Our current standard deliverables include a 2D CAD drawing that vectorizes all visible objects within the defined CAD boundary, an elevation point grid, and surface contours created from the ground surface model. We also provide a comparative model showing the variance between true north and grid north. Additionally, the deliverables include an orthomosaic image, a detailed digital surface model (DSM).
The deliverables are provided in the following formats:
- 2D CAD drawings: DWG and PDF
- Orthomosaic image: JPEG and TIFF
- Digital Surface Model (DSM): JPEG and TIFF
- Digital Terrain Model (DTM): TIFF
- TIN Surface: LandXML
The orthomosaic and DSM are displayed within the SolarGain portal as interactive layers, allowing users to view and analyze them directly within the platform to aid visualization and project management.
What is a DSM and how do you process it?
A Digital Surface Model (DSM) represents the earth's surface, including structures like buildings and vegetation. Using photogrammetry in Agisoft Metashape, we process a DSM by capturing aerial images, aligning them, and generating a dense point cloud. The DSM is built directly from this dense point cloud by assigning elevation values to each point based on its vertical position and rasterizing the data to create a continuous surface. Alongside the DSM, we generate an orthomosaic image. Both the DSM and Orthomosaic are exported in JPEG and TIFF format and displayed interactively in the SolarGain portal for easy analysis and visualization.
What is a DTM and how do you process it?
A Digital Terrain Model (DTM) represents the bare earth surface, excluding objects like buildings and vegetation. We generate the DTM by processing aerial images in photogrammetry software to create a dense point cloud. Ground points are extracted by filtering out above-ground features, and these points are used to construct a Triangulated Irregular Network (TIN) surface. The TIN is then converted into a TIFF file, assigning elevation values to create a continuous raster representation of the terrain.
What is a Orthomosaic and how do you process it?
An orthomosaic image is a high-resolution aerial photo that has been geometrically corrected to remove distortions caused by terrain relief and camera angles, providing an accurate representation of the earth’s surface. Using specialised photogrammetry software, we process the orthomosaic by capturing aerial images, aligning them, and generating a dense point cloud. The orthomosaic is created by stitching together these aligned images, ensuring that the final product is seamless and accurate. This orthomosaic serves as a crucial reference for analysing land use, vegetation, and other surface features. Additionally, it complements the Digital Terrain Model (DTM), allowing for better visualization and understanding of the terrain context. Both the Orthomosaic and DTM are exported in TIFF format and displayed interactively in the SolarGain portal for easy analysis and visualization.
How do you create your Grid Points and Contour Lines?
In our standard deliverables, we define a regular grid over the DTM area at 5 x 5 meters (we can represent them down to 1x1 meter grid, if required) and extract elevation values at each grid point, resulting in a structured dataset that accurately represents terrain elevation. The contour lines exported at 0.25m or 0.5m at a 1 meter subsample are also derived from the DTM indicating areas of equal elevation and visualizes the terrain's shape and slope. Both the grid points and contour lines are included in the DWG provided
What is a Grid Point and what does it represent?
A grid point is a specific point within a point grid that represents an elevation value at a precise location on the grid. It is used in terrain modeling to show the height or elevation of the surface at that particular spot, helping to create a digital representation of the land.
What is a Contour Line and what does it represent?
A contour line connects points of equal elevation on a map, representing the terrain's shape and slope. Closely spaced lines indicate steep slopes, while widely spaced lines show gentle slopes or flat areas. Contour lines never cross, as each line represents a single elevation level. They help visualize the three-dimensional shape of the land on a two-dimensional map.
Any differences on your internal process when combining it with LiDAR?
When combining our processes with LiDAR data, the internal workflow remains largely the same; however, the integration enhances the accuracy of the terrain model. LiDAR provides precise elevation data that can be used to create a Triangulated Irregular Network (TIN) surface, which captures the terrain's complexities more effectively than standard photogrammetry alone. This allows us to produce a more detailed and accurate Digital Terrain Model (DTM). While the fundamental steps of defining grid points, generating contour lines, and exporting to 2D DWG files remain unchanged, the incorporation of LiDAR data significantly improves the overall quality and reliability of the terrain representation.
What is a TIN surface? and can you provide them?
Yes, we are capable of delivering Triangulated Irregular Network (TIN) surfaces. They are generated using the Digital Surface Model (DSM) to create the Digital Terrain Model (DTM). TIN surfaces provide a detailed representation of the terrain by connecting elevation points with triangular facets, allowing for a more accurate depiction of complex terrain features. The TIN surfaces can be included in the 2D DWG files and delivered as a land XML.
What is the difference between Drone Photogrammetry DTM TIN surface compared to Drone LIDAR DTM TIN surface?
The difference between creating a TIN surface from drone LiDAR versus drone photogrammetry lies in how each technology captures and processes terrain data:
Drone LiDAR:
- Point Capture: LiDAR actively emits laser pulses that can penetrate through vegetation and capture the ground surface directly, even in forested areas.
