Satellite data reveals real-time weather patterns of Humberto and Imelda.

📢 Explore EuroSAT – A Novel Dataset for Land Use & Land Cover Classification 🛰️ Built from Sentinel-2 imagery (13 spectral bands) under the Copernicus program 🌍 27,000 geo-referenced images across 10 land cover classes ✨ Benchmarked with state-of-the-art Deep CNNs 🎯 Achieves an impressive 98.57% overall accuracy 🗺️ Applications: Land use & land cover change detection, Improving and updating geographical maps & Supporting a wide range of Earth observation tasks 📄 More info: https://bit.ly/EuroSAT #EuroSAT #LandCover #RemoteSensing #Sentinel2 #EarthObservation #DeepLearning #OpenData #Geospatial

A powerful open dataset for land cover classification using Sentinel-2 imagery. EuroSAT is a great step forward for remote sensing and Earth observation applications. Worth exploring!

Understanding Spectral Resolution in Remote Sensing! 🛰️📸 Ever wondered how satellites can tell the difference between a forest, a lake, and a city road — all from space? 🤔 That’s the magic of Spectral Resolution! 🌍 Spectral resolution refers to how finely a sensor can distinguish between different wavelengths of light. 👉 A high spectral resolution sensor captures many narrow wavelength bands — perfect for detecting subtle differences like healthy vs. stressed crops 🌾 or clean vs. polluted water 💧. 👉 A low spectral resolution sensor captures fewer, broader bands — great for general land cover mapping 🗺️. In short, the higher the spectral resolution, the more color details the sensor “sees” — just like upgrading from a black-and-white TV to ultra-HD color! 🎨📺 #RemoteSensing #SpectralResolution #EarthObservation #GIS #SpaceTechnology #Geoinformatics #SatelliteImagery

Radiometric Resolution: The Power of Pixel Sensitivity! 🛰️✨ Ever wondered how satellites can tell the difference between light and “slightly lighter” areas on Earth? 👀 That’s the work of Radiometric Resolution! 🌍 Radiometric resolution refers to how finely a sensor can detect variations in energy (brightness or reflectance) coming from the Earth’s surface. 🎚️ Think of it like this: ✅A low radiometric resolution (8-bit) sensor can record 256 shades of brightness. ✅A high radiometric resolution (12-bit or 16-bit) sensor can record thousands of subtle shades, capturing finer details — like the difference between dry soil and slightly moist soil 🌾, or light vs. dense vegetation 🌳. In short, the higher the radiometric resolution, the more “sensitive” your satellite eyes become! 👁️🌈 #RemoteSensing #RadiometricResolution #SatelliteImagery #EarthObservation #GIS #GeospatialIntelligence #ClimateMonitoring #Geoinformatics

This HydroClimateSight feature highlight explores how the platform’s Remote Sensing Map Layers give users access to high-quality environmental datasets for better water and land management decisions. By integrating data from trusted sources such as NASA, ECCC, NSIDC, AAFC, and ISRIC, HydroClimateSight provides real-time and static geospatial layers that support both physics-based and machine-learning hydrologic models. From satellite imagery and precipitation maps to soil surveys and land cover inventories, these layers deliver the spatial and temporal detail needed to understand environmental conditions across scales. By combining these authoritative datasets within an interactive visualization framework, HydroClimateSight transforms complex environmental information into actionable insights for flood forecasting, agricultural planning and climate resilience. https://lnkd.in/eVgxBVCg

