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  6. I would like to present a project withthe acronym 4DShoreMap that has been going on for a year. The project is called „Innovative system for multidimensional and multitemporal monitoring of the coastal zone using an autonomous unmanned vessel” (4DShoreMap). It will use an Auonomous Surface Vehicle called HydroDron-1. The goal of the project is development of a prototype of a multidimensional and multitemporal coastal zone monitoring system using autonomous unmanned floating platforms platforms at a single survey pass. Creating a 4D map is based on geodata from such sensors as: - echo sounder, - sonar, - metric camera, - LiDAR. You can read more about our project on the company's website [EN]: https://marinetechnology.pl/en/project/4dshoremap-2/ You can read more about our project on the project website [PL]: http://4dshoremap.marinetechnology.pl/ All scientific publications based on our project will be available on the ResearchGate website [EN]: https://www.researchgate.net/project/Innovative-system-for-multidimensional-and-multitemporal-monitoring-of-the-coastal-zone-using-an-autonomous-unmanned-vessel-4DShoreMap-2
  7. Following the successful launch of Landsat 8 and during the development of Landsat 9, the United States Geological Survey (USGS) and NASA assembled a team of experts from within both agencies for a Joint Agency Sustainable Land Imaging Architecture Study Team to evaluate how to inform an acquisition strategy for a follow-on mission that would best satisfy the diverse and evolving user needs collect by the USGS. The highest-recommended architecture was a small constellation of “superspectral” space-based sensors that would improve the spectral, spatial, and temporal capabilities. Landsat Next data would be sufficiently consistent with data from the earlier Landsat missions to permit studies of land cover and land use change over multi-decadal period. Landsat Next Defined Landsat Next will be a constellation of three observatories sent into orbit on the same launch vehicle, which will provide an improved temporal revisit for monitoring dynamic land and water surfaces such as vegetation, wildfire burns, reservoirs and waterways, coastal and wetland regions, glaciers, and dynamic ice sheets. Landsats 8 and 9 measure 11 spectral bands from the visible to thermal infrared wavelengths. Landsat Next will have 26 bands; this includes refined versions of the 11 Landsat “heritage” bands, five bands with similar spatial and spectral characteristics to the European Space Agency’s Copernicus Sentinel-2 bands to allow easier merging of data products, and ten new spectral bands to support emerging Landsat applications. With these improvements, Landsat Next will collect on average about 20 times more data than its predecessor, Landsat 9, and continue to provide free and open data access for all users. The Landsat Next mission successfully passed Key Decision Point A (KDP-A) and is currently in Phase A. Upcoming project studies will complete the mission design, data management and compression approaches, flight instrument requirements and architecture, and spacecraft resource definition. The mission is planned to launch in late 2030. https://landsat.gsfc.nasa.gov/satellites/landsat-next/
  8. A Long March-4B (CZ-4B serial number Y55) rocket launched the Gaofen-11 04 observation satellites from Taiyuan Satellite Launch Center (platform LC9), north China's Shanxi Province, Dec. 2022, at 15:37 Beijing time (07:37 UTC). According to official sources, Gaofen-11 04 (高分 十一 号 04, Gāo Fēn Shíyī hào04, “GF11-04”) entered the initial target orbit as planned, and a network with Gaofen-11 01, 02 and 03 will be added to “improve the efficiency of Earth observation and make greater contributions for social development in the areas of land survey, urban planning, land ownership, road network design, crop estimation and disaster prevention and mitigation.” The common initial orbit for GF 11 spacecraft is 248 km x 694 km, inclined at 97.4° to the equator. According to reports, the Gaofen 11 spacecraft may be able to achieve a ground image resolution of 10 cm or less. If this conjecture can be confirmed, China confirms that it has a satellite image capacity second only to that of the United States. Even with a civilian name, the Gaofen 11 clearly demonstrate their military use: they can provide information for the implementation of major national strategies such as the “Belt and Road” and the modernization of Chinese national defense. The satellite imaging system allows “reading a newspaper headline in the hands of someone on the ground”. The satellite