Filippo Milazzo’s Post

Historical Satellite Images: Accessing The Old Data Collections of historical satellite images allow comparing the present and the past to detect changes, make predictions, and mitigate losses. There are multiple sources with commercial historical images or granting access for free, with the downloading option or just for online view. The richest catalogs are often the most helpful, and EOSDA LandViewer offers historical aerial or satellite images from nearly a dozen sources. https://lnkd.in/gTDSPcmm

Step Aside, Internal Tides: Supercomputer Modeling Improves Satellite Altimetry Precision - American Geophysical Union Eos: New supercomputer models can provide valuable information about the ocean's layers and movements, particularly slow moving features such as eddies and currents. https://lnkd.in/eVCTP6cj

The Ground Sampling Distance (GSD) for Sentinel’s visible and near-infrared (VNIR) bands is specified at 10 meters, but it may not accurately reflect ground resolution due to environmental effects. Ground Resolved Distance (GRD) serves as an alternative measure for actual resolution, but information about Sentinel GRD is lacking, calibration targets are not always available, and GRD may vary across different tiles. Our latest paper estimates Sentinel’s GRD using a scene-driven approach that analyzes the edges of natural targets, reducing the challenges associated with artificial targets. The methodology has broader applicability across various geospatial datasets, regardless of sensor type. It can be applied to coarser-resolution imagery including Landsat and finer-resolution datasets, e.g., commercial high-resolution satellite imagery and aerial images—special thanks to all the collaborators Farhad Samadzadegan, Ahmad Toosi, Claudio Persello, Mathias Mchneider. Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente Earth Observation Science ITC PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

In #remote sensing (RS), the choice of data types for representing and storing pixel values is crucial for efficient data management and analysis. Different #data_types cater to varying requirements for precision, range, and memory considerations. The most common data types used in RS are: 1. #Byte: Represents values from 0 to 255 (unsigned) or -128 to 127 (signed). Ideal for grayscale and RGB images, as well as categorical data in land cover classification. Efficient in memory usage, requiring only 1 byte per pixel. 2. #Integer: Represents whole numbers, typically in 16-bit or 32-bit formats. Suitable for digital elevation models (DEMs) and vegetation indices. More memory-intensive than bytes, especially with 32-bit integers. 3. #Float: Represents real numbers with decimal points, commonly using single (32-bit) or double precision (64-bit). Essential for continuous data such as reflectance values and thermal imaging. Requires more memory but offers the precision needed for scientific applications. 4. #Double: Provides higher precision than float, suitable for high-accuracy requirements in scientific calculations. Memory-intensive, but necessary for applications needing fine detail. 5. #Unsigned Integer: Represents non-negative whole numbers, useful in applications that do not require negative values. More efficient than signed integers, especially for datasets focused on indexing. 6. #Complex Data Types: Used primarily in #radar and interferometry, these types represent both phase and amplitude. Critical for interpreting surface characteristics in Synthetic Aperture Radar (SAR) data. #GIS #Geology #Remote_sensing

https://lnkd.in/gZ2unkDx Check out this video showcasing how DEEP BLOCK extracts land cover from high-resolution aerial orthophotos. We've recently enhanced DEEP BLOCK's image segmentation application by adding a polygon correction feature. This innovative update empowers users to leverage the geolocation data of various objects found in DEEP BLOCK when analyzing ultra-high resolution images, thereby eliminating the need for tedious post-processing tasks. Here’s how it works: DEEP BLOCK's advanced algorithms automatically identify and segment different land cover types, such as forests, urban areas, and water bodies, with remarkable accuracy. With the new polygon correction feature, users can extract these segmented areas with more precise boundaries. This becomes particularly valuable when working with complex landscapes or areas where land cover types frequently change. While tools like ARCGIS and QGIS offer polygon merging and correction functions, DEEP BLOCK has integrated these features seamlessly within its platform. This integration means you won't need to switch between multiple software programs or perform additional steps to achieve the desired level of detail and accuracy. DEEP BLOCK simplifies the process, allowing you to focus on analysis and decision-making. Imagine the time saved and the increased efficiency when mapping large areas for environmental studies, urban planning, or disaster response. DEEP BLOCK’s combination of high-resolution imagery and sophisticated image segmentation ensures that you get reliable, actionable insights without the hassle of manual corrections. But that's not all. DEEP BLOCK’s no-code interface makes it accessible even to those who may not have extensive technical expertise. Whether you're a seasoned GIS professional or a newcomer to remote sensing, DEEP BLOCK provides the tools you need to achieve professional-grade results. Get started with DEEP BLOCK for free today and experience the future of no-code computer vision for remote sensing. Visit deepblock.net to learn more and take your first step towards more efficient and accurate land cover analysis. No-code Computer Vision for Remote Sensing – deepblock.net #remotesensing #microscopy #pathology #earthobservation #gis #geoAI #wemakeaieasy #nocode #ai #ml #machinelearning #imagesegmentation #photogrammetry #semanticsegmentation #aiplatform #aitool #geoscience #geometry #aerialphotography #computervision #precisionfarming #precisionagriculture #satelliteimagery #urbanplanning #cartography #artificialintelligence

