The first permanent Earth observation (EO) satellite, initially called Earth Resources Technology Satellite 1 but today better known as Landsat 1, was launched in July 1972 by the National Aeronautics and Space Administration (NASA), USA. With a ground sampling distance (GSD) of 79 metres, the images captured by the Landsat Multispectral Scanner provided an astonishingly comprehensive and panoramic view of areas never before mapped. That is to say, astonishingly for that particular space era. But technology is evolving all the time, and space power is gradually being distributed more fairly among the nations in the world, as Mathias Lemmens explains in this column.
After all, technology is not an independent being with a will of its own, but a product. Technology has no marital status, address or phone number. Technology is the result of the ingenuity and toil of a particular subgroup of mankind, often condescendingly referred to as ‘techies’ or ‘nerds’. Why are the bearers of technology belittled in this way? Without them, mankind would suffer and ultimately starve.
Launched in August 2014, WorldView-3 – also made in the USA – has a nadir GSD of 31cm in the panchromatic band. This means that the GSD of spaceborne EO sensors has increased by a factor of more than 260 over a time span of 42 years. In other words, the area covered by one pixel in 1972 is covered by nearly 70,000 pixels today, a laudable average year-on-year increase of 30%. This figure indicates a doubling of pixels per unit of area every two and a half years. In 1986, France joined the USA in space by putting the SPOT satellite into orbit, marking Europe’s appearance on the space stage. The first of the Pléiades twins (identical satellites) was put into orbit in December 2011, and the second almost exactly a year later in December 2012. With a GSD of 50cm and a swath width of 20km, these two EO satellites challenged the WorldView family with respect to spatial resolution. But Europe is not intent on keeping its technology to itself. Airbus Defence and Space – successor of the European Aeronautic Defence and Space Company in January 2014 – built an EO satellite for Kazakhstan with a GSD of 1m. Launched on 30 April 2014, the KazEOSat-1 captures 220,000km² daily and can revisit any area in Kazakhstan within three days. On 20 June 2014 the satellite was joined by the KazEOSat-2 with a GSD of 6.5m, aimed at agricultural and resource monitoring, disaster management and land use mapping. Kazakhstan’s neighbouring country and former fellow member of the Soviet Union, Russia, permanently challenges the space power of USA and Europe by operating and gradually extending the Resurs-P family of EO satellites; Resurs-P 1 was launched in 2013, Resurs-P 2 in 2014 and Resurs-P 3, of which one of the solar panels failed to deploy, in 2016. These siblings have a GSD of 1m and a swath width of 38km. With the launches of the Resurs-P family, Russia joined USA and Europe in operating very-high-resolution (VHR) EO space stations, i.e. with a GSD of 1m or better.
China is rapidly emerging as another nation which observes the Earth from space. The country is blasting EO satellites into space from the Gobi Desert with such frequency that it could well usurp the USA as the leading space power before long. China launched 22 EO satellites in 2016 alone, and that annual figure increased to 40 in 2018. One of the satellites is equipped with a sensor which takes 20 frames per second with a GSD of 90cm and a swath width of 22.5km, which now qualifies China as a nation operating VHR EO satellites.
Today, over 30 nations have hundreds of ‘eyes’ in space. With respect to spatial resolution, Europe and the USA still take the lead. Obviously, these developed nations are not hampered by the law of the handicap of a head start – at least, not for the time being. In the meantime, nearly 50 years have passed since the undisputed dominant nation of the planet drastically changed our view of the Earth: from a hostile, injurious environment which should be tamed, to a tiny, vulnerable celestial body floating in the universe together with millions of others – a body which needs protection against human exploitation and malicious depletion. This feeling is aptly captured in one all-encompassing term: climate change. Meanwhile, space power is gradually being distributed more fairly among the nations of the world.
http://www.GIM-INTERNATIONAL.com Jueves 11 de Enero del 2019
The Turkish city of Istanbul is developing a 3D city model mainly aimed at urban planning. The data sources used so far include airborne Lidar, aerial images and 2D maps containing footprints of buildings. Everybody engaged in creating 3D models of large cities faces many issues, challenges and limitations, including excessive data storage requirements, the need for manual editing, incompleteness and other data quality problems. In this article, the authors share their experiences on creating models of the city of Istanbul at the level of detail (LOD) 2 and 3.