is sometimes compared to the Key Hole KH-11 KENNEN of the Americans, and the diameter of the optical telescope aperture is 1.7 meters – which may indicate the presence of a large mirror used by a Dobsonian-type telescope. The largest mirror carried by a commercial observation satellite is the 1.1 m mirror on the Worldview 3 & 4, manufactured in the USA by ITT Exelis. For non-commercial satellites, the French have published images of their Helios 2, suggesting they have a 1.4 m mirror. The GF-1 is better than all of them and is only surpassed in its optical imaging category by two US devices, the Hubble Space Telescope, which has a 2.4 m mirror working at optical wavelengths; the KENNEN spies which must have a mirror size similar to that of Hubble. As the GF-11s are positioned at 247 × 693 km (parameters converted to a circular orbit of 470 km later), a 1.7 m mirror would give a terrestrial resolution of 8 to 10 cm at perigee, around 10:00 am local solar and 20°N, directly over India and the South China Sea. The resolution released in November 2020 by academic Li Deren, in fact, is 0.1 meter. At an average altitude of 470km the resolution is still 15 to 20 cm, surpassing all commercial and most reconnaissance satellites. This puts China in the select club of countries that can acquire NIIRS 8-9 resolution qualities which means the resolution is high enough to identify handheld small arms. Supposedly, the only members of this club are the US and now China, and this will continue to be the case for the foreseeable future, with perhaps Russia joining them later if the Razdan program lives up to its promises. No further details of the satellite released, continuing Chinese reticence with this type of Gaofen. To date, China has launched sixty-two orbital rockets, with 60 of them successful. The rocket and satellite used in this launch were developed by the Fifth Academy of China Aerospace Science and Technology Corporation – CASC and manufactured by Aerospace Dongfanghong Satellite Co. According to official media, this was the 457th flight of a rocket that uses the Long March name, counting all models. The Taiyuan Launch Center experiences very cold weather in December, with temperatures reaching minus 30 degrees Celsius. In order to ensure that the rocket was not affected by environmental conditions and was launched within the allotted time, safeguard measures were taken: on the one hand, low temperature protection for winter, and the combination of insulation layer and pumped hot air is used; on the other hand, preparations in order to improve the trajectory in relation to the load of high altitude winds, when the wind conditions reach a certain limit, the trajectory is modified increasing the probability of launch in the predetermined window. The Long March 4B is developed by the Aerospace Science and Technology Group, capable of operating at room temperature as a three-stage rocket, capable of launching various types of satellites in different orbit capacity requirements, placing a single satellite or multiple payloads. in sun-synchronous orbit or in geosynchronous orbit. The CZ-4B is developed by the Aerospace Science and Technology Group, capable of launching various types of satellites in different orbit capacity requirements, placing a single satellite or multiple payloads, in sun-synchronous orbit or geosynchronous orbit. The orbital payload capacity can reach 2.5 tons. source: https://www.spaceintel101.com/post/china-launches-gaofen-11-04
  9. SpaceX, the space exploration company, launched a research satellite into orbit on Friday 16 December 2022. Named SWOT (Surface Water and Ocean Topography), the satellite – which is the size of a large car – will measure the water level on more than 90% of the planet’s surface from its position 890km above the Earth. Scientists will use this data to identify areas vulnerable to flooding or extreme drought, as well as to track the rate of rising sea levels and the resulting coastal erosion. It will take SWOT three weeks to map the world’s water. By regularly repeating the process, scientists will be able to gain insight into water currents and create a complete picture of the world. Thanks to advanced technology, SWOT allows scientists to observe seas and oceans at previously unattainable scales. Mapping Earth’s Water in Real Time With this new high-resolution satellite, scientists can now capture the behaviour of Earth’s water bodies in real time. This allows for the detection of changes that necessitate accuracy to the centimetre level, such as the motion of coastal currents or streams or rivers. The goal of the satellite mission is to observe the water cycle in all oceans, rivers and seas for a minimum of 3.5 years. The satellite was developed by the American and French space agencies NASA and CNES, with financial support from Canada and the United Kingdom. source: New Satellite Set to Revolutionize Understanding of Water on Earth | GIM International (gim-international.com)
