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  1. NASA has launched one of its most crucial science missions to date, the Mars 2020 mission that carries its Perseverance robotic rover. This rover, a successor to the Curiosity robotic explorer, is equipped with sensors specifically designed to help it hopefully fund evidence of ancient, microbiotic life on Mars. Mars 2020 departed from Cape Canaveral in Florida at 7:50 AM EDT (4:50 PM PDT). Perseverance was loaded atop a United Launch Alliance (ULA) Atlas V rocket, which had a good liftoff and deployed its second stage which put the spacecraft into a parking orbit as it readies to depart on its trip towards Mars, which will see it arrive in February 2021. Once at Mars, the lander vehicle will take Perseverance down to the planet’s surface on February 18, 2021, to a target landing zone found in what’s known as Jezero Crater. This location on Mars was once a lake, long ago when the atmosphere on Mars was quite different than the dry, dusty and cold environment we know today. This has been chosen specifically because it’s a prime spot for finding any evidence of microbiological life that might exist, since it contains one of the best-preserved deposits of a river delta on Mars. NASA scientists don’t expect to be able to confirm the existence of life on Mars using the instruments on Perseverance, however – they think they can find strong indications that the conditions and materials necessary for life once existed, but the ultimate proof could come from the ambitious Mars sample return mission being planned for 2026. This would involve NASA launching a return rocket to the red planet, which will carry a rocket that can take off from the Mars surface with samples collected by Perseverance on board. That would then meet up with a rover to be launched by the European Space Agency (ESA) which would then make the trip all the way back to Earth for scientists to study. In addition to its contained, radioactive nuclear battery power source, environment sensors, cameras and a suite of other instruments to help pick up any preserved evidence of ancient life, Perseverance is equipped with microphones. This is the first time that microphones are making the trip to the surface of another world, and it means we could hear what it sounds like on the surface of another world, something we’ve never done before. Perseverance also carries the Mars Ingenuity helicopter, a small drone designed for first-ever self-powered flight, which is also designed to warm itself to survive the cold Martian night. It is set to hopefully make up to five flights in 30 days, could include color photos – the first ever taken from an aerial vantage point. This is a great first step for this historic Mars 2020 mission, and now we’ll wait and watch for other significant milestones, including next in around two weeks when the spacecraft fires its engines for its departure from Earth’s orbit and begins the long trip to Mars. Techcrunch Mars 2020 Mission Overview Good luck to 'Perseverance'. Find us another home since Earth seem not to last very long. 😅
  2. Have you tried any cron job or scheduler? I tried on websites but haven't tried on databases. Sending email are easy but triggering certain events tricky, try this.
  3. 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.
  4. Interesting! @juliusmall, have you tried ASTER GEDM v3? The problem you are facing are probably elevation void areas which are reduced in the new version released last year.
  5. @darksabersan interesting! Do share what you achieved later on. About RoboMaster, I haven't seen a programmable rc vehicle before. Definitely a better choice for smart kids. The price is ok, recommended for parents who's thinking about ipad and similar devices for a b-day gift. Drones on the other-hand should involve an adult when operated. BTW even for adults flying drones are not permitted in my country, only with special permissions. 😏
  6. I like drones but just got more interested in this,
  7. The nature of the service depends on various other thing, what type of feature you used, how did you published, what is the license and user level. Check this link.
  8. Simple Analysis of Vegetative Trends in Earth Engine - SAVETREE - is a tool developed in Google Earth Engine for the Lassen Volcanic National park, it estimates tree mortality by fitting a linear trend to time serries data of a user chosen spectral index. The user can export their new map in the form of TIFF files,add historic fire layers, and the user can produce graphs which view the values in the time series for a particular pixel by clicking on the layer. Running SAVETREE Hit the “run” button in the center panel to make the widget appear. Using SAVETREE the user can do the following things: * Spectral Index: Choose from NDMI, NDVI, NDWI or NBR to select which spectral index you would like to create a linear regression layer for. The default is NDMI. * Area of interest: Choose from Lassen Volcanic National Park, Lassen National Forest, DEVELOP T2 Study Area, the Badger Planning area or choose Your asset (below) to perform the analysis on an asset you load yourself see Loading an Asset for instructions on loading your own asset. The default is LVNP. * End year and duration: The year must be in YYYY format, it is the last year of the duration of the analysis. The duration should be a number less than 20, with the most meaningful results coming from 3-7 years, it is the number of years it will create the time series for. For example, if you put in 1990 and a duration of 3, the analysis will be run on 1988, 1989, and 1990. The defaults are 2016 and 5. * Add Coefficient map: Performs the coefficient trend map analysis on the spectral index and area of interest for the duration you supplied ending with the year you specified and adds that layer to the map. * Add Bivariate map: Performs the Bivariate map analysis on the spectral index and area of interest for the duration you supplied ending with the year you specified and adds that layer to the map. * Reset Map: Clears all layers. Note: it does not reset the area of interest or any items in the widget. To reset the area of interest, choose a different area of interest from the dropdown before running a new analysis. * Fire history start and end years: These years must be in YYYY format. These numbers create a filter for the fire history data where the only data to be added to the map will be fires or treatments that occurred during those years. * Fire History Dataset: Select from FRAP Statewide Wildfire Dataset, RX fire, Other treatment, or load your own fire data asset. To load your own asset see Loading an Asset. The wildfire, rx fire and other treatments are FRAP datasets, for more details on the FRAP data and for the most up-to-date data sets please go to http://frap.fire.ca.gov/projects/fire_data/fire_perimeters_index * Export Coefficient Map: Exports the Coefficient trend layer as a TIFF file. See Exporting a Layer to get details on how to export layers to your Google Drive. * Export Bivariate Map: Exports the Bivariate map layer as a TIFF file. See Exporting a Layer to get details on how to export layers to your Google Drive. * Change Inspector: Click on any part of the Coefficient Trend or Bivariate Map layers and a graph of the change during each year for your duration for that particular point will appear at the bottom of the widget. Click the little box with the arrow in the upper right hand corner of the graph to open the graph in a new tab. You can download this graph from this new tab. SAVETREE was developed over two terms with DEVELOP: * Authors v1.0: Joshua Verkerke, Anna McGarrigle, John Dilger * Authors v2.0: Heather Myers, Anna McGarrigle, Peter Norton, Andrea Ferrer Download code
  9. 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.
  10. 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
  11. HDF format works best in QGIS because of GDAL, but you can use ArcGIS too. It is not clear if you have multidimensional raster or multi-point vector. Please write in details.
  12. IMHO switching two software for same operation shows different result because each takes different parser and environmental parameters, but they can be tweaked too. If you can set those as close as possible, the result will surely be similar. BTW, why do would you need separate softwares to process same image in the first place?
  13. 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
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