🚀 Unlocking the Power of Variograms in Geostatistical Interpolation! 🌍 I’m thrilled to share my recent report, "Understanding Variograms in Geostatistical Interpolation: A Comprehensive Guide." This paper delves into the essential role variograms play in spatial data analysis and interpolation techniques, especially for geostatistics. 📊 What’s Inside: Intro to Geostatistics: Discover the impact of this field on spatial data analysis. Variogram Types: Dive into key models like Spherical, Exponential, and Gaussian with real-world examples. Elevation Data Collection: Explore advanced methods like GPS and LiDAR and how variograms optimize techniques such as Kriging. Future Outlook: See where advancements in geostatistical methods are heading across different industries. 🔍 Why It Matters: In our data-driven world, mastering these concepts is crucial for GIS, environmental science, and data analytics professionals. 💡 Whether you’re a student or a seasoned expert, I invite you to read my report and join the conversation about the future of geostatistics! 👉 https://lnkd.in/dYNE6-Wa 🌐 Let’s Connect! I’d love to hear your thoughts, experiences, or questions on this topic. Drop your insights in the comments below! #Geostatistics #Variograms #SpatialAnalysis #EnvironmentalScience #DataInterpolation #GIS #StatisticalModeling #RemoteSensing

📌️Certificate Achieved: Remote Sensing and Satellite Image Processing.🗺️🌍 Proud to have successfully completed the "Remote Sensing and Satellite Image Processing with the #EOS_Platform" course from GEO University. This course has equipped me with the skills to process and analyze satellite data using advanced remote sensing techniques. The knowledge gained will be essential in understanding environmental changes, urban planning, and resource management through precise geospatial data interpretation. Grateful for the opportunity to enhance my expertise in this critical and rapidly evolving field of #Geoinformatics! #RemoteSensing #SatelliteImageProcessing #Geoinformatics #GIS #Sustainability #DataScience #GeospatialAnalysis #SpatialData #EarthObservation #EnvironmentalMonitoring #SustainableDevelopment #DataScience #EOPlatform #GeoUniversity

I’m excited to share that I’ve completed the Remote Sensing and Satellite Image Processing with the EOS Platform course from #Geo_University! 🗺️ This certification has solidified my understanding of remote sensing and satellite image processing, equipping me with valuable skills for analyzing and interpreting Earth observation data. #GIS #GeoUniversity #RemoteSensing #SatelliteImagery #SpatialAnalysis

Ushering a New Era of Hyperspectral Remote Sensing to Advance Remote Sensing Science in the Twenty-first Century. Download the full article from: https://lnkd.in/g68TuNBC [Published in Hyperspectral Remote Sensing August, 2024 Special Issue Introduction for the Photogrammetric Engineering & Remote Sensing (PE&RS) Journal of the The American Society for Photogrammetry and Remote Sensing (ASPRS)] Authors: Thenkabail, Prasad S. ; Aneece, Itiya; Teluguntla, Pardhasaradhi Source: Photogrammetric Engineering & Remote Sensing, Volume 90, Number 8, August 2024, pp. 467-470(4) Publisher: American Society for Photogrammetry and Remote Sensing Download the full article @ DOI: https://lnkd.in/g68TuNBC [Note: This article is Open Access under the terms of the Creative Commons CC BY-NC-ND license]. Figure Below:. EnMAP Hyperspectral Vs. Landsat 9 Multispectral. A comparison of hyperspectral data from new-generation Germany’s DLR’s Environmental Mapping and Analysis Program (EnMAP) sensor and multispectral data from USA’s NASA’s and USGS’s Landsat 9. A) Spectral signatures for agriculture, urban, and fallow samples from images acquired in May 2024. B) EnMAP image cube. C) Landsat 9 image cube. D) Sample locations on EnMAP image.Hyperspectral narrowband (HNB) data acquired from new generation Germany’s DLR’s Environmental Mapping and Analysis Program (EnMAP) sensor that was launched in the year 2022.

Do you use #commercial #highres #EarthObservation data in your projects? Are you delivering #analytics or #GIS projects to tight deadlines? If you haven't already, now is the perfect time to share your views on our poll on overcoming Earth Observation data accessibility challenges. Click the link below to participate and let us know which operational hurdle slows you down the most when utilizing satellite images: https://lnkd.in/gmFTsxEk #SatelliteData #RemoteSensing #DataAccessibility #DataAnalysis #Geospatial #SatelliteImagery