  10. Shapely 2.0 version is a major release featuring a complete refactor of the internals and new vectorized (element-wise) array operations, providing considerable performance improvements (based on the developments in the PyGEOS package), along with several breaking API changes and many feature improvements. Refactor of the internals# Shapely wraps the GEOS C++ library for use in Python. Before 2.0, Shapely used ctypes to link to GEOS at runtime, but doing so resulted in extra overhead and installation challenges. With 2.0, the internals of Shapely have been refactored to expose GEOS functionality through a Python C extension module that is compiled in advance. Vectorized (element-wise) geometry operations Before the 2.0 release, Shapely only provided an interface for scalar (individual) geometry objects. Users had to loop over individual geometries within an array of geometries and call scalar methods or properties, which is both more verbose to use and has a large performance overhead. Shapely 2.0 API changes (deprecated in 1.8) The Shapely 1.8 release included several deprecation warnings about API changes that would happen in Shapely 2.0 and that can be fixed in your code (making it compatible with both <=1.8 and >=2.0). See Migrating to Shapely 1.8 / 2.0 for more details on how to update your code. It is highly recommended to first upgrade to Shapely 1.8 and resolve all deprecation warnings before upgrading to Shapely 2.0. Summary of changes: Geometries are now immutable and hashable. Multi-part geometries such as MultiPolygon no longer behave as “sequences”. This means that they no longer have a length, are not iterable, and are not indexable anymore. Use the .geoms attribute instead to access individual parts of a multi-part geometry. Geometry objects no longer directly implement the numpy array interface to expose their coordinates. To convert to an array of coordinates, use the .coords attribute instead (np.asarray(geom.coords)). The following attributes and methods on the Geometry classes were previously deprecated and are now removed from Shapely 2.0: array_interface() and ctypes asShape(), and the adapters classes to create geometry-like proxy objects (use shape() instead). empty() method source: Version 2.x — Shapely 2.0.0 documentation
  11. Landsat Next is on the horizon—the new mission will not only ensure continuity of the longest space-based record of Earth’s land surface, it will fundamentally transform the breadth and depth of actionable information freely available to end users. Landsat Next will also provide new capabilities for the next generation of Landsat users. The enhanced spatial and temporal resolution of the 26-band “superspectral” Landsat Next constellation will unlock new applications for water quality, crop production and plant stress, climate and snow dynamics, soil health and other essential environmental variables. Landsat Next also continues Landsat’s decades-long data record of multispectral imagery, which affords global, synoptic, and repetitive coverage of Earth’s land surfaces at a scale where natural and human-induced changes can be detected, differentiated, characterized, and monitored over time. Landsat Next Defined Landsat Next will be a constellation of three observatories sent into orbit on the same launch vehicle, which will provide an improved temporal revisit for monitoring dynamic land and water surfaces such as vegetation, wildfire burns, reservoirs and waterways, coastal and wetland regions, glaciers, and dynamic ice sheets. Landsats 8 and 9 measure 11 spectral bands from the visible to thermal infrared wavelengths. Landsat Next will have 26 bands; this includes refined versions of the 11 Landsat “heritage” bands, five bands with similar spatial and spectral characteristics to the European Space Agency’s Copernicus Sentinel-2 bands to allow easier merging of data products, and ten new spectral bands to support emerging Landsat applications. With these improvements, Landsat Next will collect on average about 20 times more data than its predecessor, Landsat 9, and continue to provide free and open data access for all users. The mission is planned to launch in late 2030. The Path to Landsat Next Following the successful launch of Landsat 8 and during the development of Landsat 9, the United States