🌍 Top 15 Free Satellite Imagery & Earth Observation Data Sources (2025 Update) Whether you’re a GIS analyst, remote sensing researcher, or environmental professional — here are some of the best portals for accessing free satellite imagery and geospatial datasets 👇 1️⃣ USGS Earth Explorer 🌐 https://lnkd.in/dCPqWusb 🛰️ Landsat (optical), declassified CORONA, Hyperion hyperspectral 2️⃣ Sentinel / Copernicus Browser 🌐 https://lnkd.in/dDBTARNr 🛰️ Sentinel-2 (optical multispectral), Sentinel-1 (SAR) 3️⃣ NASA Earthdata Search 🌐 https://lnkd.in/dZQ9yXZz 🛰️ Land cover, atmosphere, cryosphere, biosphere products 4️⃣ NOAA Data Access Viewer 🌐 https://lnkd.in/dtEdh_qs 🛰️ Satellite, aerial imagery, coastal LiDAR 5️⃣ Maxar Open Data Program 🌐 http://maxar.com/open-data 🛰️ High-resolution disaster imagery, stereo, elevation, footprints 6️⃣ Geo-Airbus Defense 🌐 https://lnkd.in/d3EkT_MV 🛰️ SPOT, Pleiades (optical), TerraSAR-X (radar), WorldDEM 7️⃣ NASA Worldview 🌐 https://lnkd.in/dnujDZpG 🛰️ Interactive visualization of global scientific satellite data 8️⃣ NOAA CLASS (Comprehensive Large Array-data Stewardship System) 🌐 http://class.noaa.gov 🛰️ GOES/POES imagery, aerosols, ozone, environmental data 9️⃣ ISRO Bhuvan 🌐 http://bhuvan.nrsc.gov.in 🛰️ Indian satellite data: IRS, Cartosat, OceanSat, CartoDEM 🔟 JAXA ALOS World 3D (AW3D30) 🌐 http://eorc.jaxa.jp 🛰️ Global 30 m DSM / elevation from ALOS L-band radar 1️⃣1️⃣ NOAA Digital Coast 🌐 https://lnkd.in/dsapmfAa 🛰️ Coastal imagery, elevation, radar, socio-economic data 1️⃣2️⃣ PROBA-V MEP (GeoViewer) 🌐 https://lnkd.in/dwJDSb-9 🛰️ PROBA-V, SPOT-VGT, METOP — global vegetation trends 1️⃣3️⃣ Global Land Cover Facilities 🌐 (global land cover data sources via e.g. landsat, MODIS, AVHRR) 🛰️ Landsat, MODIS, AVHRR — global land use/land cover maps 1️⃣4️⃣ UNAVCO (SAR & Geodesy Data) 🌐 http://unavco.org 🛰️ SAR, tectonic deformation, geodesy data for Earth science 1️⃣5️⃣ INPE (Brazil’s National Institute for Space Research) 🌐 http://dgi.inpe.br 🛰️ CBERS, ResourceSat, UK-DMC — regional optical datasets Follow us: https://lnkd.in/dXzJqUti For More info: https://lnkd.in/dgm-6mv6 #GIS #RemoteSensing #SatelliteImagery #EarthObservation #GeospatialData #NASA #USGS #Sentinel #Landsat #Copernicus

My first time at the OSGeo:UK #foss4guk conference in #Leeds this week. I really recommend anyone attending the next one. It inspired me to highlight how #opendata such as #Copernicus programme #Sentinel1 satellite imagery from European Space Agency - ESA is a valuable resource for #environmental monitoring. Daniel Hölbling and I applied this resource to #peatland using #qgis with some interesting results, which were recently published in the MDPI Remote Sensing journal https://lnkd.in/eaPpipQu The map extracts below represent visually dry peatland in orange and wet boggy areas in turquoise compared with my photo survey of an area of the West Pennine Moors. We did this by interpreting #sar radar measurements from #satellite data. If you are a peatland professional we would be very pleased to discuss our research in more detail. With zero funding resources, all of this was possible because of #opendata & #opensource #gis software. We've also been published in the Water journal, where we applied Sentinel1 to #naturalfloodmanagement in supporting evaluation efforts to strengthen the evidence base for its use. https://lnkd.in/gynKEEpk #remotesensing #earthobservation #mapping

