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  1. 4 points
    geemap is a Python package for interactive mapping with Google Earth Engine (GEE), which is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. During the past few years, GEE has become very popular in the geospatial community and it has empowered numerous environmental applications at local, regional, and global scales. GEE provides both JavaScript and Python APIs for making computational requests to the Earth Engine servers. Compared with the comprehensive documentation and interactive IDE (i.e., GEE JavaScript Code Editor) of the GEE JavaScript API, the GEE Python API lacks good documentation and functionality for visualizing results interactively. The geemap Python package is created to fill this gap. It is built upon ipyleaflet and ipywidgets, enabling GEE users to analyze and visualize Earth Engine datasets interactively with Jupyter notebooks. geemap is intended for students and researchers, who would like to utilize the Python ecosystem of diverse libraries and tools to explore Google Earth Engine. It is also designed for existing GEE users who would like to transition from the GEE JavaScript API to Python API. The automated JavaScript-to-Python conversion module of the geemap package can greatly reduce the time needed to convert existing GEE JavaScripts to Python scripts and Jupyter notebooks. For video tutorials and notebook examples, please visit https://github.com/giswqs/geemap/tree/master/examples. For complete documentation on geemap modules and methods, please visit https://geemap.readthedocs.io/en/latest/source/geemap.html. Features Below is a partial list of features available for the geemap package. Please check the examples page for notebook examples, GIF animations, and video tutorials. Automated conversion from Earth Engine JavaScripts to Python scripts and Jupyter notebooks. Displaying Earth Engine data layers for interactive mapping. Supporting Earth Engine JavaScript API-styled functions in Python, such as Map.addLayer(), Map.setCenter(), Map.centerObject(), Map.setOptions(). Creating split-panel maps with Earth Engine data. Retrieving Earth Engine data interactively using the Inspector Tool. Interactive plotting of Earth Engine data by simply clicking on the map. Converting data format between GeoJSON and Earth Engine. Using drawing tools to interact with Earth Engine data. Using shapefiles with Earth Engine without having to upload data to one's GEE account. Exporting Earth Engine FeatureCollection to other formats (i.e., shp, csv, json, kml, kmz) using only one line of code. Exporting Earth Engine Image and ImageCollection as GeoTIFF. Extracting pixels from an Earth Engine Image into a 3D numpy array. Calculating zonal statistics by group (e.g., calculating land over composition of each state/country). Adding a customized legend for Earth Engine data. Converting Earth Engine JavaScripts to Python code directly within Jupyter notebook. Adding animated text to GIF images generated from Earth Engine data. Adding colorbar and images to GIF animations generated from Earth Engine data. Creating Landsat timelapse animations with animated text using Earth Engine. Searching places and datasets from Earth Engine Data Catalog. Using timeseries inspector to visualize landscape changes over time. Exporting Earth Engine maps as HTML files and PNG images. Searching Earth Engine API documentation within Jupyter notebooks. Installation To use geemap, you must first sign up for a Google Earth Engine account. geemap is available on PyPI. To install geemap, run this command in your terminal: pip install geemap geemap is also available on conda-forge. If you have Anaconda or Miniconda installed on your computer, you can create a conda Python environment to install geemap: conda create -n gee python conda activate gee conda install -c conda-forge geemap If you have installed geemap before and want to upgrade to the latest version, you can run the following command in your terminal: pip install -U geemap If you use conda, you can update geemap to the latest version by running the following command in your terminal: conda update -c conda-forge geemap Usage Important note: A key difference between ipyleaflet and folium is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only (source). Note that Google Colab currently does not support ipyleaflet (source). Therefore, if you are using geemap with Google Colab, you should use import geemap.eefolium. If you are using geemap with binder or a local Jupyter notebook server, you can use import geemap, which provides more functionalities for capturing user input (e.g., mouse-clicking and moving). Youtube tutorial videos GitHub page of geemap Documentation While working on a small project I found this. This is a quite new library, some features shown in the tutorial may not work as intended but overall a very good package. The tools make the code much cleaner and readable. Searching EE docs from notebook is not yet implemented. Check out the youtube channel, it's great.
  2. 3 points
    Our objective is to provide the scientific and civil communities with a state-of-the-art global digital elevation model (DEM) derived from a combination of Shuttle Radar Topography Mission (SRTM) processing improvements, elevation control, void-filling and merging with data unavailable at the time of the original SRTM production: NASA SRTM DEMs created with processing improvements at full resolution NASA's Ice, Cloud,and land Elevation Satellite (ICESat)/Geoscience Laser Altimeter (GLAS) surface elevation measurements DEM cells derived from stereo optical methods using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data from the Terra satellite Global DEM (GDEM) ASTER products developed for NASA and the Ministry of Economy, Trade and Industry of Japan by Sensor Information Laboratory Corp National Elevation Data for US and Mexico produced by the USGS Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) developed by the USGS and the National Geospatial-Intelligence Agency (NGA) Canadian Digital Elevation Data produced by Natural Resources Canada We propose a significant modernization of the publicly- and freely-available DEM data. Accurate surface elevation information is a critical component in scientific research and commercial and military applications. The current SRTM DEM product is the most intensely downloaded dataset in NASA history. However, the original Memorandum of Understanding (MOU) between NASA and NGA has a number of restrictions and limitations; the original full resolution, one-arcsecond data are currently only available over the US and the error, backscatter and coherence layers were not released to the public. With the recent expiration of the MOU, we propose to reprocess the original SRTM raw radar data using improved algorithms and incorporating ancillary data that were unavailable during the original SRTM processing, and to produce and publicly release a void-free global one-arcsecond (~30m) DEM and error map, with the spacing supported by the full-resolution SRTM data. We will reprocess the entire SRTM dataset from raw sensor measurements with validated improvements to the original processing algorithms. We will incorporate GLAS data to remove artifacts at the optimal step in the SRTM processing chain. We will merge the improved SRTM strip DEMs, refined ASTER and GDEM V2 DEMs, and GLAS data using the SRTM mosaic software to create a seamless, void-filled NASADEM. In addition, we will provide several new data layers not publicly available from the original SRTM processing: interferometric coherence, radar backscatter, radar incidence angle to enable radiometric correction, and a radar backscatter image mosaic to be used as a layer for global classification of land cover and land use. This work leverages an FY12 $1M investment from NASA to make several improvements to the original algorithms. We validated our results with the original SRTM products and ancillary elevation information at a few study sites. Our approach will merge the reprocessed SRTM data with the DEM void-filling strategy developed during NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) 2006 project, "The Definitive Merged Global Digital Topographic Data Set" of Co-Investigator Kobrick. NASADEM is a significant improvement over the available three-arcsecond SRTM DEM primarily because it will provide a global DEM and associated products at one-arcsecond spacing. ASTER GDEM is available at one-arcsecond spacing but has true spatial resolution generally inferior to SRTM one-arcsecond data and has much greater noise problems that are particularly severe in tropical (cloudy) areas. At one-arcsecond, NASADEM will be superior to GDEM across almost all SRTM coverage areas, but will integrate GDEM and other data to extend the coverage. Meanwhile, DEMs from the Deutsches Zentrum für Luft- und Raumfahrt Tandem-X mission are being developed as part of a public-private partnership. However, these data must be purchased and are not redistributable. NASADEM will be the finest resolution, global, freely-available DEM products for the foreseeable future. data page: https://lpdaac.usgs.gov/products/nasadem_hgtv001/ news links: https://earthdata.nasa.gov/esds/competitive-programs/measures/nasadem
  3. 3 points
    Interesting application of WebGIS to plot Dinosaur database, and you can search how is your place in the past on the interactive globe Map. Welcome to the internet's largest dinosaur database. Check out a random dinosaur, search for one below, or look at our interactive globe of ancient Earth! Whether you are a kid, student, or teacher, you'll find a rich set of dinosaur names, pictures, and facts here. This site is built with PaleoDB, a scientific database assembled by hundreds of paleontologists over the past two decades. check this interactive webgis apps: https://dinosaurpictures.org/ancient-earth#170 official link: https://dinosaurpictures.org/
  4. 3 points
    link: https://press.anu.edu.au/publications/new-releases
  5. 3 points