Geological Survey (USGS) and NASA assembled a team of experts from within both agencies for a Joint Agency Sustainable Land Imaging Architecture Study Team to evaluate how to inform an acquisition strategy for a follow-on mission that would best satisfy the diverse and evolving user needs collect by the USGS (Wu et al., 2019). The highest-recommended architecture was a small constellation of “superspectral” space-based sensors that would improve the spectral, spatial, and temporal capabilities. Landsat Next data would be sufficiently consistent with data from the earlier Landsat missions to permit studies of land cover and land use change over multi-decadal period. Why Landsat Next Landsat is a civilian satellite program that was initiated to map, monitor, and manage Earth’s natural resources. It has provided an unbiased and unvarnished history of the planet and its changing conditions during the past half-century. Landsat data are critical for mapping natural resources and impact numerous society benefits such as food security, water use, disaster response and more. Landsat also provide essential data for monitoring the ecosystems, water quality, land cover and land use change, and an unparalleled data record of the environment and climate change. Landsat has been the cornerstone of Earth observing for more than half a century, and Landsat Next will add to this record for the next generation: Landsat has been ranked as a top Earth-observation program in terms of societal benefits provided, along with GPS and weather satellites according to the 2014 National Science and Technology Council report. Landsat is the most widely used land remote sensing data source within Federal civil agencies. Commercial providers rely on Landsat’s rigorous calibration to build/improve products. Landsat has been an essential data source for a wide range of Earth science research. Landsat is the most cited Earth-observation data set within the scientific literature (Wulder et al., 2022). Landsat Next will provide enhancements to Landsat “heritage” data: Improved temporal revisit for monitoring dynamic land and water surfaces such as vegetation and crop phenology, burn severity, water use and quality, coastal and wetland change, glacier, and ice sheet dynamics. Improved spatial resolution for agricultural monitoring, ecological monitoring, urban studies, water resources management and other applications. Landsat Next will provide new capabilities for the next generation of Landsat users: New spectral bands and refined bands will support new and evolving applications, including surface water quality, cryospheric science, geology, and agricultural applications including crop management and water consumption. The new bands will have similar spatial/spectral characteristics to those of the European Space Agency’s Copernicus Sentinel-2 satellite, to allow easier merging of data products. source: Landsat Next | Landsat Science (nasa.gov)
  12. This removal includes all Collection 1 Level-1, Level-2, Level-3, and ESPA- related Landsat 1-8 products. Collection 1 has not been updated with Landsat products since December 31, 2021 and does not include Landsat 9 data. Users are encouraged to migrate their workflow to Landsat Collection 2 as soon as possible. Due to advancements in data processing and algorithm development, users are discouraged from using Collection 1 and Collection 2 interchangeably within the same workflow. Landsat Collection 2 was first made available in 2020, marking the second major reprocessing of the Landsat Archive by the USGS. The effort harnessed recent advancements in data processing, algorithm development, and data access and distribution capabilities to substantially enhance Landsat data products. The collection includes Landsat Level-1 data for all sensors since 1972 as well as global Level-2 surface reflectance and surface temperature scene-based products from 1982 to present. source: https://www.usgs.gov/landsat-missions/news/landsat-collection-1-datasets-be-removed-december-30-2022
  13. github much more than developer collab tools nowadays you can get interesting links, tutorial list, leaks, hack, and even now a discount lists, LOL 🤣
  14. Black Friday is here, despite the inflation. All the developers and designers who are looking for something here is a curated list. https://github.com/trungdq88/Awesome-Black-Friday-Cyber-Monday Hurry up !!
  15. Good news because I've been waiting for it since a long time. I have already tried it but I was really disappointed. "Input" from merginmap is really far more efficient than QField. I've been using this tool for 2 years now and it's really powerful.
