just a few minutes.
WMTS Server Combined with ImageServer
The image shows a map created from an experimental ESRI WMTS server providing world topographic data hillshaded from multiple angles, overlaid with a Google image server layer providing streets and labels with transparency between to allow the topographic WMTS layer to shine through. Manifold can re-project on the fly faster than web servers can serve tiles, to enable effortless use of data from different sources that use different web serving techologies together in the same map, even if coordinate systems require re-projection on the fly. The large images show the scene expanded over three monitors. Manifold is so fast that it is effortless to work with such immense seas of pixels even as layers are being re-projected on the fly.
OSM Buildings on Two Different Backgrounds
The images show an extraction from OpenStreetMap buildings for Monaco, overlaid on a Bing Streets layer and also on a Canvas Dark theme ESRI ArcGIS REST web server. Changing to a different background map creates a completely different feel to exactly the same data. Manifold has colored the building polygons on the fly using the Color Brewer Spectral palette for a more visually interesting display.
LiDAR Data as a Shaded Surface
The image shows a LiDAR scan of the Pentagon in the US. 8 million points of LiDAR data was imported into Manifold using the built-in LAS/LAZ importer and then Manifold's Kriging interpolation template was used to create a surface. The Style panel applied a range of colors and shading to the resultant surface. Everything is automatically georegistered so the LiDAR data appears in correct georegistration over a background layer provided by Bing Streets.
Uber Movement Drive Times in Boston
The image shows a map created from Uber Movement data downloaded as a .csv file. Manifold on the fly transforms text coordinates in the .csv into geometry for display. The polygons show drive time from downtown Boston colored by the time required. The Latitude / Longitude Uber data is re-projected on the fly by Manifold to match the Pseudo-Mercator coordinate system used by the Google background layer and the Google Transparent Streets layer used as the uppermost layer in the map. See the Uber Movement YouTube Video that shows the process.
GPKG File Database - All Real Estate Parcels in New Zealand
GPKG is an open source spatial format that utilizes SQLite / Spatialite files to store spatial data, either rasters or vectors. Land Information New Zealand (LINZ), a government organization, publishes numerous free data sets for New Zealand in GPKG format as well as other formats. This image shows real estate parcels in a section of Auckland, New Zealand, a small part of the nearly 1.4 GB vector data set in GPKG format that shows all real estate parcels in New Zealand. The information box open at left shows attribute data for the parcel nearby that was alt-clicked. Every parcel in the view and in all of New Zealand contains such info. Manifold has colored the parcels on the fly and has re-projected the parcels layer from the New Zeland local coordinate system into the same Pseudo Mercator projection used by the Google background layer in the map.
The image at left shows the example vector drawing from the geopackage.org samples collection that shows a vector drawing of vegetation counts in Washington, DC. It is colored on the fly by Manifold and displayed together with a Google Satellite layer for background, being re-projected on the fly into the same pseudo-Mercator coordinate system used by Google.
Manifold products using Manifold technology can read virtually any image format known, including image formats used in graphics arts as well as for GIS or stpatial data.
Manifold can read hundreds of different formats and subformats, automatically applying transparency and multiple channel image data. Click on the thumbnailto see the sample image on a Manifold Future desktop with background white layer on and off to show transparency.
Web Servers using ArcGIS REST Servers
ESRI World Street Map illustrated, with tiles automatically downloaded into the Manifold layer.
Web Servers using OpenGIS Web Map Tile Service (WMTS) servers.
The illustration shows a connection to the ESRI World Imagery Service using WMTS, with an OpenStreetMap layer drawn over the map. ESRI allows use of the World Imagery Service to create vector data for contribution to OSM by tracing over their imagery and using it to guide edits. Cool!
Bing Maps showing satellite imagery.
The image shows a Bing satellite layer used as a background for a map layer showing streets in Palo Alto and Menlo Park taken from an SDTS file. The white horizontal line in the middle left of the display is the Stanford Linear Accelerator.
Bing Maps showing streets and features. See worldwide streets automatically pulled down as a layer from Bing servers.
