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A three-dimensional represantation of the relief interpolated from soil class points of the classified LiDAR point cloud. It is available in raster format according to the customer needs.
A three-dimensional representation of the absolute height of vegetation and the built environment interpolated from real surface points of the classified LiDAR point cloud. It is available in raster format according to customer needs.
A three-dimensional representation of the relative height of vegetation and built environment from the ground level calculated by the difference between the digital surface model and the terrain model. It is available in raster format according to customer needs.
Vector dataset generated from the (LiDAR) terrain model that links the surface elevation points of the same height.
Overview raster format map layers that represent terrain, land cover categories, contour lines together. Unlike traditional topographic maps, it is generated from up-to-date, high-resolution aerial remotely sensed data.
A vector grid model with lower spatial resolution (5,10,20 or 50 m) generated by the (LiDAR) terrain model, representing the elevation values of the terrain.
Vector polyline elements and modeled poles mounted on high-voltage wires derived from high-density LiDAR point clouds. For planning and monitoring powerline networks.
The images are recordeed in Nadir and contain RGB (Visible Color Range) bands. The central parts of the images are selected for mosaicing. Photogrammetrically processed images are orthorectified and georectified. The planimetric accuracy is the same as the GSD. The mosaicing method ensures the best color matching between images and produces a gap-free mosaic.
CIR orthophotos are recorded using infrared, red, and green (IR, R, G) bands, resulting false color images. The central sections of the images are selected for mosaicing. Photogrammetrically processed images are orthorectified and georectified. The planimetric accuracy is the same as the GSD. The mosaicing method ensures the best color matching between images and produces a gap-free mosaic.
The orthophoto consists of 4 channels: R, G, B, NIR. The central sections of the images are selected for mosaicing. Photogrammetrically processed images are orthorectified and georectified. The planimetric accuracy is the same as the GSD. The mosaicing method ensures the best color matching between images and produces a gap-free mosaic.
This product is intended to provide a complete overview of the street surface, suitable for displaying the entire surface of those streets, where the surface can be perfectly identified. The seamlines are hand-edited, whereby the buildings are depicted to their base, thus distorting the shape of the buildings ('folding them out'). Integrity of buildings are not considered in the generated product as the facades of the buildings are tilted.
This product is based on oblique data acquisition. The survey is carried out using a camera system consisting of several RGB cameras intalled in 45°. "Dense matching" is used to extract point clouds from overlapping images. The point cloud can be used to create a 3D irregular triangle network (3D mesh). The 3D model can be textured with oblique images for realistic rendering.
This product consists of raw tiff images, external orientation after aerial triangulation, and a triangulation report.
The purpose of the crop map is to create a map layer that shows the location of arable crops in the current year at national level. The map layer can be created at multiple times according to the development conditions of the culture.
A series of maps for crop growth monitoring. A series of colored composites and biomass intensity maps that cover the vegetation period of the given plant.
A map layer to identify changes in condition of a plant, presenting changes over a selected period. It is capable to identify relevant changes that have occurred in the specified area for a specific reason, location, or time period.
The vegetation index map provides information on the current state of vegetation. Different vegetation indices examine the condition of vegetation from different perspectives (eg. general condition of biomass, plant diseases, etc.).
Various targeted vegetation indices are evaluated and weather conditions and annual reference yields are analysed to estimate the annual yield. The target plot is depicted and analyzed by the used descriptive data, and based on these the annual yield is estimated.
The yield map represents the yield data calculated using on-field yield measurements. The sampling procedure and the production of the yield map is carried out based on standard protocols for each particular crop.
A map layer to spatially identify the effects of a severe weather or other (harmful) event on a given parcel, or to accurately represent the spatial extent of a biomass loss.
The management zone helps to delimit the areas requiring different treatment to result optimum yield. The map layer considers time-varying development of the target plot and plot-heterogeneity, thus underdeveloped areas can be identified.
Targeted spectral indices and other spectral information to identify water surfaces can assist the effective classication of contiguous water surfaces, thereby identifying, for example, inland water areas of agricultural lands. Accordingly, detected water spots can also be represented on maps.
A surface model derived from the LiDAR data points
The difference between the surface (DSM) and the highest point of the canopy
Determination of undergrowth below canopy from LiDAR data.
Relief inclination in degrees
Surface containing aspect values that is scaled clockwise and begin with a value of N (0-359)
Orthophoto in visible spectral region in EOV or UTM projection system
False color orthophoto containing near infrared channel, in EOV or UTM projection system
Suitable for tree species classification of the upper canopy level, includes tree species within each canopy border.
Vector polyline elements and modeled poles fitted on high-voltage wires derived from high-density LiDAR point clouds. For planning and monitoring power line networks.
Identification of openings surrounding power lines
Vector dataset generated from the (LiDAR) terrain model that links the surface elevation points of the same height.
Determination of the position of each tree based on the canopy model and log detection
Canopy closure of forest subcompartments, calculated from canopy models and available on forest subcompartment boundaries
A three-dimensional represantation of the relief of urban areas interpolated from soil class points of the classified LiDAR point cloud. It is available in raster format according to the customer needs.
A three-dimensional representation of the absolute height of vegetation and the buildings in urban environment interpolated from real surface points of the classified LiDAR point cloud. It is available in raster format according to customer needs.
A three-dimensional representation of the relative height of vegetation and built objects from the ground level calculated by the difference between the digital surface model and the terrain model. It is available in raster format according to customer needs.
A vector grid model with lower spatial resolution (5,10,20 or 50 m) generated by the (LiDAR) terrain model, representing the elevation values of the terrain.
A vector or raster image representing vegetation-covered surfaces of urban areas, based on high-resolution aerial remote sensing data.
Aerial hyperspectral and LiDAR data generated urban asbestos roof database as a result of complex image processing.
3D roof structure models with LOD2 detail containing geometric information (extent, orientation, slope).
CityGML compatible building models with LOD2 detail based on aerial LiDAR data and orthophotos. The building models are represented by detailed roof structures.
Photorealistic 3D textured mesh data from oblique and nadir aerial photographs, produced by photogrammetry. In urban areas, the facades of buildings are also well represented.
Polygon vector data derived from high resolution aerial imagery and LiDAR data that contains the outlines of buildings that can be identified in the area. Comparable with property register data.
Vector data containing surface coverage categories generated by object-based image classification based on aerial LiDAR and multispectral aerial images. Typical Classes: asphalt surfaces, paving surfaces, lawn, bushy area, woody vegetation, water surfaces, other coverings.
Raster data derived from an urban surface model representing the amount of incoming global radiation on the given surfaces and objects.
Vector data derived from an urban terrain model that identifies areas that are loaded by rainfall and poor drainage based on land cover data and slope conditions.
A vector database made from aerial LiDAR data. Each tree unit is represented by dots, and the height and canopy diameter of the individuals are belong to points as an attribute. In the case of hyperspectral data, the species of each individual tree can also be defined and assigned to points as attributes.
A covered surface model containing the relative height of the objects and intersections or polygon of a plane with a client-deifned height. Map of objects above a given terrain.
Vector polyline elements and modeled columns mounted on high-voltage wires selected from high point density LiDAR point clouds. For planning and monitoring power line networks in built-in areas.
Cross and longitudinal sections formed from LiDAR point cloud from points belong to soil, drainage ditches and canals.
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