Sistemas de Información Geográfica

Our #QGIS AI Georeferencer is now available! You can now georeference north oriented aerial imagery and maps automatically in QGIS—here's how 👇 First, go to the Manage and Install Plugins window in QGIS and either update your Bunting Labs AI Vectorizer plugin to the latest version or install it if you haven't already Then, go to Layer -> Georeferencer and you will find two new buttons 🛰️ Aerial imagery georeferencing—our most powerful georeferencer. To use this: 1. load your satellite or aerial image in the georeferencer, 2. zoom your QGIS window to be roughly the same area as the image, 3. select the satellite icon "Generate GCP points from satellite" to generate control points 🌐⚡️ Full georeferencing—our experimental georeferencer. This mode uses our AI model built to read the map, identify features that a human would georeference to like roads, building outlines, and coordinates, and place ground control points. This allows our AI to georeference maps that do not have an aerial imagery base. To use this: 1. load your map in the georefernecer, 2. zoom your QGIS window to be roughly the same area as the map, 3. select the globe and lightning bolt icon "Generate GCP points with AI" to generate control points Make sure that whatever you try to georeference is north oriented. But, we are training it to handle rotated maps so that will be coming soon!

Our #QGIS AI Georeferencer is now available! You can now georeference north oriented aerial imagery and maps automatically in QGIS—here's how 👇 First, go to the Manage and Install Plugins window in QGIS and either update your Bunting Labs AI Vectorizer plugin to the latest version or install it if you haven't already Then, go to Layer -> Georeferencer and you will find two new buttons 🛰️ Aerial imagery georeferencing—our most powerful georeferencer. To use this: 1. load your satellite or aerial image in the georeferencer, 2. zoom your QGIS window to be roughly the same area as the image, 3. select the satellite icon "Generate GCP points from satellite" to generate control points 🌐⚡️ Full georeferencing—our experimental georeferencer. This mode uses our AI model built to read the map, identify features that a human would georeference to like roads, building outlines, and coordinates, and place ground control points. This allows our AI to georeference maps that do not have an aerial imagery base. To use this: 1. load your map in the georefernecer, 2. zoom your QGIS window to be roughly the same area as the map, 3. select the globe and lightning bolt icon "Generate GCP points with AI" to generate control points Make sure that whatever you try to georeference is north oriented. But, we are training it to handle rotated maps so that will be coming soon!

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𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐢𝐧 𝐐𝐆𝐈𝐒 𝐃𝐞𝐞𝐩𝐧𝐞𝐬𝐬: 𝐃𝐞𝐞𝐩 𝐍𝐞𝐮𝐫𝐚𝐥 𝐑𝐞𝐦𝐨𝐭𝐞 𝐒𝐞𝐧𝐬𝐢𝐧𝐠 : Inference of deep neural network models (ONNX) for segmentation, detection and regression Deepness plugin allows to easily perform segmentation, detection and regression on raster ortophotos with custom ONNX Neural Network models, bringing the power of deep learning to casual users. Features highlights: - processing any raster layer (custom ortophoto from file or layers from online providers, e.g Google Satellite) - limiting processing range to predefined area (visible part or area defined by vector layer polygons) - common types of models are supported: segmentation, regression, detection - integration with layers (both for input data and model output layers). Once an output layer is created, it can be saved as a file manually - model ZOO under development (planes detection on Bing Aerial, Corn field damage, Oil Storage tanks detection, cars detection, ...) - training data Export Tool - exporting raster and mask as small tiles - parametrization of the processing for advanced users (spatial resolution, overlap, postprocessing) Plugin requires external python packages to be installed. After the first plugin startup, a Dialog will show, to assist in this process. 𝐋𝐢𝐧𝐤 𝐏𝐥𝐮𝐠𝐢𝐧𝐬 : https://lnkd.in/dvN3Nyqk 𝐋𝐢𝐧𝐤 𝐌𝐨𝐝𝐞𝐥𝐬 : https://lnkd.in/dya9Z8Je 𝐀𝐛𝐨𝐮𝐭 𝐟𝐨𝐫 𝐏𝐥𝐮𝐠𝐢𝐧𝐬 : https://lnkd.in/dVhR7dCe Deep Learning is a field of Artificial Intelligence that focuses on teaching computer models to represent data in a way that supports prediction and classification. One important application of Deep Learning is extracting information from satellite images, where learning models can analyze these images accurately and extract objects and important information from them. When Deep Learning techniques are applied to extracting objects from satellite images, models can gradually and autonomously learn how to recognize patterns and distinctive features in these images and extract useful information accurately and effectively. #deeplearning #QGIS  #open_source #ai #gis #googlemap #Plugins #Remotesensing