Google Maps Satellite Imagery
Click on the image at right to open up a full size view of a typical Google satellite high resolution display seen when zooming in to the famous chateau at Chambord, in the valley of the Loire in France.
The other thumbnails show the same view as the street map scene of Munich shown below by the Google street map image server plus a zoomed in view. The satellite imagery and street map imagery layers in a map automatically align with each other and with other imageserver layers.
Google Maps Street Map
Displayed for the entire world from your Manifold console, Google Maps showsstreets and features, automatically pulling down tiles.
Google Maps Streets (Transparent)
Google Maps showing streets with transparency between, to allow lower layers to show through. A transparent Google street maps layer appears in the accompanying image, showing USGS LULC 100K data imported from CTG raster format for the San Franciso Bay region, colored on the fly by Manifold and overlaid in a map on a Google Satellite layer for context, with a Google transparent streets layer above for labels.
Google Maps Terrain
The images show a Google Maps Terrain layer as a background to the synthetic, hill shaded terrain created by Manifold from an SDTS DEM.
OpenStreet Maps OpenCycleMap Transport
OpenStreet Maps OpenCycleMap with public transportation added, automatically produced as a layer by Manifold with no user formatting required.
ESRI File Geodatabase
ESRI File Geodatabases using ESRI GDB files for storage. The images show ESRI's "Naperville" tutorial example GDB data set with tax parcels layer and pipes layer (in green), overlaid on a Bing streetmap web server layer for context. The larger image is zoomed in to show detail on parcels, which have been colored on the fly by Manifold to better distinguish different classes of tax parcels. The larger image spreads across multiple monitors. All data resides within the ESRI geodatabase.
Hill shaded terrain with Web Server Contrast Adjustment
Two images of the Lake Wales Ridge region of Florida using terrain elevation raster data. Manifold creates hill shaded terrain and colors it using a specified palette, maintaining perfect georegistration. One view shows the hill shaded data overlaid on Bing web server satellite photography. The other screenshot shows the Manifold-generated view overlaid on Google web-served terrain, with Google web-served transparent streets and street labels as the uppermost layer. Manifold has been used to adjust contrast on the fly in the Google transparent streets layer, to provide better contrast and readability against the colored hill shaded terrain. The upper layer is shown using 90% opacity as well for a better blending effect. Manifold Release 9 not only uses an incredible range of web servers, but in addition has the unique capability to transform web-served layers on the fly to change display characteristics such as contrast and color channels. Super!>
Hill Shaded Terrain using a Custom Palette
A full resolution image that fits on a single monitor, showing how different, contrasting colors used in adjacent terrain elevation intervals can bring out detail that would be difficult to see without a custom palette and hill shading. The ethereal visual effects are also super.
S57 ENC (Electronic Navigation Chart) vector format.
S57 charts are used worldwide for ship navigation. The display shows a tiny fraction of the infromation in a typical chart, formatted as lines on a black background. Endless variations in appearance are possible with Manifold.
Sythetic terrain formed from ESRI ADF format data
ESRI .adf format, also sometimes called ArcGrid format. ADF files can contain either raster or vector data.
The first thumbnail expands to a view of the Yosemite National Park region, a terrain elevation data set colored on the fly by Manifold with a quantized palette showing elevations.
The second thumbnail shows a zoomed-in view to the Yosemite Valley and Yosemite Village area famous to millions of tourists and uses a smooth interpolation palette to show terrain elevations. Larger thumbnails expand to multi-monitor sized images using either a more natural palette or a false color palette to show elevations.
Land Use in India from ESRI ADF format data
Thumbnails show raster data imported from a ESRI .adf format file. The thumbnail showing an overall scene shows the GLC2000 land use data set for India, a raster data set color-coded by land use type. It imports with perfect georegistration as seen in the overall image, which uses an NOAA world shaded relief WMS layer for background and a Google transparent streets layer on top for context. The detailed image thumbnail expands to show use of ESRI ArcGIS REST server layers, with a hill shading layer at 50% opacity blended with the land use data to provide relief and a boundaries and place name layer above for context. The GLC2000 data set shows land use at 1 km per pixel resolution on the Equator. Manifold is automatically re-projecting the Latitude / Longitude GLC2000 layer on the fly into Pseudo-Mercator projection as used by the map and the web server layers.
