Udemy - Geospatial Ai - Deep Learning For Satellite Imagery

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size3.2 GB
  • Uploaded Byfreecoursewb
  • Downloads122
  • Last checkedSep. 30th '25
  • Date uploadedSep. 26th '25
  • Seeders 6
  • Leechers12

Infohash : B35D042D2DEBD37904A94C92A8B1480355DB80B7

Geospatial Ai: Deep Learning For Satellite Imagery

https://WebToolTip.com

Published 9/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.18 GB | Duration: 4h 25m

Build AI Models for Geospatial Data and Satellite Imagery

What you'll learn
Preprocess satellite imagery for AI using Python and Google Earth Engine.
Build and train CNNs for geospatial tasks like crop health classification.
Apply deep learning to analyze satellite data for real-world applications.
Evaluate and optimize AI models with metrics and hyperparameter tuning.

Requirements
No prior experience needed! Basic Python knowledge is helpful but not required. You'll need a computer, internet access, and a free Google account for Google Colab. All tools and datasets are provided in the course!

Files:

[ WebToolTip.com ] Udemy - Geospatial Ai - Deep Learning For Satellite Imagery
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction to Geospatial AI and Satellite Imagery
    • 1 - