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See detailMerging DEMs from VHR Optical Imagery with Drone Data - A High-resolution DEM for Tristan da Cunha
Backes, Dietmar UL; Teferle, Felix Norman UL

Scientific Conference (2018, December 12)

The extraction of high-resolution, Digital Elevation Models (DEM) from very high-resolution (VHR) optical satellite imagery, as well as low altitude drone images by Photogrammetric methods or modern ... [more ▼]

The extraction of high-resolution, Digital Elevation Models (DEM) from very high-resolution (VHR) optical satellite imagery, as well as low altitude drone images by Photogrammetric methods or modern Structure from Motion (SFM) engines, has rapidly matured. Today both data sources are representing cost-effective alternatives to dedicated airborne sensors, especially for remote and difficult to access regions. Ever-growing archives of high-resolution Satellite imagery, are providing a rich data source which covers even the most remote locations with high-resolution imagery up to 0.30m ground sample distance multiple times enabling the generation of high-resolution DEMS. Furthermore, low-cost, low weight and easy to use drones can easily be deployed in remote regions and capture limited areas with very high resolution. Dense point clouds derived from this method provide an invaluable data source to fill the gap between globally available low-resolution DEMs and highly accurate terrestrial surveys. The presented case study investigates the use of VHR archive imagery as well as low-cost drone imagery to generate high-quality DEMs using photogrammetric tools over a remote region which is difficult to access by manned airborne platforms. We highlight the potential and limitations of both data sources to provide high resolution, accurate elevation data. [less ▲]

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See detailCase Study 'Lunar Water and Volatiles' - Lunar resources prospecting with AI
Backes, Dietmar UL

Presentation (2018, June 26)

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See detailTowards multiscale data fusion of high-resolution space borne and terrestrial datasets over Tristan da Cunha
Backes, Dietmar UL; Teferle, Felix Norman UL; Abraha, Kibrom Ebuy UL et al

Poster (2018, April 10)

Ever improving low cost, lightweight and easy to use sensing technologies are enabling the capture of rich 3D Datasets to support an unprecedented range of applications in Geosciences. Especially low-cost ... [more ▼]

Ever improving low cost, lightweight and easy to use sensing technologies are enabling the capture of rich 3D Datasets to support an unprecedented range of applications in Geosciences. Especially low-cost LiDAR systems as well as optical sensors, which can be deployed from terrestrial or low altitude aerial platforms, allow the collection of large datasets without detailed expert knowledge or training. Dense pointcloud derived from these technologies provide an invaluable source to fill the gap between highly precise and accurate terrestrial topographic surveys and large area Digital Surface Models (DSMs) derived from airborne and spaceborne sensors. However, the collection of reliable 3D pointclouds in remote and hazardous locations remains to be very difficult and costly. Establishing a reliable georeference, ensuring accuracy and data quality as well as merging such rich datasets with existing or space borne mapping provide additional challenges. The presented case study investigates the data quality and integration of a heterogeneous dataset collected over the remote island of Tristan da Cunha. High-resolution 3D pointclouds derived by TLS and drone Photogrammetry are merged with space borne imagery while preserving the accurate georeference provided by Ground Control derived from geodetic observations. The volcanic island of Tristan da Cunha located in the centre of the Southern Atlantic Ocean is one of the most remote and difficult to access locations on the planet. Its remote location, rough climatic conditions and consistent cloud coverage provides exceptional challenges for terrestrial, aerial as well as space borne data acquisition. Amongst many other scientific installations, the island also hosts a continuous GNSS station observation and monitoring facilities operated by the University of Luxembourg, which provided the opportunity to conduct a local terrestrial data acquisition campaign consistent with a terrestrial ground survey, Laserscanning and an image acquisition from a low-cost drone. The highly accurate Ground Control network, observed by GNSS and total station, provides a reliable georeference. Pointclouds were acquired around the area of the harbour using a Leica P20 terrestrial Laserscanner, as well as drone Photogrammetry based on images collected by a low-cost DJI Phantom3 drone. To produce a map of the complete island a comprehensive dataset of high-resolution space borne imagery based on the Digital Globe WorldView constellation was acquired which provided high resolution mapping information. The case study presents a cross-validation of terrestrial, low altitude airborne as well as spaceborne datasets in terms coregistration, absolute georeference, scale, resolution and overall data quality. Following the evaluation a practical approach to fuse this heterogeneous dataset is applied which aims to preserve overall data quality, local resolution and accurate georeference and avoid edge artefacts. The conclusions drawn from our preliminary results provide some good practice advice for similar projects. The final topographic dataset enables mapping and monitoring of local geohazards as, e.g. coastal erosion and recent landslides thus also supporting the local population. [less ▲]

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See detailNASA-FDL Artificial Intelligence in Planetary Science; Lunar Resource Mission
Backes, Dietmar UL

Scientific Conference (2017, December 15)

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See detailAn evaluation of low-cost consumer-grade UAS systems for 3D reality capture
Backes, Dietmar UL; Teasdale, Oliver; Eloff, Jacques

Poster (2016, September 22)

During the last years, small lightweight and low cost remotely piloted aerial systems (RPAS) commonly referred as Drones have rapidly developed into capable low-cost Unmanned Aerial Systems (UAS). Fuelled ... [more ▼]

During the last years, small lightweight and low cost remotely piloted aerial systems (RPAS) commonly referred as Drones have rapidly developed into capable low-cost Unmanned Aerial Systems (UAS). Fuelled by a vibrant community of scientists, professionals and hobby enthusiasts enabling technologies have matured quickly, and prices of consumer grade as well as semi-professional systems fell sharply. Especially multirotor vertical take-off and landing (VTOL) UAS have proven to be versatile and flexible platforms which can be equipped with a range of sensors capable of capturing aerial data for a variety of 2D and 3D mapping applications. Consumer grade, low weight systems as the DJI Phantom or 3DR Solo have a limited payload and can carry low weight action cameras like the GoPro Hero models which are capable of collecting video as well as still RGB and near-infrared imagery. Applying traditional Photogrammetric methods to imagery from low-cost UAS systems proved complex and impractical in the past. However modern the state-of-the-art structure from motion algorithms implemented in off the shelf software packages (sometimes referred as new Photogrammetry), cloud processing environments and available via open source libraries promise to generate dense 3D point clouds, textured models and orthomosaics in high quality and without much effort. How accurate and how reliable are data products generated from such systems? Expanding from a preliminary study (BACKES & TEASDALE 2015) we review the every progressing capabilities and features of COTS (commercial of the shelf) user and semi-professional UAS systems under the aspects of deployable sensors, ease of use, reliability as well as safety. We show the workflow from flight planning, data collection to dense pointclould matching using a range of software products. The resulting point clouds are evaluated and benchmarked using a highly accurate and dense reference data acquired via geodetic terrestrial survey and Laserscanning. The results of this evaluations allow conclusions on the current accuracy capabilities of this such low-cost systems. [less ▲]

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