- TIN Surface: A TIN surface generated from LiDAR data is typically more accurate in areas with complex terrain or vegetation. It offers better ground-level precision, especially in heavily vegetated or hard-to-reach areas where photogrammetry might struggle to capture the ground.
- Use Case: Best for areas with heavy vegetation, dense forests, or complex terrain where getting a clear ground reading is difficult.
Drone Photogrammetry:
- Point Capture: Photogrammetry uses overlapping aerial images to create 3D models by stitching and calculating surface points from image pixels. It captures the top of all surfaces, including vegetation, buildings, and ground.
- TIN Surface: A TIN surface created from photogrammetry data is highly accurate in bare earth areas, where there is little to no vegetation. However, in forested or vegetated areas, photogrammetry captures the tops of trees and foliage, making it less effective for ground modelling.
- Use Case: Best for open or bare earth areas with minimal vegetation, where high-resolution imagery is sufficient to model the terrain accurately.
Conclusion:
- LiDAR: Provides better ground accuracy in vegetated or forested areas.
- Photogrammetry: Performs well in open or bare earth areas but is less effective at penetrating vegetation. Both methods can produce TIN surfaces, but the choice depends on the terrain type and project requirements.
What features are you able to represent in your Linework?
In our linework, we are able to represent any features visible from the drone that fall within the Above CAD boundary we offset from the client's area of interest. This includes, but is not limited to:
- Building footprints: Outlines of structures and buildings.
- Roads and pathways: Major roads, tracks, bridleways and pathways.
- Vegetation: Tree canopies, hedgerows, and other types of low, medium or high vegetation.
- Fences and walls: Boundary markers and retaining walls.
- Water bodies: Rivers, lakes, ponds, and other water features.
- Topographic features: Hills, valleys, and other significant terrain changes.
- Infrastructure: Utility poles, transmission lines, and other visible infrastructure.
Are you able to provide the height of all objects/obstacles including but not limited to trees, other vegetation, overhead lines and towers, poles, buildings/dwellings, ruins, walls?
Above can provide the height for larger hard features such as building or warehouse roofs and uncovered walls within absolute accuracy of <9cm accuracy, however smaller or more complex features such as poles, pylons we can guarantee accuracy of 0.5 meters.
Are you able to provide the exact height and location of electric and communication lines?
Above can provide the location of the base of an electrical or communication line as well as the height within an accuracy of 0.5 meters.
What is your guaranteed accuracy?
Our guaranteed accuracy in bare earth areas is 6 cm horizontally (X and Y) and 9 cm vertically (Z). In vegetated areas, accuracy may be lower. However, we often achieve accuracy well within these limits, depending on factors like project area, vegetation coverage, conditions, and equipment used.
What impacts can vegetation have in the accuracy of the deliverables?
Vegetation can impact the accuracy of deliverables because drone photogrammetry captures the top of the vegetation, making it difficult to reconstruct the ground beneath unless bare earth is visible. In heavily vegetated areas, using drone LiDAR provides better results as it can penetrate the vegetation and more accurately map the ground surface.
Can you remove all types of vegetation when recreating a DTM?
When creating a DTM, we can remove most vegetation if it is not too dense. However, if the vegetation is dense, we can't fully remove it unless we use drone LiDAR, which is more effective in penetrating dense vegetation and capturing the ground surface.
Can you deliver a point cloud file?
Yes. We can deliver it in LAS or LAZ file formats.
What classification do you apply to your point cloud?
We typically apply filtering to classify point clouds and create a DTM raster. However, we’ve recently added manual post-processing methods for DSM and orthomosaics to create more accurate TIN surfaces which is used to create the DTM. This combination improves the quality of the final terrain models.
How can I take into account the Grid Convergence Angle when designing my Plant Layout?
When designing a solar plant, accounting for the Grid Convergence Angle is important to ensure your array is properly oriented to maximize solar efficiency. Here's how to take it into account:
- Understand the Grid Convergence Angle: This is the angular difference between true north (geographic north) and grid north (north direction in your local map projection). In solar design, panels are typically aligned to true north for optimal sunlight exposure, making it crucial to correct for this angle if your design uses grid north.
- Determine the Angle: Obtain the grid convergence angle from the DWG. Provided by Above. This value will help adjust the orientation of your solar plant design.
- Correct the Solar Panel Layout: When laying out your solar panels, if the design is initially aligned to grid north, rotate the array only by the grid convergence angle to ensure that the panels are aligned with true north. This step is essential for maximizing solar efficiency, as even slight deviations in orientation can impact energy output.
- Software Adjustments: Most solar design software allows you to input the grid convergence angle directly, ensuring that the plant layout is properly aligned to true north. Ensure this setting is correctly applied to avoid misalignment in the field.
- Final Integration in CAD/GIS: After applying the angle correction, confirm that your CAD or GIS files reflect the proper alignment, with the solar array oriented correctly relative to true north.
By incorporating the grid convergence angle into your solar plant design, you optimize the alignment of your panels for the best possible energy yield and ensure your layout is accurate for both design and construction.