    Interesting video on How Tos: WebOpenDroneMap is a friendly Graphical User Interfase (GUI) of OpenDroneMap. It enhances the capabilities of OpenDroneMap by providing a easy tool for processing drone imagery with bottoms, process status bars, and a new way to store images. WebODM allows to work by projects, so the user can create different projects and process the related images. As a whole, WebODM in Windows is a implementation of PostgresSQL, Node, Django and OpenDroneMap and Docker. The software instalation requires 6gb of disk space plus Docker. It seem huge but it is the only way to process drone imagery in Windows using just open source software. We definitely see a huge potential of WebODM for the image processing, therefore we have done this tutorial for the installation and we will post more tutorial for the application of WebODM with drone images. For this tutorial you need Docker Toolbox installed on your computer. You can follow this tutorial to get Docker on your pc: https://www.hatarilabs.com/ih-en/tutorial-installing-docker You can visit the WebODM site on GitHub: https://github.com/OpenDroneMap/WebODM Videos The tutorial was split in three short videos. Part 1 https://www.youtube.com/watch?v=AsMSoWAToxE Part 2 https://www.youtube.com/watch?v=8GKx3fz0qgE Part 3 https://www.youtube.com/watch?v=eCZFzaXyMmA
  6. 3 points
    7th International Conference on Computer Science and Information Technology (CoSIT 2020) January 25 ~ 26, 2020, Zurich, Switzerland https://cosit2020.org/ Scope & Topics 7th International Conference on Computer Science and Information Technology (CoSIT 2020) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computer Science, Engineering and Information Technology. The Conference looks for significant contributions to all major fields of the Computer Science and Information Technology in theoretical and practical aspects. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe · Geographical Information Systems/ Global Navigation Satellite Systems (GIS/GNSS) Paper Submission Authors are invited to submit papers through the conference Submission system. Here’s where you can reach us : [email protected] or [email protected]
  7. 3 points
    The first thing to do before mapping is to set up the camera parameters. Before to set up camera parameters, recommended resetting the all parameters on camera first. To set camera parameters manually need to set to manual mode. Image quality: Extra fine Shutter speed: to remove blur from photo shutter speed should be set for higher value. 1200–1600 is recommended. Higher the shutter speed reduce image quality . if there is blur in the image increase shutter speed ISO: lower the ISO higher image quality. ISO between 160–300 is recommended. if there is no blur but image quality is low, reduce ISO. Focus: Recommended to set the focus manually on the ground before a flight. Direct camera to an object which is far, and slightly increase the focus, you will see on camera screen that image sharpness changes by changing the value. Set the image sharpness at highest. (slide the slider close to infinity point on the screen you will see the how image sharpness changes by sliding) White balance: recommended to set to auto. On surveying mission Sidelap, Overlap, Buffer have to be set higher to get better quality surveying result. First set the RESOLUTION which you would like to get for your surveying project. When you change resolution it changes flight altitude and also effects the coverage in a single flight. Overlap: 70% This will increase the number of photos taken during each flight line. The camera should be capable to capture faster. Sidelap: recommended 70% Flying with higher side-lap between each line of the flight is a way to get more matches in the imagery, but it also reduces the coverage in a single flight Buffer: 12% Buffer increases the flight plane to get more images from borders. It will improve the quality of the map source: https://dronee.aero/blogs/dronee-pilot-blog/few-things-to-set-correctly-to-get-high-quality-surveying-results
  8. 3 points
    The GeoforGood Summit 2019 drew its curtains close on 19 Sep 2019 and as a first time attendee, I was amazed to see the number of new developments announced at the summit. The summit — being a first of its kind — combined the user summit and the developers summit into one to let users benefit from the knowledge of new tools and developers understand the needs of the user. Since my primary focus was on large scale geospatial modeling, I attended the workshops and breakout sessions related to Google Earth Engine only. With that, let’s look at 3 new exciting developments to hit Earth Engine Updated documentation on machine learning Documentation really? Yes! As an amateur Earth Engine user myself, my number one complaint of the tool has been its abysmal quality of documentation spread between its app developers site, Google Earth’s blog, and their stack exchange answers. So any updates to the documentation is welcome. I am glad that the documentation has been updated to help the ever-exploding user base of geospatial data scientists interested in implementing machine learning and deep learning models. The documentation comes with its own example Colab notebooks. The Example notebooks include supervised classification, unsupervised classification, dense neural network, convolutional neural network, and deeplearning on Google Cloud. I found that these notebooks were incredibly useful to me to get started as there are quite a few non-trivial data type conversions ( int to float32 and so on) in the process flow. Earth Engine and AI Platform Integration Nick Clinton and Chris Brown jointly announced the much overdue Earth Engine + Google AI Platform integration. Until now, users were essentially limited to running small jobs on Google Colab’s virtual machine (VM) and hoping that the connection with the VM doesn’t time out (which usually lasts for about 4 hours). Other limitations include lack of any task monitoring or queuing capabilities. Not anymore! The new ee.Model() package let’s users communicate with a Google Cloud server that they can spin up based on their own needs. Needless to say, this is a HUGE improvement over the previous primitive deep learning support provided on the VM. Although it was free, one could simply not train, validate, predict, and deploy any model larger than a few layers. It had to be done separately on the Google AI Platform once the .TFRecord objects were created in their Google bucket. With this cloud integration, that task has been simplified tremendously by letting users run and test their models right from the Colab environment. The ee.Model() class comes with some useful functions such as ee.Model.fromAIPlatformPredictor() to make predictions on Earth Engine data directly from your model sitting on Google Cloud. Lastly, since your model now sits in the AI Platform, you can cheat and use your own models trained offline to predict on Earth Engine data and make maps of its output. Note that your model must be saved using tf.contrib.saved_model format if you wish to do so. The popular Keras function model.save_model('model.h5') is not compatible with ee.Model(). Moving forward, it seems like the team plans to stick to the Colab Python IDE for all deep learning applications. However, it’s not a death blow for the loved javascript code editor. At the summit, I saw that participants still preferred the javascript code editor for their non-neural based machine learning work (like support vector machines, random forests etc.). Being a python lover myself, I too go to the code editor for quick visualizations and for Earth Engine Apps! I did not get to try out the new ee.Model() package at the summit but Nick Clinton demonstrated a notebook where a simple working example has been hosted to help us learn the function calls. Some kinks still remain in the development— like limiting a convolution kernel to only 144 pixels wide during prediction because of “the way earth engine communicates with cloud platform” — but he assured us that it will be fixed soon. Overall, I am excited about the integration because Earth Engine is now a real alternative for my geospatial computing work. And with the Earth Engine team promising more new functions in the ee.Model() class, I wonder if companies and labs around the world will start migrating their modeling work to Earth Engine. Cooler Visualizations! Matt Hancher and Tyler Erickson displayed some new functionality related to visualizations and I found that it made it vastly simpler to make animated visuals. With ee.ImageCollection.getVideoThumbURL() function, you can create your own animated gifs within a few seconds! I tried it on a bunch of datasets and the speed of creating the gifs was truly impressive. Say bye to exporting each iteration of a video to your drive because these gifs appear right at the console using the print() command! Shown above is an example of global temperature forecast by time from the ‘NOAA/GFS0P25’ dataset. The code for making the gif can be found here. The animation is based on the example shown in the original blog post by Michael DeWitt and I referred to this gif-making tutorial on the developers page to make it. I did not get to cover all the new features and functionality introduced at the summit. For that, be on the lookout for event highlights on Google Earth’s blog. Meanwhile, you can check out the session resources from the summit for presentations and notebooks on topics that you are interested in. Presentation and resources Published in Medium
  9. 3 points
    Details geological-geophysical aspects of groundwater treatment Discusses regulatory legislations regarding groundwater utilization Serves as a reference material for scientists in geology, geophysics and environmental studies
  10. 2 points
    the satellites from planet can now take imagery at 50cm, they changed their orbit in order to achieve better GSD SKYSAT IMAGERY NOW AVAILABLE Bring agility to your organization with the latest advancements in high-resolution SkySat imagery, available today. Make targeted decisions in ever-changing operational contexts with improved 50 cm spatial resolution and more transparency in the ordering process with the new Tasking Dashboard.
  11. 2 points
    for those like me who are not English mother tongue I recommend this site for translations (English - French - German - Italian - Spanish - Portuguese - Russian - Chinese - Japanese etc.)... fantastic and intuitive that is based on artificial intelligence https://www.deepl.com/ another interesting website https://www.linguee.com/
  12. 2 points
    A new set of 10 ArcGIS Pro lessons empowers GIS practitioners, instructors, and students with essential skills to find, acquire, format, and analyze public domain spatial data to make decisions. Described in this video, this set was created for 3 reasons: (1) to provide a set of analytical lessons that can be immediately used, (2) to update the original 10 lessons created by my colleague Jill Clark and I to provide a practical component to our Esri Press book The GIS Guide to Public Domain Data, and (3) to demonstrate how ArcGIS Desktop (ArcMap) lessons can be converted to Pro and to reflect upon that process. The activities can be found here. This essay is mirrored on the Esri GeoNet education blog and the reflections are below and in this video. Summary of Lessons: Can be used in full, in part, or modified to suit your own needs. 10 lessons. 64 work packages. A “work package” is a set of tasks focused on solving a specific problem. 370 guided steps. 29 to 42 hours of hands-on immersion. Over 600 pages of content. 100 skills are fostered, covering GIS tools and methods, working with data, and communication. 40 data sources are used, covering 85 different data layers. Themes covered: climate, business, population, fire, floods, hurricanes, land use, sustainability, ecotourism, invasive species, oil spills, volcanoes, earthquakes, agriculture. Areas covered: The Globe, and also: Brazil, New Zealand, the Great Lakes of the USA, Canada, the Gulf of Mexico, Iceland, the Caribbean Sea, Kenya, Orange County California, Nebraska, Colorado, and Texas USA. Aimed at university-level graduate and university or community college undergraduate student. Some GIS experience is very helpful, though not absolutely required. Still, my advice is not to use these lessons for students’ first exposure to GIS, but rather, in an intermediate or advanced setting. How to access the lessons: The ideal way to work through the lessons is in a Learn Path which bundle the readings of the book’s chapters, selected blog essays, and the hands-on activities.. The Learn Path is split into 3 parts, as follows: Solving Problems with GIS and public domain geospatial data 1 of 3: Learn how to find, evaluate, and analyze data to solve location-based problems through this set of 10 chapters and short essay readings, and 10 hands-on lessons: https://learn.arcgis.com/en/paths/the-gis-guide-to-public-domain-data-learn-path/ Solving Problems with GIS and public domain geospatial data 2 of 3: https://learn.arcgis.com/en/paths/the-gis-guide-to-public-domain-data-learn-path-2/ Solving Problems with GIS and public domain geospatial data 3 of 3: https://learn.arcgis.com/en/paths/the-gis-guide-to-public-domain-data-learn-path-3/ The Learn Paths allow for content to be worked through in sequence, as shown below: You can also access the lessons by accessing this gallery in ArcGIS Online, shown below. If you would like to modify the lessons for your own use, feel free! This is why the lessons have been provided in a zipped bundle as PDF files here and as MS Word DOCX files here. This video provides an overview. source: https://spatialreserves.wordpress.com/2020/05/14/10-new-arcgis-pro-lesson-activities-learn-paths-and-migration-reflections/
  13. 2 points
    Stop me if you’ve heard this before. DJI has introduced its latest enterprise powerhouse drone, the DJI Matrice 300 RTK. We learned a lot about the drone earlier this week due to a few huge leaks of specs, features, photos, and videos. But it’s worth looking at the drone again now that it’s official – and an incredible intro video. Also called the M300 RTK, this drone is an upgrade in every way over its predecessor, the M200 V2. That includes a very long flight time of 55 minutes, six-direction obstacle avoidance, and a doubled (6 pound) payload capability. That allows it to carry a range of powerful cameras, which we’ll get to in a bit. The drone is also built for weather extremes. IP45 weather sealing keeps out rain and dust. And a self-heating battery helps the drone to run in a broad range of temperatures, from -4 to 122 Fahrenheit. The DJI Matrice 300 RTK can fly up to 15 kilometers (9.3 miles) from its controller and still stream 1080p video back home. That video and other data can be protected using AES-256 encryption. The drone can also be flown by two co-pilots, with one able to take over for the other if any problem arises or a handoff scenario. A workhorse inspection drone All these capabilities are targeted to the DJI Matrice 300 RTK’s purpose as a drone for heavy-duty visual inspection and data collection work, such as surveys of power lines or railways. In fact, it incorporates many advanced camera features for the purpose. Smart inspection is a new set of features to optimize data collection. It includes live mission recording, which allows the drone to record every aspect of a flight, even camera settings. This allows workers to train a drone on an inspection mission that it will repeat again and again. With AI spot check, operators can mark the specific part of the photo, such as a transformer, that is the subject of inspection. AI algorithms compare that to what the camera sees on a future flight, so that it can frame the subject identically on every flight. An inspection drone is only as good as its cameras, and the M300 RTK offers some powerful options from DJI’s Zenmuse H20 series. The first option is a triple-camera setup. It includes a 20-megapixel, 23x zoom camera; a 12MP wide-angle camera; and a laser rangefinder that measures out to 1,200 meters (3,937 feet). The second option adds a radiometric thermal camera. TO make things simpler for operators, the drone provides a one-click capture feature that grabs videos or photos from three cameras at once, without requiring the operator to switch back and forth. Eyes and ears ready for danger With its flight time and range, the DJI Matrice 300 RTK could be flying some long, complex missions, easily beyond visual line of site (if its owner gets an FAA Part 107 waiver for that). This requires some solid safety measures. While the M200 V2 has front-mounted sensors, the M300 RTK has sensors in six directions for full view of the surroundings. The sensors can register obstacles up to 40 meters (98 feet) away. Like all new DJI drones, the M300 RTK also features the company’s AirSense technology. An ADS-B receiver picks up signals from manned aircraft that are nearby and alerts the drone pilot of their location. It’s been quite a few weeks for DJI. On April 27, it debuted its most compelling consumer drone yet, the Mavic Air 2. Now it’s showing off its latest achievement at the other end of the drone spectrum with the industrial grade Matrice 300 RTK. These two, very different drones help illustrate the depth of product that comes from the world’s biggest drone maker. And the company doesn’t show signs of slowing down, despite the COVID-19 economic crisis. Next up, we suspect, will be a revision to its semi-pro quadcopter line in the firm of a Mavic 3. It is available at DJI.It’s been quite a few weeks for DJI. On April 27, it debuted its most compelling consumer drone yet, the Mavic Air 2. Now it’s showing off its latest achievement at the other end of the drone spectrum with the industrial grade Matrice 300 RTK. These two, very different drones help illustrate the depth of product that comes from the world’s biggest drone maker. And the company doesn’t show signs of slowing down, despite the COVID-19 economic crisis. Next up, we suspect, will be a revision to its semi-pro quadcopter line in the firm of a Mavic 3. It is available at DJI. source: https://dronedj.com/2020/05/07/dji-matrice-300-rtk-drone-official/
  14. 2 points
    @intertronic, thanks for your input. I found a solution that suite my case better due to the fact that we are using both version of QGIS and also because I was looking for interoperability. Therefore I have decided to use QSphere. Most probably not well known around the globe. https://qgis.projets.developpement-durable.gouv.fr/projects/qsphere GUI quiete ugly but at least is doing the job. 😉 darksabersan
  15. 2 points
    DRONE MAKER DJI announced an update to its popular Mavic Air quadcopter today. The Mavic Air 2 will cost $799 when it ships to US buyers in late May. That's the same price as the previous Mavic Air model, so the drone stays as DJI's mid-range option between its more capable Mavic 2 and its smaller, cheaper Mavic Mini. The Mavic Air 2 is still plenty small, but the new version has put on some weight. DJI says that testing and consumer surveys suggested that most people don't mind lugging a few extra grams in exchange for a considerable upgrade in flight time and, presumably, better handling in windy conditions. Even better, thanks to a new rotor design and other aerodynamic improvements, DJI is claiming the Mavic Air 2 can remain aloft for 34 minutes—a big jump from the 21 minutes of flight time on the original Mavic Air. The Camera Eye he big news in this update is the new larger imaging sensor on the drone's camera. The Mavic Air 2's camera ships with a half-inch sensor, up from the 1 2/3-inch sensor found in the previous model. That should mean better resolution and sharper images, especially because the output specs haven't changed much. The new camera is still outputting 12-megapixel stills, but now has a bigger sensor to fill that frame with more detail. There's also a new composite image option that joins together multiple single shots into a large, 48-megapixel image. On the video side, there's some exciting news. The Mavic Air 2 is DJI's first drone to offer 4K video at 60 frames per second and 120 Mbps—previous DJI drones topped out at 30 fps when shooting in full 4K resolution. There are also slow-motion modes that slow down footage to four times slower than real life (1080p at 120 fps), or eight-times slower (1080 at 240 fps). Combine those modes with the more realistic contrast you get with the HDR video standard, and you have considerably improved video capabilities in a sub-$1,000 drone. More interesting in some ways is DJI's increasing forays into computational photography, which the company calls Smart Photo mode. Flip on Smart Photo and the Mavic Air 2 will do scene analysis, tap its machine intelligence algorithm and automatically choose between a variety of photo modes. There's a scene recognition mode where the Mavic Air 2 sets the camera up to best capture one of a variety of scenarios you're likely to encounter with drone photography, including blue skies, sunsets, snow, grass, and trees. In each case, exposure is adjusted to optimize tone and detail. The second Smart Photo mode is dubbed Hyperlight, which handles low-light situations. To judge by DJI's promo materials, this is essentially an HDR photography mode specifically optimized for low-light scenes. It purportedly cuts noise and produces more detailed images. The final smart mode is HDR, which takes seven images in rapid succession, the combines elements of each to make a final image with a higher dynamic range. One last note about the camera: The shape of the camera has changed, so if you have any lenses or other accessories for previous DJI drones, they won't attach to the Air 2. Automatic Flight for the People If you dig through older YouTube videos there's a ton of movies that play out like this: unbox new drone, head outside, take off, tree gets closer, closer, closer, black screen. Most of us just aren't that good at flying, and the learning curve can be expensive and steep. Thankfully drone companies began automating away most of what's difficult about piloting a quadcopter, and DJI is no exception. The company has added some new automated flight tricks to the Air's arsenal. DJI's Active Track has been updated to version 3.0, which brings better subject recognition algorithms and some new 3D mapping tricks to make it easier to automatically track people through a scene, keeping the camera on the subject as the drone navigates overhead to stay with them. DJI claims the Point of Interest mode—which allows you to select an object and fly around it in a big circle while the camera stays pointed at the subject—is better at tracking some of the objects that previous versions struggled with, like vehicles or even people. The most exciting new flight mode is Spotlight, which comes from DJI's high-end Inspire drone used by professional photographers and videographers to carry their DSLR cameras into the sky. Similar to the Active Track mode, Spotlight keeps the camera pointed a moving subject. But while Active Track automates the drone's flight, the new Spotlight mode allows the human pilot to retain control of the flight path for more complex shots. Finally, the range of the new Mavic Air 2 has been improved, and it can now wander an impressive six miles away from the pilot in ideal conditions. The caveat here is that you should always maintain visual contact with your drone for safety reasons. However, you aren't going to be able to see the Mavic Air 2 when it's two miles away, let alone six. Despite a dearth of competitors, DJI continues to put out new drones and improve its lineup as it progresses. The Mavic Air 2 looks like an impressive update to what was already one of our favorite drones, especially considering several features—the 60 fps 4K video and 34 minute flight time—even best those found on the more expensive Mavic 2 Pro. links: https://www.dji.com/id/mavic-air-2
  16. 2 points
    I like drones but just got more interested in this,
  17. 2 points
    Harvard Online Courses Advance your career. Pursue your passion. Keep learning. links: https://online-learning.harvard.edu/CATALOG/FREE
  18. 2 points
  19. 2 points
    Saw a similar news last month - Using Machine Learning to “Nowcast” Precipitation in High Resolution by Google. The result seemed pretty good. Here, A visualization of predictions made over the course of roughly one day. Left: The 1-hour HRRR prediction made at the top of each hour, the limit to how often HRRR provides predictions. Center: The ground truth, i.e., what we are trying to predict. Right: The predictions made by our model. Our predictions are every 2 minutes (displayed here every 15 minutes) at roughly 10 times the spatial resolution made by HRRR. Notice that we capture the general motion and general shape of the storm. The two method seem similar.
  20. 2 points
    With Huawei basically blocked from using Google services and infrastructure, the firm has taken steps to replace Google Maps on its hardware by signing a partnership with TomTom to provide maps, navigation, and traffic data to Huawei apps. Reuters reports that Huawei is entering this partnership with TomTom as the mapping tech company is based in the Netherlands — therefore side-stepping the bans on working with US firms. TomTom will provide the Chinese smartphone manufacturer with mapping, live traffic data, and software on smartphones and tablets. TomTom spokesman Remco Meerstra confirmed to Reuters that the deal had been closed some time ago but had not been made public by the company. This comes as TomTom unveiled plans to move away from making navigation hardware and will focus more heavily on offering software services — making this a substantial step for TomTom and Huawei. While TomTom doesn’t quite match the global coverage and update speed of Google Maps, having a vital portion of it filled by a dedicated navigation and mapping firm is one step that might appease potential global Huawei smartphone buyers. There is no denying the importance of Google app access outside of China but solid replacements could potentially make a huge difference — even more so if they are recognizable by Western audiences. It’s unclear when we may see TomTom pre-installed on Huawei devices but we are sure that this could be easily added by way of an OTA software update. The bigger question remains if people are prepared to switch from Google Maps to TomTom for daily navigation. resource: https://9to5google.com/2020/01/20/huawei-tomtom/
  21. 2 points
    January 3, 2020 - Recent Landsat 8 Safehold Update On December 19, 2019 at approximately 12:23 UTC, Landsat 8 experienced a spacecraft constraint which triggered entry into a Safehold. The Landsat 8 Flight Operations Team recovered the satellite from the event on December 20, 2019 (DOY 354). The spacecraft resumed nominal on-orbit operations and ground station processing on December 22, 2019 (DOY 356). Data acquired between December 22, 2019 (DOY 356) and December 31, 2019 (DOY 365) exhibit some increased radiometric striping and minor geometric distortions (see image below) in addition to the normal Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) alignment offset apparent in Real-Time tier data. Acquisitions after December 31, 2019 (DOY 365) are consistent with pre-Safehold Real-Time tier data and are suitable for remote sensing use where applicable. All acquisitions after December 22, 2019 (DOY 356) will be reprocessed to meet typical Landsat data quality standards after the next TIRS Scene Select Mirror (SSM) calibration event, scheduled for January 11, 2020. Landsat 8 Operational Land Imager acquisition on December 22, 2019 (path 148/row 044) after the spacecraft resumed nominal on-orbit operations and ground station processing. This acquisition demonstrates increased radiometric striping and minor geometric distortions observed in all data acquired between December 22, 2019 and December 31, 2019. All acquisitions after December 22, 2019 will be reprocessed on January 11, 2020 to achieve typical Landsat data quality standards. Data not acquired during the Safehold event are listed below and displayed in purple on the map (click to enlarge). Map displaying Landsat 8 scenes not acquired from Dec 19-22, 2019 Path 207 Rows 160-161 Path 223 Rows 60-178 Path 6 Rows 22-122 Path 22 Rows 18-122 Path 38 Rows 18-122 Path 54 Rows 18-214 Path 70 Rows 18-120 Path 86 Rows 24-110 Path 102 Rows 19-122 Path 118 Rows 18-185 Path 134 Rows 18-133 Path 150 Rows 18-133 Path 166 Rows 18-222 Path 182 Rows 18-131 Path 198 Rows 18-122 Path 214 Rows 34-122 Path 230 Rows 54-179 Path 13 Rows 18-122 Path 29 Rows 20-232 Path 45 Rows 18-133 After recovering from the Safehold successfully, data acquired on December 20, 2019 (DOY 354) and from most of the day on December 21, 2019 (DOY 355) were ingested into the USGS Landsat Archive and marked as "Engineering". These data are still being assessed to determine if they will be made available for download to users through all USGS Landsat data portals. source: https://www.usgs.gov/land-resources/nli/landsat/january-3-2020-recent-landsat-8-safehold-update
  22. 2 points
    just found this interesting articles on Agisoft forum : source: https://www.agisoft.com/forum/index.php?topic=7851.0
  23. 2 points
    one of my favorite image hosting, , this is their announcement : Rest in Peace TinyPic
  24. 2 points
    not necessary to excel environment but : https://github.com/orbisgis/h2gis/wiki/4.2-LibreOffice
  25. 1 point
    Hi Everyone, July 13–16, 2020 | The world’s largest, virtual GIS event (FREE this year) The 2020 Esri User Conference (Esri UC) is a completely virtual event designed to give users and students an interactive, online experience with Esri and the GIS community. Participate in sessions and view presentations that offer geospatial solutions, browse the online Map Gallery, watch the Plenary Session, and much more. Registration here : https://www.esri.com/en-us/about/events/uc/overview Enjoy
  26. 1 point
    The big news in the geospatial world at the moment is Facebook’s acquisition of Mapillary. For those unfamiliar, Mapillary is a darling of the mapping world and one of the highest-profile geospatial startups of the last decade—launched in 2013, their mission was to create a global street-level imagery dataset to rival Google Street View. Mapillary was the prototypical “venture-scale” business — preposterously ambitious, technically impressive, inarguably valuable for the world, and plausibly monetizable. What Mapillary accomplished in a short seven years is simply staggering. Google, with an enormous head start and untold resources at the ready to support Street View announced last year that they’ve collected over ten million miles of street imagery. Mapillary, on the other hand, crossed three million miles of mapped streets in 2018 and has more than doubled the number of images in their catalog in the years since (to over one billion!), putting them squarely in the same conversation as Google. That’s an insane accomplishment for any company, let alone a startup who, over its lifetime, raised ~$25M or about half of the annual compensation package for a typical member of Google’s C-Suite. In addition, they’ve created some stuff I deeply admire as a member of the broader open source geospatial community. Two quick examples: OpenSfM, a popular computer vision engine for stitching together overlapping images to reconstruct places in 3D. Vista, a free 25K image dataset labeled for semantic segmentation — one of the largest such open datasets in existence. What is Facebook Up To, Exactly? If you predicted Facebook would acquire Mapillary, congratulations — you are probably alone. Apart from a public image that stands in cartoonish opposition to Mapillary’s ethos of grassroots community building and radical openness, Facebook doesn’t really do maps, do they? Google, Apple, and Microsoft have invested billions into consumer mapping applications and acquired numerous companies to support those efforts. Any of those three behemoths would have felt like natural landing spots for Mapillary. But Facebook? What’s that about? Unless you are already tapped into the seedy underworld known as the “geospatial industry,” you can be forgiven for not knowing that Facebook actually does do maps. In this incredible paper¹ released j̶u̶s̶t̶ ̶l̶a̶s̶t̶ ̶m̶o̶n̶t̶h̶ just over a year ago², researchers found that Facebook has contributed over 800,000 kilometers of mapped roads to OpenStreetMap (if you’re unfamiliar, it’s one of the largest crowdsourcing projects in history). They rank third in kilometers mapped behind Mapbox/Development Seed (1.69M) and Apple (1.64M). And beyond directly contributing cartographic features to OSM, they’ve publicly released an open source, AI-assisted road mapping tool called RapID that is an impressive thing to witness in action. They also support the OSM Foundation at the highest corporate giving level and have had a formidable presence at the annual OSM conference the past few years. Still — the mere fact Facebook has dipped its gargantuan toes into the mappy water doesn’t explain why they would bother with acquiring Mapillary. complete story: https://medium.com/@joemorrison/why-on-earth-did-facebook-just-acquire-mapillary-9838405272f8
  27. 1 point
    The United States Space Force’s GPS III program reached another milestone with the successful core mate of GPS III Space Vehicle 08 at Lockheed Martin’s GPS III Processing Facility in Waterton, Colorado, April 15. With core mate complete, the space vehicle was named in honor of NASA trailblazer and “hidden figure” Katherine Johnson. The two-day core mate consisted of using a 10-ton crane to lift and complete a 90-degree rotation of the satellite’s system module, and then slowly lowering the system module onto the satellite’s vertical propulsion core. The two mated major subsystems come together to form an assembled GPS III space vehicle. Despite the COVID-19 pandemic, the Space and Missile Systems Center (SMC) and its mission partner Lockheed Martin ensured that SV08 core mate took place, in accordance with all Centers for Disease Control and local guidelines to minimize exposure or transmission of COVID-19. The GPS III Processing Facility’s cleanroom high bay was restricted to only key personnel directly supporting the operation. “Core mate is the most critical of the GPS space vehicle single-line-flow operations,” said Lt. Col. Margaret Sullivan, program manager and materiel lead for the GPS III program. “Despite the restrictions presented by the COVID-19 pandemic, our team adapted and worked tirelessly to achieve this essential milestone.” Katherine Johnson. When the core mate operation is successfully completed, a GPS III satellite is said to be “born.” In keeping with the team’s tradition of naming GPS III satellites after famous explorers and pioneers, SV08 was named “Katherine Johnson” in honor of the trailblazing NASA mathematician and “human computer” who designed and computed orbital trajectories for NASA’s Mercury, Apollo and space shuttle missions. One of four African-American women at the center of the nonfiction book by Margot Lee Shetterly and the movie Hidden Figures, Johnson was awarded the Presidential Medal of Freedom in 2015 for her groundbreaking contributions to the U.S. space program. Other GPS III satellites have been named in honor of explorers including GPS III SV01 “Vespucci” after Amerigo Vespucci; GPS III SV02 “Magellan” after Ferdinand Magellan; and GPS III SV03 “Columbus” after Christopher Columbus. Next up, performance tests. The next step for the newly christened “Katherine Johnson” is the post-mate Systems Performance Test (SPT) scheduled to begin in August. SPT electrically tests the performance of the satellite during the early phase of build and provides a baseline test data set to be compared to post-environmental test data. GPS III SV08 is currently scheduled to launch in 2022. GPS III is the most powerful GPS satellite ever developed. It is three times more accurate and provides up to eight times improved anti-jamming capability over previous GPS satellites on orbit. GPS III brings new capabilities to users as a fourth civilian signal (L1C), designed to enable interoperability between GPS and international satellite navigation systems, such as Europe’s Galileo system. GPS III satellites will also bring the full capability of the Military Code (M-code) signal, increasing anti-jam resiliency in support of the warfighter. These continued improvements and advancements to the GPS system makes it the premier space-based provider of positioning, navigation, and timing services for more than four billion worldwide. GPS III SV03 to Launch June 30. Launched in December 2018 and August 2019, GPS III SV01 and SV02 became part of today’s operational constellation of 31 satellites, on January 13 and April 1, 2020 respectively. GPS III SV03 is scheduled to launch on June 30. The SMC, located at the Los Angeles Air Force Base, California, is the center of excellence for acquiring and developing military space systems. Its portfolio includes the GPS, military satellite communications, defense meteorological satellites, space launch and range systems, satellite control networks, space based infrared systems, and space situational awareness capabilities. source: https://www.gpsworld.com/gps-iii-sv-08-born-with-core-mate-complete-named-katherine-johnson/
  28. 1 point
    Hi, I wanted to say hello to all. I am making maps. I am using Illustrator plus MaPublisher mostly, Photoshop, Manifold and Global Mapper. I prefer to use many small programs instead one big monster ArcGIS, but it is a question of taste I think. Besides that also Erdas and eCognition sometimes. Thank you all for great software here.
  29. 1 point
    GRASS GIS was, for a long time, something I dismissed as ‘too complex’ for my everyday geospatial operations. I formulated any number of excuses to work around the software and could not be convinced it had practical use in my daily work. It was ‘too hard to set-up’, ‘never worked well with QGIS’, and ‘made my scripting processes a nightmare’. In this example we will: part 1: 1. Download a small piece of elevation data from the LINZ Data Service 2. Build a GRASS environment to process these data 3. Build a BASH script to process the catchments 4. Import the elevation into the GRASS environment 5. Perform some basic GRASS operations (fill and watershed) 6. Export raster format for viewing 7. Export the vector catchments to shapefile part 2: 1. Creating multiple watershed boundaries of different sizes with GRASS and using a basic loop in BASH for the process. 2. Clipping the original raster by the watershed boundaries using GDAL and SQL with a basic loop in BASH. links: part 1: https://xycarto.com/2020/05/03/basic-grass-gis-with-bash/ part 2: https://xycarto.com/2020/05/05/basic-grass-gis-with-bash-plus-gdal/ source code: https://github.com/xycarto/xycarto_code/tree/master/scripts/grass/GRASS_BASH_blog
  30. 1 point
    Changes in ocean circulation may have caused a shift in Atlantic Ocean ecosystems not seen for the past 10,000 years, new analysis of deep-sea fossils has revealed. This is the striking finding of a new study led by a research group I am part of at UCL, funded by the ATLAS project and published in the journal Geophysical Research Letters. The shift has likely already led to political tensions as fish migrate to colder waters. The climate has been quite stable over the 12,000 years or so since the end of the last Ice Age, a period known as the Holocene. It is thought that this stability is what allowed human civilisation to really get going. In the ocean, the major currents are also thought to have been relatively stable during the Holocene. These currents have natural cycles, which affect where marine organisms can be found, including plankton, fish, seabirds and whales. Yet climate change in the ocean is becoming apparent. Tropical coral reefs are bleaching, the oceans becoming more acidic as they absorb carbon from the atmosphere, and species like herring or mackerel are moving towards the poles. But there still seems to be a prevailing view that not much has happened in the ocean so far – in our minds the really big impacts are confined to the future. Looking into the past To challenge this point of view, we had to look for places where seabed fossils not only covered the industrial era in detail, but also stretched back many thousands of years. And we found the right patch of seabed just south of Iceland, where a major deep sea current causes sediment to pile up in huge quantities. To get our fossil samples we took cores of the sediment, which involves sending long plastic tubes to the bottom of the ocean and pushing them into the mud. When pulled out again, we were left with a tube full of sediment that can be washed and sieved to find fossils. The deepest sediment contains the oldest fossils, while the surface sediment contains fossils that were deposited within the past few years. One of the simplest ways of working out what the ocean was like in the past is to count the different species of tiny fossil plankton that can be found in such sediments. Different species like to live in different conditions. We looked at a type called foraminifera, which have shells of calcium carbonate. Identifying them is easy to do using a microscope and small paintbrush, which we use when handling the fossils so they don't get crushed. A recent global study showed that modern foraminifera distributions are different to the start of the industrial era. Climate change is clearly already having an impact. Similarly, the view that modern ocean currents are like those of the past couple of thousand years was challenged by our work in 2018, which showed that the overturning "conveyor belt" circulation was at its weakest for 1,500 years. Our new work builds on this picture and suggests that modern North Atlantic surface circulation is different to anything seen in the past 10,000 years – almost the whole Holocene. The effects of the unusual circulation can be found across the North Atlantic. Just south of Iceland, a reduction in the numbers of cold-water plankton species and an increase in the numbers of warm-water species shows that warm waters have replaced cold, nutrient-rich waters. We believe that these changes have also led to a northward movement of key fish species such as mackerel, which is already causing political headaches as different nations vie for fishing rights. Further north, other fossil evidence shows that more warm water has been reaching the Arctic from the Atlantic, likely contributing to melting sea ice. Further west, a slowdown in the Atlantic conveyor circulation means that waters are not warming as much as we would expect, while furthest west close to the US and Canada the warm gulf stream seems to be shifting northwards which will have profound consequences for important fisheries. One of the ways that these circulation systems can be affected is when the North Atlantic gets less salty. Climate change can cause this to happen by increasing rainfall, increasing ice melt, and increasing the amount of water coming out of the Arctic Ocean. Melting following the peak of the Little Ice Age in the mid 1700s may have triggered an input of freshwater, causing some of the earliest changes that we found, with modern climate change helping to propel those changes beyond the natural variability of the Holocene. We still don't know what has ultimately caused these changes in ocean circulation. But it does seem that the ocean is more sensitive to modern climate changes than previously thought, and we will have to adapt. source: https://www.sciencealert.com/fossils-reveal-our-ocean-is-changing-in-a-ways-it-hasn-t-for-10-000-years
  31. 1 point
    6x open positions (as of 24.04.2020) French site (simple put "GIS" or "Swisstopo" in the search area) https://www.stelle.admin.ch/stelle/fr/home/stellen/stellenangebot.html German https://www.stelle.admin.ch/stelle/de/home/stellen/stellenangebot.html
  32. 1 point
    or another swiss/french site : https://www.camptocamp.com/en/jobs/
  33. 1 point
    In March, the U.S. government received an unusual inquiry about GPS disruptions. It was from a user in Iran reporting what appeared to be “circle spoofing” — a phenomenon that had only previously been observed in China. “Some of GPS devices received fake signal and show the fake valid location. Yesterday I test a device, it can get signal and give real position. After 10 minutes the device show moving around a big circle in tehran by 35 km/h speed. I can’t fix this problem by restarting the device. “The GPS module time is correct but the location is not. I attach Excel file of data and map of the track. I can’t get any response from Communications Regulatory Authority (CRA) of The I.R. of Iran. Do you know about this?” A little internet research showed that the spoofing was taking place at or near Iran’s “AJA University of Command and Staff,” formerly called the “War University.” It is the staff college for Iran’s Army. Reports to the U.S. government about GPS disruption are normally listed on the U.S. Coast Guard’s Navigation Center website. This one has not been posted. Coast Guard officials said that it is because the report was received by another agency and did not contain sufficient information. Attempts by Coast Guard personnel to contact the reporting source for more information to enable the report to be posted were unsuccessful. GPS spoofing is often easiest to detect in maritime areas. Ship automatic identification system (AIS) transmissions include location data and are detected by satellite. The data is then aggregated and used by various companies for a number of applications. Viewing ship location reports over time has revealed thousands of ship receivers spoofed to airports in Russia, and hundreds spoofed into circles (presumably around the spoofing device) in China. Clearly, though, any system that aggregates and displays GPS location data can help detect wide area spoofing activity. Strava is a mobile app for runners and cyclists. The company aggregates location data and displays it on a heat map to highlight athletes’ favorite routes. The Strava heat map for Tehran shows that circle spoofing has also been employed in at least one other location. The below screenshot shows GPS-enabled fitness trackers circling a government complex that houses offices for several defense and technology-related organizations. Iran was the first nation to publicly announce it had the ability to spoof GPS signals and seems to have used it to great advantage. In 2011, a CIA drone that had been operating across the border in Afghanistan landed at an Iranian airfield. Iran’s government claimed that its forces had sent false signals to the drone’s GPS receiver in order to capture it. At first, U.S. government officials said that this kind of spoofing was not possible. Several months later, Prof. Todd Humphreys demonstrated how it could be done to a drone at the University of Texas football stadium. U.S. officials then admitted that spoofing was possible, but said it wasn’t what happened to the CIA drone. At the same time, they offered no alternate explanation of how the drone was captured. In 2016 Iranian forces captured two U.S. Navy boats that had strayed into Iran’s territorial waters. This was just after President Obama had succeeded in pressing that nation to give up nuclear weapons research, and was on the same day as Obama’s last State of the Union address. There was little reason for the U.S. Navy boats to have veered so far off course, and it was clear that the Iranian Navy was waiting for them. Many speculated that Iran had spoofed GPS signals to lure the U.S. Navy boats into Iranian waters. U.S. officials have denied that this was the cause of the incident, but have not publicly offered an alternate explanation other than “mis-navigation.” During heightened tensions in the Persian Gulf in 2019, Iran shot down a U.S. surveillance drone and President Trump seemed ready to launch a retaliatory strike. This was called off at the last minute. According to some reports, the strike was canceled because of the likelihood the drone was in Iranian airspace at the time. At about the same time British intelligence was warning merchant vessels in the area that Iran was attempting to use GPS spoofing to lure them into Iranian waters as a pretext for seizing the ships. While the Middle East has been a hotbed of jamming and conventional spoofing for years, these recent circle-spoofing incidents are the first of the kind we know of in the region. It may well be that Iranian forces have recently received equipment from China and are experimenting with it. They could also be using it to deter GPS guided drones and disrupt other surveillance systems in the vicinity of sensitive government facilities. source: https://www.gpsworld.com/gps-circle-spoofing-discovered-in-iran/
  34. 1 point
    for anyone who works with COVID-19, you can get a 6-month free ArcGIS licence https://www.esri.com/en-us/disaster-response/request-assistance Info To help public health agencies and other organizations jump-start their response to coronavirus disease 2019 (COVID-19), Esri is providing the ArcGIS Hub Coronavirus Response template at no cost along with a complimentary six-month ArcGIS Online subscription with ArcGIS Hub Basic and ArcGIS Insights. The ArcGIS Hub Coronavirus Response template includes examples, materials, and configurations for rapidly deploying an ArcGIS Hub environment. ArcGIS Hub is a framework for building your own website to help you visualize and analyze the COVID-19 crisis in the context of your organization's or community's population and assets. ArcGIS Insights allows you to put your data into context and analyze it on a whole new level, helping you respond, resource, and recover. Request access to ArcGIS Online with ArcGIS Hub Basic and ArcGIS Insights by completing the form below. Once you have your subscription, or if you already have ArcGIS Online, go to the app launcher, navigate to ArcGIS Hub, click New Site, browse the gallery, and activate the ArcGIS Hub Coronavirus Response template.
  35. 1 point
    A possible design flaw has surfaced by DJI Mavic Mini owners experiencing the propellers rubbing against the drone‘s body due to the lightweight and flexible design of the arms. It looks to be a known problem with people already working on fixes for it. Images have appeared on DJI’s community website with a user posting images of their Mavic Mini with what looks be marks caused by the propellers contacting the body. The user had shared that they recently started to notice the front set of propellers tend to hit the drone body, even if new ones were put on. He also stated it was more likely to happen when the drone is in sport mode. It looks like the cause of the problem is the flexibility in the arms used due to the weight restrictions DJI was able to keep within. The arms can flex due to the torque of the motors, causing the propellers to get closer to the drone body, and therefore rub against it. It looks to be a known problem for some as a company has already produced prop mounts that raise the height of the propellers just enough to clear the body fully. There are also designs on Thingeverse that replace the soft foam inserts in the arms with stiff 3D printed plastic. Both of these options will likely set the Mavic Mini over the 250-gram weight limit. source: https://dronedj.com/2020/03/24/dji-mavic-mini-design-flaw-pops-hit-done-body/
  36. 1 point
    preciso de ajuda para trabalhar operaction Dashbord ArcGis ja crie a conta esri mais não me deixa usar operaction Dashbord ArcGis
  37. 1 point
    sorry i cant see your picture, our country block imgur, really suck 😷
  38. 1 point
    did you mean for function area is Spherical function area?
  39. 1 point
    Take a look on this: links: https://www.cambridge.org/core/what-we-publish/textbooks untested, maybe you need make free user account first they have nice collection of engineering and geosciences books https://www.cambridge.org/core/what-we-publish/textbooks/listing?aggs[productSubject][filters]=F470FBF5683D93478C7CAE5A30EF9AE8 https://www.cambridge.org/core/what-we-publish/textbooks/listing?aggs[productSubject][filters]=CCC62FE56DCC1D050CA1340C1CCF46F5
  40. 1 point
    NRM638 Python Scripting with ArcGIS10 Objectives: To learn basic Python scripting independent of GIS To learn how to write stand-alone geoprocessing scripts for ArcGIS10 To learn how to make and document script tools for ArcGIS10 To learn how to automate mapping tasks using arcpy.mapping http://nrm.salrm.uaf.edu/~dverbyla/nrm638/
  41. 1 point
    The Gridded Population of the World (GPW) collection, now in its fourth version (GPWv4), models the distribution of human population (counts and densities) on a continuous global raster surface. Since the release of the first version of this global population surface in 1995, the essential inputs to GPW have been population census tables and corresponding geographic boundaries. The purpose of GPW is to provide a spatially disaggregated population layer that is compatible with data sets from social, economic, and Earth science disciplines, and remote sensing. It provides globally consistent and spatially explicit data for use in research, policy-making, and communications. For GPWv4, population input data are collected at the most detailed spatial resolution available from the results of the 2010 round of Population and Housing Censuses, which occurred between 2005 and 2014. The input data are extrapolated to produce population estimates for the years 2000, 2005, 2010, 2015, and 2020. A set of estimates adjusted to national level, historic and future, population predictions from the United Nation’s World Population Prospects report are also produced for the same set of years. The raster data sets are constructed from national or subnational input administrative units to which the estimates have been matched. GPWv4 is gridded with an output resolution of 30 arc-seconds (approximately 1 km at the equator). The nine data sets of the current release are collectively referred to as the Revision 11 (or v4.11) data sets. In this release, several issues identified in the 4.10 release of December 2017 have been corrected as follows: The extent of the final gridded data has been updated to a full global extent. Erroneous no data pixels in all of the gridded data were recoded as 0 in cases where census reported known 0 values. The netCDF files were updated to include the Mean Administrative Unit Area layer, the Land Area and Water Area layers, and two layers indicating the administrative level(s) of the demographic characteristics input data. The National Identifier Grid was reprocessed to remove artefacts from inland water. In addition, two attributes were added to indicate the administrative levels of the demographic characteristics input data, and the data set zip files were corrected to include the National Identifier Polygons shapefile. Two new classes (Total Land Pixels and Ocean Pixels) were added to the Water Mask. The administrative level names of the Greece Administrative Unit Centre Points were translated to English. Separate rasters are available for population counts and population density consistent with national censuses and population registers, or alternative sources in rare cases where no census or register was available. All estimates of population counts and population density have also been nationally adjusted to population totals from the United Nation’s World Population Prospects: The 2015 Revision. In addition, rasters are available for basic demographic characteristics (age and sex), data quality indicators, and land and water areas. A vector data set of the centrepoint locations (centroids) for each of the input administrative units and a raster of national level numeric identifiers are included in the collection to share information about the input data layers. The raster data sets are now available in ASCII (text) format as well as in GeoTIFF format. Five of the eight raster data sets are also available in netCDF format. In addition, the native 30 arc-second resolution data were aggregated to four lower resolutions (2.5 arc-minute, 15 arc-minute, 30 arc-minute, and 1 degree) to enable faster global processing and support of research communities that conduct analyses at these resolutions. All of these resolutions are available in ASCII and GeoTIFF formats. NetCDF files are available at all resolutions except 30 arc-second. All spatial data sets in the GPWv4 collection are stored in geographic coordinate system (latitude/longitude).