The core data for creating the 3D city model of Istanbul was collected by a helicopter flying at a height of 500m and a speed of 80 knots (150km/h) during surveys carried out in 2012 and 2014. The helicopter was equipped with a Q680i Lidar system from RIEGL (Austria), a DigiCam 60MP camera, an AeroControl GNSS/IMU navigation system and an IGI CCNS-5 flight management system. The Lidar point cloud was captured with an average point density of 16 points/m2. The images were recorded with a ground sampling distance (GSD) of 5cm and with 60% along-track and 30% across-track overlap. To ensure high geometric accuracy, eight GNSS base stations were used. The recording of the whole city covering 5,400km2 required thorough flight planning as the flying height and overlap determine a major part of the data quality. Added to this, data accuracy is directly affected by how good the boresights of IMU, GNSS and camera are calibrated and remain stable during the surveys.
The Lidar point clouds were geometrically corrected using RIEGL RiPROCESS and RiANALYZE. The Lidar point cloud was stored in 17,000 LAS files, each covering an area of 500m by 700m. Next these LAS files were used for generating a digital surface model (DSM), a digital elevation model (DEM) and a DSM in which the heights of buildings and other objects refer to the ground surface instead of a local or national reference system. Such a DSM is called a normalized DSM (nDSM). The GSD of the three digital terrain models was 25cm. Combining the DSM with the simultaneously recorded images enabled the creation of orthoimages. The LAS files were also used for classifying the points on the ground, on buildings and in low, medium and high vegetation using MicroStation V8i Connect, TerraSolid and TerraScan software.
The automatic classification of points was done with 90% accuracy. Automatic classification faces severe limitations when adjacent objects clutter. For example, the wrong class was often assigned to buildings close to high trees (Figure 1). Extensive visual checks and manual editing was required to improve the quality of the classification result. Next, the classified points were combined with building footprints extracted from the 1:1,000 base map and with the orthoimages using TerraModeller software to automatically generate around 1.5 million building cubes. Cubes are a 3D representation of level of detail (LOD) 2 (see side bar). Next, the building blocks were augmented by automatically adding roofs using TerraModeller. Mosques, churches, bridges and other complex structures had to be manually mapped using ZMAP software, however. The base map was created from aerial images. Since roof outlines are mapped rather than the actual building footprints, it is not always possible to separate roofs from high trees automatically, thus again requiring extensive manual editing.
The files generated from processing Lidar point clouds, aerial images and 2D base maps were converted to CityGML using an FME Workbench. Next, topologically and semantically correct LOD 2 3D models of buildings were created – in total 1.5 million – with the help of CityGRID software (Figure 2). Various classifications and automatic and manual corrections were made until the 3D model contained the desired details. Based on architectural 3D CAD files (Figure 3), 3,800 landmarks such as mosques were modelled with greater geometric details of facades and roofs (LOD 3) than other buildings. Since no georeferenced photos taken at street level were available, no photo texture was draped over any of the buildings, including the landmarks (Figure 4). The non-textured LOD 3 models were based on the CityGRID XML format to facilitate the topologically correct outlining of roofs, facades, footprints and details such as balconies, dormers and chimneys. In a next step, the created files must be converted to a full CityGML structure.
The creation of the 3D city model of Istanbul is still work in progress. Presently, the main data sources consist of an airborne Lidar point cloud, simultaneously recorded aerial images and the building footprints from the 2D base map of Istanbul. Ground-based data collection has been scheduled to increase the level of detail, with respect to both the geometry and the image texture.>> The preferred technology is laser scanning and 360⁰ panoramic imaging simultaneously captured from a moving car. Many streets in downtown Istanbul are small and narrow and thus inaccessible for cars. It is planned to capture these parts of the city with backpack mobile mapping systems. For the whole of Istanbul, the ground-based data will cover 32,000 kilometres of roads and streets resulting in 2.73 petabytes of panoramic image data. The 3D city model is not yet connected to a database containing semantic building information, but this is part of the future development work.
Biljecki, F. (2017). Level of Details in 3D City Models. PhD thesis, TU Delft, The Netherlands, 353 p.
