Oblique D2M

Oblique D2M

Oblique D2M

Five-lens RGB Camera

The Oblique D2M is a powerful oblique imaging system consisting of five high-resolution multidirectional cameras, making it the ideal tool for large scale 3D photogrammetry.

A fast trigger interval along with custom high-speed storage provides class-leading time efficiency without compromising data quality. The payload combines four oblique and one downward camera to capture complex geometries with ease.

This ensures remarkable detail even on slanted surfaces and makes Oblique D2M destined for 3D mesh generation of high-rise areas, industrial environments, archaeological sites and alike.

Qube 240 LiDAR

Oblique D2M
Technical Specification

GSD 1.50 cm @100m AGL
Cameras 1 x NADIR, 4 x oblique
Sensor resolution 26 MP (6252 x 4168 px)
Total resolution 130 MP
Sensor size 23.5 x 15.6 mm
Sensor type CMOS
Sensor format APS-C
Focal length 25 mm NADIR, 35 mm (oblique)
Trigger interval ≥ 0.8 s
Payload weight RTF 834 g
Flight time 60 minutes
Storage High speed data storage device (640 GB)

Sample Data Details

C3 Class Identification Label

Flight altitude:

120 m | 393 ft AGL
C3 Class Identification Label

Flight speed:

17 m/s
C3 Class Identification Label

GSD:

1,8 cm/px

Sony UMC-R10C

Sony UMC-R10C

Sony UMC-R10C

RGB Camera

The Sony UMC-R10C camera with 20.1 MP is used as an entry level RGB camera throughout the mapping industry.

This sensor is ideal for capturing high-quality images to create maps or 3D models. With its CMOS type image sensor the UMC-R10C delivers quality images even in low light conditions.

Qube 240 LiDAR

Camera integration done right – easily exchangeable and accessible

Sony UMC-R10C
Technical Specification

Sensor resolution 20.1 MP (5456 x 3632 px)
GSD 2.66 cm @100m AGL
Trigger interval 1.1 seconds
Pixel pitch 4.25 μm
Sensor size 23.2 x 15.4 mm
Sensor type CMOS
Sensor format APS-C
Lens f=16 mm, F2.8
Shutter type Focal plane
Paylaod weight RTF 429 g
Storage SD card (external slot)

 

Sample Data Details

C3 Class Identification Label

Flight altitude:

100 m | 328 ft AGL

C3 Class Identification Label

Flight speed:

17 m/s
C3 Class Identification Label

GSD:

2,66 cm/px

Sony RX1 RII

Sony RX1 RII

Sony RX1 RII

RGB Camera

The Sony RX1 RII meets the highest demands on RGB image quality and resolution in everyday surveying and monitoring applications, especially in the mining, civil survey, and agriculture sector.

With its resolution of 42.4 megapixels, the Sony RX1RII is ideal for all applications where the highest requirements are placed on the images.

The output possibilities vary from precise data sets like orthomosaics, digital terrain models (DTM), digital surface models (DSM), high-resolution point clouds and detailed 3D models to calculate stockpile volumes or excavation volumes for instance.

Qube 240 LiDAR

Camera integration done right – easily exchangeable and accessible

Sony RX1 RII
Technical Specification

Sensor resolution 42.4 MP (7952 × 5304 px)
GSD 1.29 cm @100m AGL
Trigger interval 1.4 seconds
Sensor size 35.9 mm × 24.0 mm
Sensor type CMOS
Sensor format Full frame
Lens f=35 mm, F2.0
Storage SD-Card (SD-card slot)
Paylaod weight RTF 694 g

 

Qube 240 LiDAR vertical view close up

Sample Data Details

C3 Class Identification Label

Flight altitude:

120 m | 393 ft AGL
C3 Class Identification Label

Flight speed:

17 m/s
C3 Class Identification Label

GSD:

1,55 cm/px

Use Case_Counting over 4,000 Pelican Nests in 30 Minutes on Queen Bess Island, Louisiana, USA

Use Case_Counting over 4,000 Pelican Nests in 30 Minutes on Queen Bess Island, Louisiana, USA

Use Case - Counting Over 4,000 Pelican Nests in 30 Minutes on Queen Bess Island, Louisiana, USA

University of Louisiana at Lafayette

The setting

The goal of the mission was to automate the process of counting pelican nests on Queen Bess Island, Louisiana, USA.