🌍 Some Free Tools for Climate Modeling and Analysis🛠️ 1️⃣ NASA Earth Exchange (NEX) 🌎 A collaborative platform that provides high-performance computing resources, datasets, and models to explore global climate data. Perfect for running large-scale simulations and climate impact assessments. 👉 https://lnkd.in/d-xhv_wH 2️⃣ CLIMADA  Using state-of-the-art probabilistic modeling, CLIMADA allows to estimate the expected economic damage as a measure of risk today, the incremental increase from economic growth and the further incremental increase due to climate change. 👉 https://lnkd.in/d2sd-9xE 🛑( I am doing a FREE LIVE SESSION on what a climate career in geospatial data science looks like because of so many questions I’ve got over the past two years. Register here as I am aiming to keep it limited - https://lnkd.in/dm6AGF7a) 🛑 3️⃣ CMIP6 Datasets 🌐 The Coupled Model Intercomparison Project (CMIP6) offers data for climate model simulations from major global research centers. This data helps to project future climate scenarios based on different greenhouse gas emissions. 👉 https://lnkd.in/dvJ5yS-n 4️⃣ Climate Data Store (CDS) 🌀 Hosted by the Copernicus Climate Change Service, CDS provides open access to climate datasets, models, and tools to assess climate patterns, forecast changes, and measure impacts. 👉 https://lnkd.in/d67BerFw 5️⃣ NOAA Climate Toolkit 🌊 A collection of tools and data visualizations to study ocean temperatures, sea level rise, and extreme weather patterns. Ideal for researchers and educators focused on understanding climate variability. 👉 https://lnkd.in/dzu8vut7 6️⃣ OpenClimateGIS 🖥️ An open-source python library which is a GIS-based climate analysis tool, offering access to multi-dimensional climate datasets and enabling spatial and temporal climate projections. 👉https://lnkd.in/dz9_VBzT 7️⃣ IPCC Interactive Atlas 📊 An interactive platform to explore data from the Intergovernmental Panel on Climate Change (IPCC) reports, providing regional climate information for impact studies and risk analysis. 👉 https://lnkd.in/dwYRUj44 #ClimateDataScience #Python #DataScience #ClimateChange #Geospatial #gis #geospatialdatascience #satellitedata #remotesensing #climateaction #climatemodeling

The Hydrologic Engineering Center (HEC) of the U.S. Army Corps of Engineers has announced the alpha release of HEC-RAS 2025, marking a significant milestone in the evolution of hydraulic modeling software. https://lnkd.in/e6aUJxWm

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30 Essential QGIS Plugins Every GIS Professional Should Know - GIS Crack https://lnkd.in/ey48TehH by Mashford Mahute

Using Mesh Data In QGIS [including accessing NetCDF data] -- https://lnkd.in/gEixsWrr <-- shared tutorial -- https://lnkd.in/gnvK8WCx <-- tutorial as a video -- H/T Hans van der Kwast “Mesh data represents an unstructured grid that can include temporal and other components. It consists of vertices, edges, and faces that form a spatial structure in 2D or 3D space. Each vertex can store multiple datasets, which can also have a temporal dimension, making mesh data highly versatile for various applications such as meteorology, hydrodynamics, and environmental modeling… This course is designed to equip you with the knowledge and skills needed to effectively work with mesh data within the QGIS environment… Besides vector and raster, data sometimes comes in the mesh format. This format is often used in hydraulics, oceanography and meteorology. A mesh is an unstructured grid usually with temporal and other components. In this tutorial…:  • Style a precipitation mesh  • Animate a precipitation mesh  • Calculate precipitation sum with mesh calculator  • Extract precipitation at a specific location  • Make graphs with the Data Plotly plugin.." #QGIS #GIS #mesh #datavisualisation #NetCDF #climate #animation #openaccess #onlinelearning #learning #spatial #mapping #tutorial

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