  16. Amazing dude , thanks for shared, regards.
  17. QField’s main new feature of this 2.5 release cycle is its brand new elevation profiling functionality which has been added to the measuring tool. Users are now able to dynamically build and analyze elevation profiles wherever they are – in the field or on their desktop – by simply drawing paths onto their maps and projects. This is a great example of QField’s capability at bringing the power of QGIS through a UI that keeps things simple and avoids being in your way until you need it. Oh and while we’re speaking of the measuring tool, check out the new azimuth measurement! This new version also brings multi-column support to feature forms. QField now respects the number of columns set by users in the attributes’ drag and drop designer while building and tweaking projects in QGIS. The implementation will take into account the screen availability and on narrow devices will revert to a one-column setup. Pro tip: try to change the background color of your individual groups to ease understanding of the overall feature form. Another highlight of this release is a brand new screen lock action that can be triggered through QField’s main menu found in the side dashboard or in the map canvas menu shown when long pressing on the map itself. Once activated, QField will become unresponsive to touch and mouse events while keeping the display turned on. When locked, QField also hides tool buttons which results in a more complete view of the map extent. Stability improvements As with every release, our ninjas have been spending time hunting nasty bugs and improving stability and QField 2.5 is no exception. In particular, the feature form should feel more reliable and even more polished. sites: QField - Efficient field work built for QGIS
  18. Although there is an extensive array of optical remote sensing sensors from a variety of satellites providing long time data records, their data are incapable of retrieving reliable land surface information when clouds, aerosols, shadows, and strong angular effects are present in the scenes. The mitigation of noise and gap filling of satellite data are preliminary and almost mandatory tasks for any remote sensing application aimed at effectively analyzing the earth's surface continuously through time. In 2020, to tackle this challenging problem Moreno-Martinez et al. (2020) proposed using the Google Earth Engine (GEE) cloud computing platform to implement the HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM). This method generates reduced noise and gap-free estimates of Landsat reflectance values at vast scales. Despite the computational power of GEE and the optimizations of HISTARFM, the computational burden and memory costs of HISTARFM are too high to carry out any extra computations after the gap-filling process. Therefore, the data have to be pre-processed in different study areas. We have generated data for number of world regions already, and in this tutorial we will show how to use it and provide examples of how you can improve your research and applications with this enhanced Landsat-based dataset. page: HISTARFM - How to Work with Gap-Filled Imagery | Google Earth Engine | Google Developers
  19. A new, substantially upgraded hydrological reanalysis dataset of the Global Flood Awareness System (GloFAS) from 1980 to July 2022 has been produced by the Joint Research Centre (JRC) of the European Commission in collaboration with ECMWF and released as part of the Copernicus Emergency Management Service (CEMS). It puts river discharge and flood events during that period at the fingertips of users. The GloFAS v4.0 reanalysis includes daily maps of discharge over the globe at a resolution of 0.05 degrees (about 5 km). It is available in the Climate Data Store of the Copernicus Climate Change Service (C3S) run by ECMWF. Generating such a high-resolution reanalysis has been made possible by ECMWF’s new high-performance computing facility in Bologna, Italy. This updated version is a new edition of the GloFAS v3.1 reanalysis, which was introduced in 2021. The new dataset uses ERA5 (ECMWF’s latest reanalysis of the atmosphere), satellite-derived datasets, and a large number of ground measurements to describe catchment physical properties and for model calibration, combined with the LISFLOOD hydrological model. The reanalysis makes it possible to study flood events and droughts globally during a much longer time frame than the period during which GloFAS has been operational. It is launched as the EU holds an extreme weather and natural disasters thematic day at the UN’s 27th Conference of the Parties (COP27) on climate change. New elements The open-source LISFLOOD hydrological model determines what happens with water