🛰️ Our 3D-SAR space mission is moving forward. At BiDS 2025 (Riga), our research engineer Anton Kostiukhin shared first results on forest height assessment—using bi-static SAR interferometry (TanDEM-X, dual-pol) to infer canopy height in hemiboreal forests. 🌲 Early outcome: bi-static dual-pol TanDEM-X data contains enough information to estimate height in birch-dominant compartments with ≈1.5 m MAE, aligning well with results reported for other forest types—clear evidence we’re on the right track. Next up: ➜ create a more robust sampling strategy to reduce dependence on geolocation features; ➜ incorporate stand properties (species, height, density) to study signal behaviour; ➜ scale to larger, more complex terrain; ➜ and compare pixel- vs compartment-based models to mitigate SAR noise. 🤝 We do this work together with Chair of Geoinformatics & Cartography, who share their expertise in geospatial analysis, spatial machine learning and modelling. And with thanks to German Aerospace Center (DLR) for TanDEM-X imagery. Co-financed by the European Union. #SAR #TanDEMX #3DSAR #ForestMonitoring #EarthObservation #RemoteSensing #BiDS2025

🌍 Did you know your phone already tracks more geospatial data than most GIS labs? Every time you use Google Maps, track your run, or check the weather, geospatial data is quietly at work behind the scenes. But here’s the mind-blowing part: Over 80% of disaster response decisions today rely on GIS and satellite data yet most people have never even heard of it. 💡 Pro Tip: You can explore environmental changes in your city using free satellite imagery from platforms like Google Earth Engine or NASA’s Earth Data. I tried it recently and mapped how urban green spaces have changed in just 10 years—it’s fascinating! GIS isn’t just for experts—it’s for anyone curious about our planet. 🌱 🔹 Your turn: What’s your favorite GIS trick or tool? Drop it in the comments—I want to see how you’re using geospatial tech to change the game! #GIS #RemoteSensing #Geospatial #Mapping #DataVisualization #EarthObservation #GeoAI

Knowing where satellites are, where they’re going, and when they’ll be there is table stakes for mission ops. Using public Two-Line Elements (TLEs) and an SGP-class orbit propagator, I built a MATLAB visualization that animates a GPS constellation in ECI, rotates Earth correctly via GMST, and computes line-of-sight (LoS) to a Norfolk, VA ground station with a 20° elevation mask. Visible spacecraft are connected in real time with green vectors; red markers show instantaneous ECI positions, full orbit information extracted from the TLE in blue. What this enables (ops view) -Pass prediction & contact planning: Who’s above 20° and for how long. -Antenna scheduling & handovers across sites. -Rapid what-ifs for outage recovery or site diversity. -Constellation health checks (e.g., Do we keep PDOP under target from this site and time?). Under the hood (tech notes) -Inputs: TLEs; time of interest in UTC. -Propagation: Mean motion from TLE + SGP-style Kepler solve. -Frames: ECI drawing; ECEF/ENU for elevation tests; WGS-84 station coordinates. -Earth: Textured ellipsoid, −GMST transform (ECEF→ECI), north-up texture. -LoS: ECI station vector recomputed each frame; elevation via ENU projection. What I’d add next - Higher-fidelity dynamics: J2/J3, drag, SRP, third body; switch to a full SGP4/SDP4 library or high-order numerical propagation for non-GNSS assets. - Terrain/obstruction masks: True horizon from DEMs for realistic elevation limits. - DOP metrics: Live GDOP/PDOP/HDOP heatmaps and “best 4” geometry picks. Multi-site ops: Site selection, diversity gain, and automatic pass scheduling. - Freshness guards: TLE age checks + auto-refresh; compare with broadcast ephemerides for GNSS. - Export & automation: Pass reports (CSV/ICS), REST hooks, and CI-packaged tooling for 24/7 operations. References: Fundamentals of Astrodynamics, 2nd Ed. (Bate, Mueller, White, Saylor) and standard SGP4 documentation. #spaceoperations #astrodynamics #MATLAB #TLE #SGP4 #GNSS #GPS #ECI #ECEF #groundstation #visualization #SSA #aerospace #aerospaceengineering