Vegetation classification from a BIL file
ESRI Band Interleaved by Line (BIL) format. The image shows a BIL file that codes different types of Alaskan vegetation zones as raster color values, using a false color palette colored on the fly by Manifold and displayed in a map with a Bing layer for background and context.
District of Columbia from Census Bureau BWx data
BWx is a legacy US Census Bureau TIGER file format. The image shows 1995 Census data for the District of Columbia, colored on the fly with the Style dialog.
Raster land use and land cover data overlaid with web server layers
USGS LULC (Land Use Land Cover) data in CTG (Composite Theme Grid) raster format, also known as Grid Cell files. The illustration shows USGS LULC 100K data imported from CTG raster format for the San Franciso Bay region, colored on the fly by Manifold and overlaid in a map on a Google Satellite layer for context, with a Google transparent streets layer above for labels.
Simple presentation of USGS roads data from SDTS files
US government SDTS (Spatial Data Transfer Standard) files ending in DDF extension. SDTS format stores rasters (terrain elevation data such as DEM, or images) and vectors (such as data sets converted from USGS DLG). The upper image shows the Palo Alto roads data set from the USGS 1:24K series, with road lines extracted, colored on the fly in orange-yellow and shown overlaid in a map on a Bing satellite layer. The Stanford Linear Accelerator is the white horizontal line a middle left.
The lower images show terrain elevation data from USGS published in SDTS raster format, with a Google terrain background and Google streets overlaid. The terrain has been colored and hill shaded on the fly by Manifold, using a more natural palette for one image and a false color palette for greater contrast in the other image.
Raster images from Log ASCII Standard raster data
DDR files as used with Log ASCII Standard (LAS) format used for well logging data. This is a legacy format different from the LAS format used for LiDAR data.
Images show DDR LAS data providing Channel 1 of a five-band AVHRR instrument flown on an earth observation satellite, overlaid on a base map with transparent labels above for reference. Manifold automatically re-projects layers in a map to conform to the map.
Terrain Elevation for the Entire World
The USGS GTOPO30 project created a worldwide terrain elevation data set at approximately 1 km resolution. Manifold Release 9 can display the complete GTOPO30 data set for the entire world at once, panning and zooming instantly. Thumbnails above show a three-monitor wide display zoomed in to show terrain elevation from Spain to the Caspian Sea, with one image showing use of Google transparent labels and the other without Google labels. The terrain has been colored using Style in discrete intervalus and hill shading has been applied. Click on the thumbnail for a full size image (be patient - these are big images) and then click again in the image that opens to zoom in to full resolution, using scroll bars in the browser to view the image if you do not have three monitors.
Drawing created from Intergraph DGN format, a CAD-oriented format
A vast amount of the world's data is in CAD formats that require massaging for use in spatial settings. Manifold has all the tools required to exploit that data.
Photographic rendering from Adobe DNG file format
DNG is an Adobe variation of TIFF used in digital photography. The images show a market in Salzburg, Austria, including a zoomed in view at native resolution, one image pixel per screen pixel. (Yes, the photographer did snap the photo off horizontal.)
Manifold's ability to handle vast file sizes makes Manifold projects a great way to store large images that may be hundreds of gigabytes in size.
Geologic map of Arizona from ESRI E00 Data
The image shows a geologic map of Arizona, overlaid on a background map generated by a web server. Manifold has colored the different geologic areas on the fly and has automatically re-projected the Lambert Conformal Conic projection used by the .e00 into the Pseudo Mercator web projection used by the background Bing web server.
1.3 Billion Pixel image of Ithaca, New York from ECW Data
The screenshot shows a 1.32 billion pixel image of Cornell University in Ithaca, Nw York, imported from an ECW file and overlaid in a map on a Google streets layer with an upper layer of labels marking points of interest in the ECW image. The 1.32 billion pixel image is in Transverse Mercator projection and is being re-projected on the fly into the Pseudo-Mercator projection used by Google and the map. Manifold re-projects the image so fast for display that pans and zooms in the map remain instantaneous with zero delay.