that comes down as rain. It decides to what extent the water evaporates, is absorbed by the soil and plants, or runs off into rivers. For the reanalysis upgrade, ECMWF and the JRC, which manages CEMS, both worked on LISFLOOD. This included upgrades in hydrological routines and improvements in the management of large input datasets and computational performance. ECMWF improved the representation of rivers, soil and vegetation in the model, and the JRC calibrated the model and performed a parameter regionalisation for ungauged catchments to ensure the best possible simulation of river flows for all catchments around the world. The new GloFAS v4.0 hydrological reanalysis also has a finer resolution than before: it works on a 0.05 degree grid (about 5 km) when before it used a 0.1 degree grid (about 10 km). Significant changes in computational performance, made possible by the parallelisation of routines in the hydrological model LISFLOOD, combined with ECMWF’s new Atos HPCF, have enabled the global simulation at the increased spatial resolution. source: Copernicus Emergency Management Service releases GloFAS v4.0 hydrological reanalysis | ECMWF
  20. RIEGL’s new era of Terrestrial Laser Scanners: the VZ-600i stands out with extreme versatility, high productivity, radical performance, and game changing mobility features. With 3D position accuracy of 3 mm and less than 30 sec scan time for high-resolution scans with 6 mm point spacing at 10 m, this enables more than 60 scan positions/hour with real-time registration. Featuring a weight less than 6 kg (13 lbs), 2.2 MHZ PRR, three internal cameras & GNSS integrated, and prepared for mobile mapping applications. These key features will speed up your workflow in indoor and outdoor applications like AEC (Architecture, Engineering, Construction), BIM (Building Information Modeling), as-built surveying, forensic and crash scene investigation, archeology & cultural heritage documentation, forestry, and many more.
  21. The UP42 ArcGIS Pro Add-in is a UI extension that lets ArcGIS Pro users quickly access UP42 storage and projects. The add-in will enable ArcGIS Pro users to seamlessly visualize and analyze their downloaded imagery from UP42 without leaving the Esri ecosystem. Technical Information The UP42 add-in for ArcGIS Pro offers users an easy, intuitive way to simplify and streamline their data access and visualization workflows. After ordering geospatial imagery from the leading data and analytics providers on the UP42 platform, ArcGIS Pro users can leverage this add-in to visualize the data, perform further analyses, and share the results with the relevant stakeholders. The add-in will significantly reduce the complexities of geospatial data access, enabling a seamless workflow between your UP42 and Esri ecosystems. If you are an UP42 user, the add-in will allow you to perform the following actions from your ArcGIS Pro account: Open an UP42 project that you are working on and access the job results within that project Access all of your downloaded data and pending orders within UP42 storage Perform further visualization or analyses on any of the downloaded imagery using ArcGIS Pro capabilities doc: https://docs.up42.com/help/knowledge-base/arcgis-addin?_ga=2.210985938.692905390.1666325415-68821805.1666325401 marketplace link: https://up42.com/marketplace/data/integration/arcgis-pro-add-in
  22. With the 2021 launch and commissioning of the first two Pléiades Neo satellites, Airbus, Intelligence delivered on its promise of providing the highest native spatial resolution Earth observation satellite imagery to the commercial market. And in the coming months, Airbus will double down on its commitment to building the most powerful and versatile remote sensing constellation with the launch of two additional Pléiades Neo satellites. Much has been said about the Pléiades Neo’s 30cm spatial resolution, and for good reason. Best on the commercial market today, this resolving power expands the applications of satellite imagery into numerous surveying and mapping sectors where detecting, identifying, and measuring small objects is paramount. Often lost in the Pléiades Neo discussions, however, are the other significant upgrades brought to the market. Most importantly, each of the four identical satellites offers a previously unmatched geolocation accuracy and image consistency, which in many ways is just as crucial to mapping applications as spatial detail. The uncorrected imagery (Primary) has a native accuracy of 3.5 meters (m) CE90. This means that 90 percent of the points in the image will fall within 3.5m horizontally of their absolute location on the Earth’s surface. In addition, the orthorectified products sold by Airbus boast a stunning 5m CE90 accuracy