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Is there a way to calibrate multispectral imagery without using a reflectance panel? I have two sets of data that need to be calibrated but they were flown without using a reflectance panel. The sensor is a Micasense RedEgde-MX. Both sets are taken in the same area.

quick maintenance, upgrade to latest script version 🙂

its been awhile since I update the forum, so here you go, sorry for the down time, but I need to update to the recent version to make sure the forum stay secure and the functions keep running, if you have any bugs please let me know

👋Hello! It's been a minute or two since I was last here. Keen to see what I have missed.

Are you a post-doctoral researcher looking for an exciting opportunity in advanced Earth Observation (EO) for Earth Science? The ESA is offering a two-year research fellowship in the Directorate of Earth Observation Programmes. The fellowship will cover a wide range of innovative topics from the development and validation of novel methods, algorithms and EO products to innovative Earth system and climate research. The successful candidate will be responsible for undertaking advanced research addressing major observational gaps and scientific priorities in EO and Earth system science. The fellowship is open to all qualified candidates irrespective of gender, sexual orientation, ethnicity, beliefs, age, disability or other characteristics. Applications from women are encouraged. Apply by October 3, 2023 1.For more information please visit: http://geospatialsight.com/post-doctoral-research-fellowship-in-advanced-eo-for-earth-science/

no, geomatics and engineer could do that, there are many software that already have deep learning function... for example, qgis already have plugin for deep learning, you can search on that in google, there are many paid software that can do that

I’am a Geomatics student and i want to train a deep learning model to extract the building footprint of a city, how can i do it ? It is easy to do for Geomatics or survey engineer ? Or this project need to IT background ? thanks

That value is in projected coordinate system like UTM (Universal Transverse Mercator), change the CRS in QGIS workspace to WGS-84 to show decimal degree coordinate

Hi friends ! In QGIS 3.32.2, I am using WGS 1984 EPSG 4326 for both Project and Basemap of Google Satellite Hybrid. When I use Google Earth Pro on Desktop and locate my home, the coordinates are: 24.590580°, 73.719466° BUT... when I navigate to my home location in Basemap of QGIS and capture coordinates with Lat-ong tool II get these values: 2825537.85691968, 8206411.02187792 Why is QGIS not showing coordinate values as Google Earth? How to convert latitude and longitude values of degrees to the values compatible with QGIS canvas? Regards, hnaudr

The open-source model will serve as the basis for future forest, crop and climate change-monitoring AI. NASA estimates that its Earth science missions will generate around a quarter million terabytes of data in 2024 alone. In order for climate scientists and the research community efficiently dig through these reams of raw satellite data, IBM, HuggingFace and NASA have collaborated to build an open-source geospatial foundation model that will serve as the basis for a new class of climate and Earth science AIs that can track deforestation, predict crop yields and rack greenhouse gas emissions. For this project, IBM leveraged its recently-released Watsonx.ai to serve as the foundational model using a year’s worth of NASA’s Harmonized Landsat Sentinel-2 satellite data (HLS). That data is collected by the ESA’s pair of Sentinel-2 satellites, which are built to acquire high resolution optical imagery over land and coastal regions in 13 spectral bands. For it’s part, HuggingFace is hosting the model on its open-source AI platform. According to IBM, by fine-tuning the model on “labeled data for flood and burn scar mapping,” the team was able to improve the model's performance 15 percent over the current state of the art using half as much data. "The essential role of open-source technologies to accelerate critical areas of discovery such as climate change has never been clearer,” Sriram Raghavan, VP of IBM Research AI, said in a press release. “By combining IBM’s foundation model efforts aimed at creating flexible, reusable AI systems with NASA’s repository of Earth-satellite data, and making it available on the leading open-source AI platform, Hugging Face, we can leverage the power of collaboration to implement faster and more impactful solutions that will improve our planet.” source: engadget

Hi, a simple solution : in the Layer Properties, add your SVG a symbol, then add a new Simple Marker (like a circle or a Square), put the SVG Marker on top of the Simple Marker. The size of the SVG and the Simple Marker has to be similar or the Simple Marker Square just a little bigger. It worked with your symbol as svg and a Square Marker.

Hello friends! I have plant symbols and would like to use them in QGIS. The problem is that these symbols don't allow you to edit the background colors. https://drive.google.com/file/d/15n7TvSRx8Ha2Igm_rs13A8KVggsSghJs/view?usp=sharing I followed jbrocha's solution posted on the GIS Stack Exchange forum: https://gis.stackexchange.com/questions/45180/how-to-create-svg-symbols-that-have-modifiable-fill-color-stroke-color-and-stro However, I would like to know if there is a more practical method to modify the colors of an SVG file imported into QGIS. Thanks.

With the onset of the this years User Conference, Esri unveiled various updates to its existing services and apps, one of which is the Sentinel-2 Land Cover Explorer announced in last February. You can also find this in the Living Atlas. Apart of the the visual updates, the biggest change this time is that the changes over the year are now more accurate. A fundamental aspect of global LULC maps is the ability to detect and assess land cover changes over time. Improving on an already accurate set of annual maps, Impact Observatory has incorporated new features and methodologies in their proprietary deep learning classification model, resulting in better temporal consistency across the entire time series. Change between two LULC maps can potentially signify an important and developing change in an area of interest. However, in some cases, classification results may vary from one annual cycle to the next due to modeling insufficiencies, variability in seasonal observations, and/or class ambiguity at 10-meter resolution. Such cases can lead to false or spurious change results when conducting temporal change analysis. With the improvements to the temporal consistency, users assessing temporal change across the time series can be confident that what they are seeing represents the natural world. Link - https://livingatlas.arcgis.com/landcoverexplorer/ Source https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/imagery/global-land-cover-updates/

