Drone Mapping & Photogrammetry - Calculating GSD & Image Overlap Distance

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At Carrot Drone Services, we understand the complexities involved in drone mapping and photogrammetry, especially when it comes to achieving the perfect overlap between images for seamless stitching. With our GSD and Overlap Calculator, we aim to simplify this process, providing a tool that intelligently calculates the necessary distances between shots to ensure optimal overlap and Ground Sample Distance (GSD). This is particularly useful for applications like vertical façade mapping.

Traditional mapping software caters to enterprise drones by offering overlap settings and GSD calculations directly. However, for those utilizing the Fly Litchi app and similar software, which only allows the setting of distance or time between images, our calculator proves invaluable. It opens up new possibilities by enabling the use of a wider range of drones, including smaller models which might be restricted by weight or size in certain projects. For detailed insights into the significance of image overlap in drone mapping, visit our article on Drone Construction Mapping.

How can one calculate the exact distance—and, considering the drone's speed, the time—between images to achieve the required overlap?

The process of calculating the perfect distance and time between drone images for mapping projects involves complex mathematical formulas. However, our aim is to demystify this process through our detailed explanation and user-friendly GSD and Overlap Calculator. This tool is designed to take the guesswork out of achieving the optimal overlap and Ground Sample Distance (GSD) for your drone mapping projects.

To utilise our calculator effectively, certain specifications of the drone's camera need to be known:

• Sensor Width (mm): The physical width of the camera's image sensor
• Real Focal Length (mm): The actual focal length of the camera's lens, not the 35mm equivalent which is normally quoted
• Image Width (Pixels): The width of the image produced by the camera, in pixels
• Image Height (Pixels): The height of the image produced by the camera, in pixels
• Horizontal Field of View (degrees): The horizontal extent of the observable world that is seen at any given moment
• Vertical Field of View (degrees): The vertical extent of the observable world that is seen at any given moment
• The Required Forward and Side Overlaps: The percentage of overlap between successive images, both in the direction of the drone's travel (forward overlap) and perpendicular to it (side overlap)

It's common for drone manufacturers to omit detailed specifications such as the real focal length or the sensor's dimensions. However, understanding the sensor type can provide us with the necessary information to deduce the sensor width and height, the real focal length and the Fields of View. These details are often included in the drone's manual or technical specifications. Our calculator bridges this information gap, allowing you to input known data to calculate the rest.

With the correct camera specifications at hand, our calculator can determine the precise distance between images needed to achieve the required overlaps and the resultant GSD. This facilitates the planning and execution of drone mapping projects, ensuring high-quality outcomes regardless of the specific software or drone model used.

To explore this calculation further, visit our GSD and Overlap Calculator: Real Focal Length & FoV Calculator.

Ground Sampling Distance - GSD

Calculating Ground Sampling Distance (GSD) is the firststep in the process of drone mapping, as it defines the real-world size of each pixel in an image. This measure is indicative of the map's level of detail and is fundamental for accurately setting overlaps between images. The formula for calculating GSD is as follows:

GSD = (Flight Height × Sensor Width) / (Real Focal Length × Image Width)

Let's break down the components of this formula:

• Flight Height: The altitude at which the drone is flying, measured in meters. This height directly influences the area covered by each pixel on the ground
• Sensor Width: The physical width of the drone camera's sensor in millimeters. This dimension plays a critical role in determining the field of view for the camera at a given focal length
• Focal Length: The distance between the camera lens and the image sensor when the subject is in focus, measured in millimeters. Unlike the 35mm equivalent, the real focal length is used here to account for the actual scale of the sensor
• Image Width: The total number of pixels along the width of the photo

Footprints

After determining the Ground Sampling Distance (GSD), the next crucial step in drone mapping is calculating the image footprint. The image footprint refers to the actual ground area covered by each image taken by the drone.

The formulas to calculate the width and height of the image footprint on the ground are as follows:

Horizontal footprint

footprint_h = 2 × altitude × tan(FOV_h / 2)

Vertical footprint

footprint_v = 2 × altitude × tan(FOV_v / 2)

Calculating Overlaps

Having established the image footprint on the ground, the next essential step in optimising drone mapping is calculating the required overlaps between images. Overlaps are calculated based on the previously determined image footprint and the desired overlap percentages for both the forward (frontal) and side directions. The calculations are as follows:

Distance between images horizontally (m)

distance_h = footprint_h × (1 - side_overlap / 100)

Distance between images vertically (m)

distance_v = footprint_v × (1 - front_overlap / 100)

The "Overlap Fraction" is the portion of the image that must overlap with the next, expressed as a decimal. For example, an 80% overlap is represented as 0.8.

Significance of Calculating Overlaps

Calculating the correct overlaps is fundamental for drone mapping accuracy. It ensures that the photogrammetry software can correctly align and stitch the images, creating a coherent and detailed map or 3D model. By adjusting the drone’s flight path based on these calculations, drone operators can efficiently cover the desired area, minimising gaps and redundancy in the captured images.

Façade Mapping

When undertaking drone mapping projects that involve façade mapping—capturing the vertical surfaces of buildings or structures—the approach to calculating overlaps requires a nuanced adjustment. Unlike traditional top-down mapping where the drone navigates across a horizontal plane, façade mapping involves capturing images along the vertical axis of the structures. This shift in perspective necessitates a corresponding adjustment in how we calculate and apply the concept of overlaps.