Buyuksalih, G. (2015). Largest 3D city model ever – case study: Istanbul, Turkey. User presentation at RIEGL Lidar 2015, Hongkong-Guangzhou, China.
Kolbe, T. (2015). CityGML goes to Broadway. Photogrammetric Week 2015, Stuttgart, Germany.
Prandi, F., Devigili, F., Soave, M., Di Staso, U., and De Amicis, E. (2015). 3D Web visualization of huge CityGML models. ISPRS Archives Vol. XL-3W3, pp. 601-605
All the efforts and help on data collection and processing received from the BIMTAS colleagues – Mr Serdar Bayburt and Dr Ismail Buyuksalih especially – is greatly acknowledged. Thanks also go to Mr Hanis Rashidan for the design of Figure 4.
Figure 5: Proposed system architecture of the 3D spatial database and sub-systems.
Last updated: 10/01/2019
http://www.GIM-INTERNATIONAL.com Jueves 11 de Enero del 2019
Providing water for drinking, irrigation and power, glaciers in the world’s highest mountains are a lifeline for more than a billion people. As climate change takes a grip and glaciers lose mass, one might think that, lubricated by more meltwater, they flow more quickly. However, satellite images from over the last 30 years show that it isn’t as simple as that. A paper published recently in 'Nature Geoscience' describes how a multitude of satellite images reveal that there has actually been a slowdown in the rate at which glaciers slide down the high mountains of Asia.
High-mountain Asia stretches from the Tien Shan and Hindu Kush in the northwest, to the eastern Himalayas in the southeast. The area is also part of what is known as ‘the third pole’ because these high-altitude ice fields contain the largest reserve of freshwater outside the polar regions. The source of the 10 major river systems, the third pole provides freshwater for over 1.3 billion people in Asia – nearly 20% of the world’s population.
For more than a decade, satellite data have documented that Asia’s high-mountain glaciers are thinning and losing mass owing to melting. “However, it has not been entirely clear what this loss of ice means for their rate of flow,” said Lead author Amaury Dehecq from NASA’s Jet Propulsion Laboratory (JPL) and from the University of Edinburgh.
Scientists need to understand what regulates glacial flow speed to predict how meltwater will affect the region’s supply of freshwater in the future, and how meltwater adds to sea-level rise.
The study, which was started under ESA’s Dragon programme, uses images from the US Landsat satellites, which are ESA Third Party Missions. Dragon is a joint undertaking between ESA and the National Remote Sensing Centre of China that promotes the use of ESA, Third Party Mission, Copernicus Sentinel and Chinese satellite data for science and applications.
Third Party Missions are not ESA satellite missions, but under an agreement, the data from these missions is also processed and archived by ESA's multi-mission ground systems. The US Geological Survey and NASA Landsat missions fall under this agreement.
Two million pairs of Landsat satellite images
Dr Dehecq and his colleagues analysed almost two million pairs of Landsat satellite images gathered between 1985 and 2017 and used automatic feature tracking to measure the distance that distinctive features on the glaciers, such as crevasses or patches of dirt, had travelled between earlier and later images. Alex Gardner, also from JPL, added: “We did this millions of times to see changes in velocity in the order of one metre a year.”
They found that ice-thickness outweighs any other factor in regulating flow – the thinner the glacier the slower it flows. This challenges the more intuitive theory of glaciers flow quickens because of the lubricating effect of meltwater at their base. One of the reasons for this slowdown is gravity.
Gravitational pull is related to mass, so as a glacier loses mass, the pull weakens causing it to flow slower. Likewise, in the few locations where glaciers have been stable or where they are thickening rather than thinning, flow speeds have been increasing slightly.
Monitoring remote regions from space
Noel Gourmelen from the University of Edinburgh said: “What's surprising about this study is that the relationship between thinning and flow speed is so consistent. These findings should help us better understand how glaciers behaved in the past and better project their contribution to water availability and sea level as they respond to climate change."
“Being able to monitor these remote regions from space over long periods of time is extremely important to understand what is happening. We also now have Europe’s Copernicus Sentinel missions, which also play a crucial role in this kind of monitoring.” With such a large portion of the world’s population dependent on water from these cold heights, changes in the size and flow of these glaciers can bring serious consequences for society.