NASADEM data products were derived from original telemetry data from the Shuttle Radar Topography Mission (SRTM), https://lpdaac.usgs.gov/products/nasadem_hgtv001/

Here in the link below you will find features of the tools https://earth.esa.int/eogateway/missions/pleiades-neo although it is an old news, for completeness of the page I put it anyway : the two satellites PNEO 5 / PNEO 6 were destroyed during the launch in December 2022. The initial stage separation was nominal. The rocket encountered an anomaly (motor issue) during the burn of the 2nd stage (here is the video of the launch : https://www.youtube.com/watch?v=5OokTuQ8wiM) In fact, the VEGA launcher failed to put the two satellites into low orbit and "re-entered" the earth (or rather permanently lost the satellites). Translated with www.DeepL.com/Translator (free version)

I am back.

https://www.researchgate.net/publication/371166560_International_Call_for_Authors_Book_Title_-_Smart_Buildings_and_Cities_with_Remote_Sensing_GIS

I need high resolution (<250m) images for monitoring ground temperature. I read there are MODIS-Landsat-Fusion Products or MODIS-Sentinel-Fusion Products, but by browsing LP DAAC and earth explorer etc. I just can't find the right data. So does anybody know: Do these Fusion Products exist, are they available for free? Can you please provide me with a link? Thank you!

Qualcomm Technologies and Xiaomi have verified meter-level positioning in the Xiaomi 12T Pro powered by the Snapdragon 8+ Gen 1 mobile platform, in Germany. Accuracy verification tests, including driving tests, were conducted by Qualcomm Technologies, Xiaomi, and Trimble in various scenarios such as open-sky rural roads and urban highways. The companies’ solutions demonstrated meter-level positioning variance at a 95% confidence level. This level of accuracy in a commercial smartphone is enabled through Qualcomm meter-level positioning for mobile in combination with Trimble RTX correction services. When integrated with Snapdragon mobile platforms, Trimble RTX enhances the phone’s positioning capabilities. Meter-level positioning accuracy can improve smartphone user experience in several scenarios, including mapping, driving, and other mobile applications. It enables greater accuracy when using ridesharing applications to identify pick-up locations for both driver and rider, fitness applications to track users’ movements, and in-vehicle real-time navigation applications for increased lane-level accuracy with greater map details and more accurate directions.

do you mean the filter function in drone2map? (densified point cloud filter?)

What tool are you using for DSM filtering?

The Aeolus mission is coming to a close on 30 April 2023 with a series of end life activities after achieving many significant accomplishments. Launched in 2018, the mission’s main goal was to measure the Earth’s wind patterns and improve our understanding of how they affect the planet’s climate. That ESA’s wind mission had outlived its predicted lifetime of three years by over 18 months. The best course of action to wind down Aeolus was to carefully re-enter the satellite back to Earth. The finishing touches to the end-of-life schedule will be made in a span of numerous weeks. Innovating Wind Measurement across Earth Using state-of-the-art laser technology, Aeolus was able to measure the wind speeds and direction from space with incredible precision. These measurements were used to create detailed maps of global wind patterns and improve weather forecasting models. In addition to its primary mission, Aeolus also made important contributions to other areas of Earth observation. For example, it provided valuable data on air pollution and dust transport across the globe. As the Aeolus mission winds down, scientists are already looking ahead to future missions that can build on its successes. This includes plans for new Earth observation missions that will focus on other key environmental factors such as ocean currents, land use, and the carbon cycle. Aeolus Mission Manager, Tommaso Parrinello was grateful to all the ESA and industry colleagues who developed and operated the mission. Improvement in Weather Forecasts Aeolus carried ALADIN, an instrument that is Europe’s most sophisticated Doppler wind lidar flown in space. A laser inbuilt the instrument fires pulses of ultraviolet light towards Earth’s atmosphere, which is received by a light detecting receiver that scatters it back from air and water molecules like aerosols and dust. With that measurement one can check the speed of the wind. Over its extended lifetime, ALADIN has beamed down over seven billion laser pulses orbiting Earth 16 times a day and covering the entire globe once a week. Aeolus data are used by major weather forecasting services worldwide, including the European Centre for Medium-Range Weather Forecasts (ECMWF), Météo-France, the UK Met Office, Germany’s Deutscher Wetterdienst (DWD), and India’s National Centre for Medium Range Weather Forecasting (NCMRWF). Since ECMWF started assimilating Aeolus data in 2020 the satellite has become the highest impact-per-observation instruments in existence. It is mainly due to Aeolus’ capacity to measure winds where data is scarce. When planes were grounded during the lockdowns imposed due to the COVID pandemic, Aeolus was able to contribute missing data to plug the gap in weather forecasts. ESA’s Director of Earth Observation Programme, Simonetta Cheli said that the Aeolus mission has been a triumph of European innovation, collaboration and technical excellence, and is an example of how ESA’s Earth Explorers perform beyond expectations, and is a shining light for our Future EO Programme. Its impacts will live long beyond its lifetime in space, paving the way for future operational missions such as Aeolus-2. Despite the mission’s impending end, the data collected by Aeolus will continue to be used by scientists around the world for years ahead in the future. This legacy is a testament to the mission’s trailblazing spirit and its important role in advancing our understanding of the Earth’s climate. First Mission to Measure Earth’s Wind Patterns Powers Down (geospatialworld.net)