Viridien’s Post

Derived Bands, Multisite Monitoring & Spatial Resolution! For part six of the satellite industry terms series, we've listed these common terms to help you understand the industry. Swipe to see 👉 Learn how satellite imagery can benefit you here ➡️bit.ly/arlula-tasking #satelliteimagery #tech #GIS #remotesensing #education

The ability to detect methane emissions using remote sensing depends on the available spectral bands of the sensor and often results in a lot of false positives. Vit Růžička et al. developed a semantic segmentation model for hyperspectral imagery and trained it on imagery from AVIRIS-NG. They developed a multispectral approach using ratios from bands that are available for WorldView-3 and a hyperspectral approach that uses a wider selection of bands. Each was used as input to a U-net. Their multispectral model out-performed other multispectral alternatives but was significantly poorer than their hyperspectral method. To evaluate the ability of their model to generalize to other satellites, they tested it on imagery from EMIT. While performance was lower than on in-domain images, it still performed reasonably well, even without any tuning. paper: https://lnkd.in/gwvdVgpy news: https://lnkd.in/gaMwD-ZC dataset: https://lnkd.in/gYgfddJg If you enjoy these posts and want to hear more, sign up my Computer Vision Insights newsletter: https://lnkd.in/g9bSuQDP #RemoteSensing #EarthObservation #ClimateAction #MachineLearning #DeepLearning #ComputerVision

#Valuable_Resource_for_Remote_Sensing_Enthusiasts #Comprehensive_Spectral_IndicesD_tabase🛰️🌍 Dear connections, I'm thrilled to share an incredible resource for the remote sensing community, researchers, and industry professionals. It's a comprehensive database that compiles a variety of essential spectral indices for the analysis of satellite imagery and remote sensing. 🔗 Link to the database: https://lnkd.in/ezFbPbra I highly encourage everyone working in the field of remote sensing to explore this database, incorporate it into your research projects, and share it with your colleagues. Remote sensing is a constantly evolving field, and sharing resources like this one contributes to the advancement of science. Together, we can push the boundaries of remote sensing and contribute to a more sustainable world.🫱🏻🫲🏻 #RemoteSensing #SatelliteImagery #SpectralIndices #Science #OpenData #gis

⏰ ⏰ Tomorrow don't miss the "EPOS GNSS within Geo-INQUIRE" seminar, and learn more about new scientific perspective and technical advances in the GNSS field! More info and registration 👇 👇 #GeoINQUIRE #EPOSGNSS #SolidEarthSciences #OpenScience #OpenData #Metadata

the Landsat 8 and 9 satellites collect visual, near-, and shortwave-infrared images with the OLI (Operational Land Imager) and OLI-2 sensors, as well as thermal infrared (heat) data with the TIRS (Thermal Infrared Sensor) and TIRS-2, respectively. These instruments offer better spatial resolution than other commonly used satellite thermal sensors and can better distinguish glacial ice and ocean features such as ice fronts, rifts, sea ice leads, and polynyas. #Optics #SatelliteImaging #ThermalImaging

I invite you to read my article "Enhancing Earth Observation capabilities - satellite data applications and implications" published by CloudFerro S.A. #satellitedata #EarthObservation #Copernicus

Studying change for a more sustainable future requires an understanding of the past. In our new blog post, GIS expert and Senior Presales Engineer at Satellogic, Solange U. Uwera, illustrates the value of #ArchiveData Explore three critical use cases that demonstrate how analysts can leverage historical data – and where to access the Satellogic Archive.

The remaining data and protostars from the eHOPS survey, which I had the privilege of leading, are now hosted by NASA. 🌌 You can dive into the catalog and our modeling results here: https://lnkd.in/gcMW8H8Q It's fascinating to see how the modeling techniques from this astronomical project are now playing a pivotal role in my current logistics projects. For instance, the predictive algorithms used for star formation are surprisingly effective in forecasting supply chain demands. As a little side note to my fellow astronomers: if you stumble upon any extraterrestrials circling those protostars, do let me know - some mysteries still keep me up at night! 😉✨ #AstronomyToLogistics #DataScience #SupplyChainInnovation #NASA #eHOPSProject

🌍 Addressing Geometric Challenges in Satellite Imaging for Land Monitoring 🛰 Satellite images often suffer from geometric errors that impair land monitoring and change detection analysis. These misalignments between consecutive acquisitions introduce noise and inaccuracies, particularly in high-temporal-resolution data collections such as Sentinel-2, Landsat, and PlanetScope. The article, written by Peresutti explores how to co-register a temporal stack of optical satellite images to mitigate these errors. Through extensive experiments, a workflow was developed that uses image-based co-registration to accurately align temporal images, thereby improving analysis accuracy. It was found that using an average temporal image as the template and a translation-only motion model produces the best results, significantly reducing the impact of geometric errors. These findings have been incorporated into the eo-learn library to facilitate use by other researchers and professionals. 🔗 More details and results are available in the blog: https://lnkd.in/dMfuqw3g #SatelliteImagery #GeometricErrors #LandMonitoring #ChangeDetection #MachineLearning #Sentinel2 #RemoteSensing #eoLearn #GIS #DataScience

A new image of 30 Doradus B contains X-rays, optical, and infrared data.