worldwide. The new constellation has ushered in an era of rapid customer tasking that makes near real-time data delivery a reality. But that’s not all; the four Pléiades Neo satellites capture image data with enhanced multispectral capacities now including the Deep Blue and Red Edge bands. The combination of spatial resolution, geolocation accuracy, new bands, and fast tasking makes the constellation the most versatile surveying and mapping satellites ever built for commercial use. Completing the Constellation The four-satellite Pléiades Neo constellation is the significantly upgraded follow-on to the high-resolution Pléiades 1A and 1B satellites launched by Airbus in 2011 and 2012. For this reason, the first two new satellites are officially called Pléiades Neo 3 and 4. They will be joined in orbit in November by Pléiades Neo 5 and 6, which are scheduled for launch aboard a Vega-C rocket from Kourou, French Guiana. The four Pléiades Neo satellites each capture 30cm panchromatic and 1.2m multispectral image data at 14km swath widths. Each can collect up to 500,000 square kilometers (km2) per day and up to 7,500 km2 in a single pass. Together, the four will capture 2 million km2 in a single day. The satellites are extremely agile which gives them the ability to slew in orbit to capture imagery at oblique angles to either side. This expands the frequency with which they can collect “revisit” imagery over the same geographic area on Earth’s surface. By itself, each satellite has daily revisit, and the two in orbit increase this cycle to twice daily. When the constellation is complete, nearly any location on the globe can be imaged at least two times a day, and sometimes three times, depending on viewing angles and latitude. This enables users to monitor rapidly evolving events such as floods, fires, and storms Designed with Accuracy and Resolution in Mind Airbus designed the Pléiades Neo satellites and processing systems from the ground up to capture and deliver the highest quality commercial imagery possible. From the perspective of nearly any geospatial application, especially feature mapping, the accuracy of pixel geolocation is equally important to the pixel resolution. In other words, knowing precisely where a feature is located on Earth is just as crucial as identifying it. Accurate geolocation starts on the satellites where onboard sensors, star trackers, and inertial measurement units ensure the spacecraft knows where it is when each image is captured. This telemetry data, along with a precise model of the imaging sensor geometry, is an important input for processing the raw imagery and achieving a high level of location accuracy. With minimal additional processing, Airbus sells this data, called a Primary product, to customers who perform their own geometric processing. As noted above, the advertised native planimetric accuracy is 3.5m CE90, although most data sets are achieving better results. In addition, a superior 5m geolocation accuracy is achieved worldwide in the Orthorectified image products where geometric distortions have been removed in a processing workflow developed specifically for the era of Pléiades Neo. To accomplish this, Airbus created a new worldwide database of highly accurate 3D ground control points, called Space Reference Points (SRPs), from archived SPOT 6 and 7 multi-view satellite image stacks acquired over decades of SPOT operations. In addition to the SRPs, the Airbus geometric processing workflow uses elevation models from the Airbus WorldDEM4Ortho product database to reach the 5m planimetric accuracy. The Digital Terrain Models and Digital Surface Models from the WorldDEM4Ortho database, which was generated from the Airbus TerraSAR-X and TanDEM-X satellite radar missions, delivers a sub-meter vertical accuracy in the orthorectified products. It is important to note that Airbus now uses the SRP/WorldDEM4Ortho geometric processing workflow to generate orthorectified image products for its Pléiades Neo, Pléiades 1A/1B, SPOT 6/7, and Vision 1 Earth observation satellites. This means all Airbus optical orthoimages are aligned to the same reference system and are fully compatible with each other for large, multi-sensor analysis projects, such as time-series change detection. Additionally, inter-sensor compatibility is a necessity for analysis involving Artificial Intelligence. Available and Accessible Now Pléiades Neo 3 and 4 have been commercially operational since late 2021, and their combination of spatial detail, geolocation accuracy, and frequent revisit have made them an instant hit for a variety of mapping and monitoring applications in Defense/Intelligence, Energy, Agriculture/Forestry, Civil Engineering, and Maritime sectors. Unprecedented user accessibility