Adjusting Overlaps for Façade Mapping

In façade mapping, the drone moves along rows parallel to the building's face and then ascends vertically to capture the next series of images. This method implies that the typical roles of frontal and side overlaps, as understood in horizontal mapping, are effectively swapped due to the change in the drone’s movement pattern:

• Frontal Overlap Becomes Vertical Overlap: What is usually calculated as frontal overlap (the overlap in the direction of the drone’s flight) needs to be reinterpreted as vertical overlap. This means the overlap that occurs as the drone moves upward along the face of the building.
• Side Overlap Becomes Horizontal Overlap: Conversely, side overlap, which typically occurs perpendicular to the direction of flight in horizontal mapping, now refers to the overlap between images as the drone moves horizontally along the rows.
• Flight Height: becomes distance away from the facade

Practical Implications

This adjustment is crucial for ensuring comprehensive coverage of the façade without gaps in the data. For instance, if a drone were to capture a series of images with an 80% vertical overlap (previously frontal overlap) and a 70% horizontal overlap (previously side overlap), the distances calculated for traditional top-down mapping would need to be applied differently:

• Vertical Overlap Calculation: The calculation for what was the frontal overlap now determines how much the drone should ascend after each horizontal pass to ensure that each new vertical row of images sufficiently overlaps with the previous row.
• Horizontal Overlap Calculation: The calculation for what was the side overlap is now used to ensure adequate overlap between images within the same horizontal row as the drone moves along the building's face.

Real Focal Length and FoVs

Understanding the Field of View (FoV) and the real focal length of a drone's camera is essential for conducting precise drone mapping and photogrammetry operations. The FoV dictates the extent of the scene captured by the camera at any given moment, while the real focal length provides insight into the camera's zoom level and how it affects image composition. These parameters are directly linked to the quality, detail, and comprehensiveness of the resulting maps or models. However, obtaining these crucial metrics can be challenging, as drone manufacturers often do not provide complete camera specifications, especially concerning real focal lengths and precise sensor dimensions. This article guides you through the process of deriving these critical values using known sensor types and the 35mm equivalent focal lengths, ensuring that even in the absence of full specifications from manufacturers, accurate mapping and modeling are still achievable.

We have provided a calculator here for this Carrot Drone Services' Camera Specs Calculator.

Calculating the Real Focal Length

The real focal length of a lens, distinct from the 35mm equivalent, is critical for precise FoV calculations. To determine this, we use the crop factor, which relates the camera's sensor size to the traditional 35mm film standard. The crop factor is calculated by comparing the diagonals of the sensor and the 35mm film frame, with the latter typically measuring approximately 43mm.

For example, the Mavic 2 Pro features a 1" sensor, which has a diagonal of approximately 15.86mm. Thus, the crop factor for this sensor can be calculated as:

Crop Factor = Diagonal of 35mm sensor / Diagonal of camera sensor

Using this crop factor, the real focal length of the camera's lens can be calculated by dividing its 35mm equivalent focal length by the crop factor. If the 35mm equivalent focal length of the Mavic 2 Pro's lens is given, dividing this value by the crop factor yields the lens's actual focal length.

Real Focal Length = 35mm Equivalent Focal Length / Crop Factor

Calculating the Field of View (FoV)

The Field of View (FoV) is the extent of the observable world at a moment, seen through the camera lens. It's determined by the sensor size and the focal length of the lens. The basic formula for FoV in degrees is:

Horizontal Field of View (FoVh):

FoVh = 2 × arctan(Horizontal Sensor Dimension / (2 × Real Focal Length))

Vertical Field of View (FoVv):

FoVv = 2 × arctan(Vertical Sensor Dimension / (2 × Real Focal Length))

This formula can be adjusted for horizontal, vertical, and diagonal FoV by substituting the respective sensor dimension. For accurate FoV calculations, knowing the sensor's width, height, or diagonal length is essential.

Sensor Types and Sizes

The sensor size varies by type, impacting the FoV and focal length calculations. Below is a table outlining common sensor types and their approximate dimensions:

Knowing the sensor type allows us to infer its size, which is essential for calculating the crop factor, real focal length, and FoV. These calculations are pivotal for drone operators to accurately plan their mapping projects, ensuring comprehensive coverage and optimal image quality.

Conclusion: Maximising Accuracy in Drone Mapping

In the realm of drone mapping, understanding and applying the concepts of Ground Sample Distance (GSD) and overlap calculations is fundamental to achieving high-quality, accurate maps. These calculations are not just mathematical exercises; they are crucial steps that significantly impact the end product's detail, clarity, and usefulness. By meticulously calculating GSD and ensuring adequate overlap, drone operators can guarantee that their aerial mapping projects meet the high standards required by various industries, from agriculture to construction.

Drone technology has revolutionised the way we capture data, offering a blend of precision, flexibility, and efficiency unparalleled by traditional methods. However, the power of this technology is fully realised only when operators have a deep understanding of the underlying principles, such as GSD and overlap. As we've explored in this guide, mastering these concepts allows for the optimisation of drone flights, ensuring every mission is both time-efficient and produces data of the highest quality.

At Carrot Drone Services, we pride ourselves on our expertise in drone mapping. Our commitment to precision and quality ensures that your projects are not just completed but elevated through our services. Whether it's through insightful consultations or executing complex mapping tasks, our goal is to deliver excellence. For more information on how we can assist with your drone mapping needs, visit our website or contact us today.