  42. 1 point
    New algorithm solves complex problems more easily and more accurately on a personal computer while requiring less processing power than a supercomputer The exponential growth in computer processing power seen over the past 60 years may soon come to a halt. Complex systems such as those used in weather forecast, for example, require high computing capacities, but the costs for running supercomputers to process large quantities of data can become a limiting factor. Researchers at Johannes Gutenberg University Mainz (JGU) in Germany and Università della Svizzera italiana (USI) in Lugano in Switzerland have recently unveiled an algorithm that can solve complex problems with remarkable facility – even on a personal computer. Exponential growth in IT will reach its limit In the past, we have seen a constant rate of acceleration in information processing power as predicted by Moore's Law, but it now looks as if this exponential rate of growth is limited. New developments rely on artificial intelligence and machine learning, but the related processes are largely not well-known and understood. "Many machine learning methods, such as the very popular deep learning, are very successful, but work like a black box, which means that we don't know exactly what is going on. We wanted to understand how artificial intelligence works and gain a better understanding of the connections involved," said Professor Susanne Gerber, a specialist in bioinformatics at Mainz University. Together with Professor Illia Horenko, a computer expert at Università della Svizzera italiana and a Mercator Fellow of Freie Universität Berlin, she has developed a technique for carrying out incredibly complex calculations at low cost and with high reliability. Gerber and Horenko, along with their co-authors, have summarized their concept in an article entitled "Low-cost scalable discretization, prediction, and feature selection for complex systems" recently published in Science Advances. "This method enables us to carry out tasks on a standard PC that previously would have required a supercomputer," emphasized Horenko. In addition to weather forecasts, the research see numerous possible applications such as in solving classification problems in bioinformatics, image analysis, and medical diagnostics. Breaking down complex systems into individual components The paper presented is the result of many years of work on the development of this new approach. According to Gerber and Horenko, the process is based on the Lego principle, according to which complex systems are broken down into discrete states or patterns. With only a few patterns or components, i.e., three or four dozen, large volumes of data can be analyzed and their future behavior can be predicted. "For example, using the SPA algorithm we could make a data-based forecast of surface temperatures in Europe for the day ahead and have a prediction error of only 0.75 degrees Celsius," said Gerber. It all works on an ordinary PC and has an error rate that is 40 percent better than the computer systems usually used by weather services, whilst also being much cheaper. SPA or Scalable Probabilistic Approximation is a mathematically-based concept. The method could be useful in various situations that require large volumes of data to be processed automatically, such as in biology, for example, when a large number of cells need to be classified and grouped. "What is particularly useful about the result is that we can then get an understanding of what characteristics were used to sort the cells," added Gerber. Another potential area of application is neuroscience. Automated analysis of EEG signals could form the basis for assessments of cerebral status. It could even be used in breast cancer diagnosis, as mammography images could be analyzed to predict the results of a possible biopsy. "The SPA algorithm can be applied in a number of fields, from the Lorenz model to the molecular dynamics of amino acids in water," concluded Horenko. "The process is easier and cheaper and the results are also better compared to those produced by the current state-of-the-art supercomputers." The collaboration between the groups in Mainz and Lugano was carried out under the aegis of the newly-created Research Center Emergent Algorithmic Intelligence, which was established in April 2019 at JGU and is funded by the Carl Zeiss Foundation. https://www.uni-mainz.de/presse/aktuell/10864_ENG_HTML.php
  43. 1 point
    Google announced Dataset Search, a service that lets you search for close to 25 million different publicly available data sets, is now out of beta. Dataset Search first launched in September 2018. Researchers can use these data sets, which range from pretty small ones that tell you how many cats there were in the Netherlands from 2010 to 2018 to large annotated audio and image sets, to check their hypotheses or train and test their machine learning models. The tool currently indexes about 6 million tables. With this release, Dataset Search is getting a mobile version and Google is adding a few new features to Dataset Search. The first of these is a new filter that lets you choose which type of data set you want to see (tables, images, text, etc.), which makes it easier to find the right data you’re looking for. In addition, the company has added more information about the data sets and the organizations that publish them. Searched 'remote sensing' and found this Geographic information A lot of the data in the search index comes from government agencies. In total, Google says, there are about 2 million U.S. government data sets in the index right now. But you’ll also regularly find Google’s own Kaggle show up, as well as a number of other public and private organizations that make public data available, as well. As Google notes, anybody who owns an interesting data set can make it available to be indexed by using a standard schema.org markup to describe the data in more detail. Source
  44. 1 point
    update: this how i do till i post this update 1. disable hardware acceleration 2. Turn off all compatibility settings except for Run This Program As Administrator (point 4 on above article) 3. Stop the Windows Presentation Foundation Font Cache (point 5 on above article). I saw little increase on performance, the workspace almost playable now, but i still find the lag when scroll it.
  45. 1 point
    This is a very interesting mapping platform for the agriculture community. The Belarus-based startup platform uses Sentinel-2 data and AI to instantly delineate thousands of crop fields and status of 20 plus crops in USA and Europe. They also have smartphone-based apps which you can use to find these solutions for your field as well. The platform applies Machine Learning, which constantly improves the service as more data and feedback is collected. Considering that a mind-boggling 376,835,301 hectares of fields across Europe and the USA have already been analyzed and catalogued, the system has reached a remarkable level of maturity. OneSoil — a Copernicus-enabled start-up from Belarus Check out their interactive map. Onesoil homepage
  46. 1 point
  47. 1 point
    Klau Geomatics has released Real-Time Precise Point Positioning (PPP) for aerial mapping and drone positioning that enables 3 to 5 cm initial positioning accuracy, anywhere in the world, without any base station data or network corrections. With this, you Just need to fly your drone at any distance, anywhere. The system allows to navigate with real-time cm level positioning or geotag your mapping photos and Lidar data. You don’t need to think about setting up a base station, finding quality CORS data or setting up an RTK radio link. You don’t need to be in range of a CORS station, you can fly autonomously, in remote areas, long corridors, unlimited range, it just works, giving you centimetre level accuracy, anywhere. Now, with this latest satellite-based positioning technology, 3 to 5cm accuracy can be achieved, anywhere in the world, with no base station. KlauPPP leverages NovAtel’s industry-leading technology to achieve this quantum leap in PPP accuracy. NovAtel PPP and Klau Geomatics hardware/software system is now the simplest, most convenient and accurate positioning system for UAVs and manned aircraft. The bundled solution enables accurate positioning in any published or custom coordinate system and datum. This technology is very applicable to surveying, mapping, navigation and particularly the emerging drone inspection industry, starting to realize that absolute accuracy is essential to analyze change over time in 3D assets. A BVLOS parcel delivery drone can now travel across a country and arrive exactly on it’s landing pad. No range limitations, no base station requirements or radio links. Highly accurate autonomous flight. Large scale enterprise drone companies can deploy their fleet of operators with a simple, mechanical workflow to capture accurate, repeatable data, without the complications of the survey world; of RTK radio links and network connections or logging base station data within a range of each of their many projects. Now they have a simple consistent operation that just works, every time, every location. “Just as Klau Geomatics led the industry from RTK and GCPs to PPK, we now lead the charge to PPP as the next technology for simple, accurate drone operations”, says Rob Klau, Director of Klau Geomatics source : http://geomatics.com.au/
  48. 1 point
  49. 1 point
    Which WEBGIS Software can be used with Google Cloud??
  50. 1 point
    look at maxmax website, they do NIR conventions on cameras that can fit on drones. Did a canon compact with them 100%. Try imageJ (free software), run the NDVI settings you will get some data. Or use a blue filter on your camera, you will get usable images for NDVI. I got good data with dessert palms from RGB images in eCognition, but you have o play with the analysis a bit! Having a NIR layer will make life easy for you. Good Luck


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