Vector land use and land cover data overlaid with web server layers
A collection of screenshots showing USGS LULC (Land Use Land Cover) data in GIRAS (Geographic Information Retrieval and Analysis System) vector format.
The illustrations show USGS LULC 100K data imported from GIRAS vector format for the San Franciso Bay region, colored on the fly by Manifold and overlaid in a map on a Bing streets layer for context. Other images show the vector data set with water areas removed, overlaid on Google satellite imagery, plus close up with transparent street layers.
Seamless mosaic of Niagara Falls / Horseshoe Falls
The image shows a view of the Horseshoe Falls at Niagara Falls on the border between Canada and the United States. Spread over multiple monitors, the image is a Manifold map that displays images from six, separate, linked JPEG 2000 images seamlessly joined (an edge line runs down the middle of the falls and is completely invisible due to the perfection of the join). The display shows a small part of an immense image that Manifold technology can pan and zoom instantaneously.
Discovering structures beneath a tree canopy using LiDAR
The images show the discovery of a cabin hidden under the trees by using LiDAR data to hunt for returns from objects below the tree canopy.
"THIS IS SO COOL. [...] With Manifold Release 9 I will finally be able do what I need to do with the LiDAR data I have been sitting on for 2 years. See attached pix." - Forum post with the above images.
Parcels in Riddells Creek, Australia
A map created from data in MapInfo mid/mif format, an interchange format for vectors. The illustration shows parcels in Riddells Creek, Australia.
Aerial image of Exeter, England, overlaid on a Google layer
Using data published in LizardTech MrSID raster format, the image shows a UK Ordnance Survey sample image showing Exeter, England, in a map that uses a Google Streets image server background layer with a layer of transparent streets and labels above the image imported from MrSID format. Manifold re-projects on the fly the image brought in from MrSid from the original British National Grid projection to the Pseudo-Mercator projection used by the map and by Google layers. Note the perfect alignment between the image and Google layers.
OpenStreetMap Native Data for Boston
Manifold directly imports OpenStreetMap (OSM) native Protocol Binary Format (PBF) for total, native access to every detail of OSM data using OSM's recommended format. The illustrations show OSM data for Boston, including line and area layers extracted with Manifold, a process that takes two clicks and a few seconds given direct import of native OSM data using the PBF dataport. The brighter image shows new styling that allows intricate overpasses and underpasses with lines. The darker image shows a typical desktop and project. Manifold also reads OpenStreetMap OSM and O5M formats. See also the PBF .pbf, OSM, O5M topic.
Parcels in Riddells Creek, Australia, using TAB format data
Manifold can consume and style data from many formats. MapInfo table format consists of multiple files, usually five files, referenced by a controlling .tab file and can contain vector, raster or tabular data. Manifold automatically adapts to the type of data the file contains. The first illustration shows parcels in Riddells Creek, Australia, imported from a vector TAB file and styled by Manifold using a very appealing Color Brewer palette.
The larger image shows a raster image imported from TAB storage that is a scan of a paper map. The screenshot shows a Manifold image that spans three monitors.
DigitalGlobe Imagery of Madrid Airport Suburb
Manifold reads virtually all known variations of TIF/TIFF and will extract projection information from GeoTIFF or older TIF files accompanied by TFW "world" files. Manifold automatically writes projection information when exporting to TIFF to create GeoTIFF files. The attached images show the DigitalGlobe GeoTIFF sample image for a suburb near Madrid airport in 30cm resolution. Images are seen in a map with a Google layer below and a transparent Google Streets layer above to provide context and to illustrate the perfect georegistration Manifold attains. Seen at less than full resolution spread over multiple monitors this view is a fraction of the full 1 GB+ image that pans and zooms instantly in Manifold, even while being re-projected on the fly into Google Pseudo-Mercator projection.
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