has also impacted the enthusiasm. Satellite tasking can be scheduled directly by the customer through the Airbus OneAtlas platform in as little as 25 minutes before a satellite passes over an area of interest. An entirely new production workflow can downlink the data from a Pléiades Neo satellite and deliver a finished product to the customer within as little as two hours of order placement – the first truly near real-time imaging capability on the market today. Contributing to this broad appeal are the advanced multispectral capabilities in the Visible Red, Green, Blue, and Near Infrared portions of the spectrum – as well as two new bands. The new Deep Blue band (400-450 nm) penetrates the water column to deliver insights related to depth and water quality in the near-shore ocean environment, as well as in freshwater lakes, rivers, and streams. Uses have developed hydrologic and bathymetric applications in the coastal zones, including mapping seafloor relief in shallow areas. The Red Edge band (620-690 nm) is being leveraged to detect subtle vegetative stress in applications related to agriculture and forestry. Sensitive to chlorophyll production in green plants, this wavelength enables users to identify problems in vegetation, such as infestation, poor irrigation, and inadequate fertilization. Red Edge data reveals the stress early in the growth cycle before the human sees it, often before the damage is permanent. The spatial detail of Pléiades Neo data allows farmers and foresters to find vegetative problems in individual plants and trees. source: https://www.intelligence-airbusds.com/imagery/constellation/pleiades-neo/
  23. Dear Lurker I am back and active . Kindly make as an active. 🖐️
  24. Following the success of Knighthawk and Watchtower, Paladin is extending LTE capabilities to DJI in an effort to help departments start drone-as-a-first-responder (DFR) programs quicker and more effectively. Paladin founder Divy Shrivastava spent most of his childhood in a town 25 min north of Columbus, Ohio. In 2016, just as he was readying to attend Berkley for Engineering, a close friend’s house caught fire and burned down. This event had a massive impact on Divy and the community as a whole and introduced him to the world of public safety. He knew there had to be a better way. After talking to the local fire chief, he learned two important facts: 1) a fire doubles in size every 30 seconds. 2) First responders never have enough information when they arrive on the scene. This is because when someone calls 911, they’re usually panicked or not trained to assess a scene. After another fire burned down a campus church during his first year at Berkley, claiming several lives, Divy set out to found Paladin with a single mission - send autonomous drones to 911 calls to give first responders a live overhead view of an emergency before they arrive, helping to increase situational awareness, decrease response times, and save lives. In 2021, Paladin released Knighthawk—an autonomous drone with LTE connection, enabling it to fly BVLOS missions and Watchtower—Paladin's cloud-based DFR software, capable of drone flight, multi-device live video feed, and data management all within one platform. Paladin EXT is the next evolution of Paladin's mission to equip first responders with a one-stop solution for a more effective DFR response. Paladin EXT integrates directly with DJI Matrice 300 and Matrice 30 via the built-in PSDK port to leverage the extended range of an LTE connection. “Paladin’s mission is to build technology that saves lives. Every department that wants DFR should be able to use it - it’s our responsibility as a company to remove barriers to make this happen,” said Paladin founder Divy Shrivastava. “Now, with the EXT module, departments won’t need to purchase new equipment to do it. It brings the ease of use and unlimited range of Paladin’s C2 link to the amazing capabilities of DJI’s Matrice lineup, making DFR more accessible than ever before.” With LTE connection, drones are able to safely travel beyond a 3-mile radius from their home base. Traditional radio-based drones carry limitations both in range and scalability because of the need for remote pilots-in-command (RPICs) with a direct connection from the remote controller to the drone. By leveraging the power of LTE, agencies and departments can eliminate the need for larger fleets and RPICs on rooftops and cut down on unnecessary costs to cover the same area. LTE allows for a 3:1 coverage when compared to radio-controlled drones. LTE is also a major factor in how Paladin is able to acquire BVLOS waivers without the need for a visual observer (VO) on a roof for its existing customers and partners.
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