It is clearly important to continue monitoring this fragile region as recently highlighted by the World Meteorological Organization (WMO) in the National Science Review: Scaling the peaks for social benefits. In recognition of the need to address ease of access to reliable, policy-relevant information on water resources, by integrating emerging knowledge on the accelerated changes in high-mountain cryosphere, the WMO High Mountain Summit will take place in October 2019.
http://www.GIM-INTERNATIONAL.com Martes 18 de Diciembre del 2018
Septentrio, a leader in high-precision GNSS technology, is teaming up with Point One Navigation, a provider of precise location as a service, for autonomous vehicle demonstrations during CES 2019 in Las Vegas, USA. Invitees will be able to ride in a fully autonomous demonstration vehicle that incorporates technology from both companies and to meet directly with technical experts.
Point One Navigation will showcase its proof-of-concept autonomous vehicle equipped with the FusionEngine vehicle localisation software. Demonstrations will utilise corrections from Point One’s Polaris Cloud, an innovative new correction network that enables high-precision GPS and computer vision-based localisation, while allowing the customer to choose the performance and price point that best fits their application. For users operating in open sky scenarios, a Septentrio RTK receiver can be used directly with Polaris Cloud to provide centimeter-level accuracy in seconds. In more challenging urban environments, Point One’s FusionEngine software further integrates cameras and additional sensors to achieve the desired level of precision, even in the complete absence of satellite signals. With coast-to-coast coverage, Polaris Cloud provides a widely available solution for precision localisation today.
High-precision GNSS and computer vision
Point One Navigation has chosen to work with Septentrio to power its solutions for both the correction network and our FusionEngine reference design because of the excellent quality, robustness and jamming resistance of their GNSS receiver technology, said Aaron Nathan, CEO & co-founder. His company has worked with Septentrio before in the autonomous space, and their GNSS technology combined with excellent application support has been integral to Point One’s success, he added.
Neil Vancans, vice-president of global sales for Septentrio stated that Point One Navigation’s thorough understanding of high-precision GNSS integrated with computer vision in a novel way offers a unique approach to solving the challenges in building safe solutions for increasingly autonomous vehicles.
Integrating camera, inertial, and GNSS data
The robust, globally accurate position and orientation obtained by Point One’s FusionEngine software is a result of highly advanced algorithms that integrate camera, inertial, and GNSS data at over 200 times per second. FusionEngine has the accuracy and the resilience to inclement weather demanded by Level 2 applications such as highway lane keeping and V2X, while offering the robustness necessary for mission-critical Level 4 and Level 5 robotaxi and full autonomy applications. What’s more, FusionEngine powered by the Polaris Cloud provides convergence to centimeter accuracy in seconds, not minutes. The platform also completely eliminates the combined location/map provider lock-in typical of other solutions based on HD mapping technology, enabling vehicle OEMs to choose the best map provider for a given region. Point One’s FusionEngine software and Polaris Cloud is a highly innovative combination for safe, cost-effective autonomous driving.
Point One’s solution is powered by Septentrio’s GNSS receivers. For accurate positioning of autonomous vehicles, Septentrio utilises at least two frequencies broadcast by each GNSS constellation (BeiDou, Galileo, GLONASS, GPS, QZSS). With the increased number of GNSS signals obtained from multiple constellations, ADAS-equipped and autonomous vehicles achieve increased accuracy, along with greater reliability, integrity, and availability of GNSS signals.
GNSS module Septentrio recently announced its new mosaic compact multi-constellation GNSS Receiver SiP (system-in-package) module, available in 2019. The Septentrio mosaic, a multi-band, multi-constellation receiver in a low-power surface-mount module with a wide array of interfaces, is designed for mass market applications like robotics and autonomous systems. The mosaic module integrates the latest GNSS and RF ASIC technology, as well as the robust positioning engine from Septentrio.
All Septentrio GNSS receivers and modules feature AIM+ technology, an advanced on-board interference mitigation commercially available. Septentrio GNSS receivers can suppress the widest variety of interferers, from simple continuous narrowband signals to the most complex wideband and pulsed jammers.
Septentrio and Point One Navigation will conduct live autonomous vehicle demonstrations for industry representatives attending the International CES in Las Vegas.
http://www.GIM-INTERNATIONAL.com Martes 18 de Diciembre del 2018
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