The Trinity F90+ drone was used to map the island off of coastal Louisiana that is critically important for brown pelicans, and other seabird, nesting. Traditional surveys at the islands are done on foot and with airplanes, but the Nelson Ecosystem Lab at the University of Louisiana at Lafayette wanted to apply new count methods and automate the counting process. The Trinity platform allows the researchers to access these remote islands and count the pelican nests while avoiding disturbing these delicate habitats.

 

The individual mission challenges were:

  • Flying at an altitude that avoids any disturbance to the birds and achieve a pixel size of less than 2.5 cm.
  • Mapping the entire island, about 17 ha, creating a mosaic in Pix4D
  • Counting nests using object-based image analysis in eCognition and pairing it with on the ground survival and breeding information from other bird researchers

Use Case Location

The story of pelicans and Queen Bess Island

The island was recently restored by the State of Louisiana because it was rapidly eroding. Louisiana loses lots of marshes each year, but this island is very important for pelicans (the official state bird) and supports 15-20% of all nests in the entire state. The restoration project was a big deal and cost about 19 million USD funded by BP oil spill money.

6,600 pelican nests were estimated in 2018 when only 2 out of 15 hectares were habitable for nesting, prior to restoration. Most of the island was becoming open water and limiting the nesting area. Sand was pumped in, rock barriers installed, and new vegetation planted to keep the island in place. Restoration is an important research topic in Louisiana because it occurs on such a large, coast wide scale, and it is difficult to predict how animals might respond to construction.

 

 

Drones combined with object-based image analysis are the tools of choice for counting pelican nests

Officials were excited to see pelicans using the island earlier in spring 2020 which made The University of Louisiana decide it was a priority for a drone survey. Ph.D. James Nelson and J. Mason Harris from the University of Louisiana at Lafayette, Department of Biology, chose a Quantum-Systems Trinity F90+ drone with Sony UMC Camera to count nests using object-based image analysis in eCognition. They came up with 4,320 nests on their first count. J. Nelson and J. Harris were working with pelican researchers to examine and double-check the numbers.

“The Trinity’s speed and accuracy is simply not matched by any other UAS platform we have ever worked with.”

James Nelson Ph.D.

Endowed Professor of Environmental Biology, University of Louisiana at Lafayette, Department of Biology

The mission was a success because of proper planning, execution, and minimal disturbance to wildlife. The UAV’s mapping capabilities allowed the researchers to study the site quickly and more effectively. The real value of the data is how it can be paired with on the ground survival and breeding information from other bird researchers to test small-scale patterns over the entire island using drone surveying.

The object-based image analysis approach helped make the counts more efficient. Nests were outlined very well using eCognition’s segmentation algorithms. After figuring out the appropriate scale and parameters, John M. Harris was able to effectively delineate nests from other objects and identify them based on spectral and geometric features. Since some nesting areas were more densely populated than others, he had to use multiple rounds of segmentation and classification. The object-based approach increased accuracy because different sections of the island could be classified using slightly different methods.

Contact:

James Nelson Ph.D.

Endowed Professor of Environmental Biology

Department of Biology
University of Louisiana at Lafayette
PO Box 43602
Lafayette, LA 70504

J. Mason Harris

Sea Grant Graduate Research Scholar

Ecosystems Ecology Lab
University of Louisiana at Lafayette

Facts & Figures

Altitude:
80 m AGL | 262.5 ft

Flight time:
30 min

Resolution

GSD:
2.5 cm | 0.98 inch

Camera

Camera:
Sony UMC-R10C

Area

Area:
17,4 ha | 43 acres

Wind

Wind:
5 m/s | 9.7 kn

Pictures taken

Pictures:
1302

Overlap

Overlap:
Side 80 %
Forward 60 %

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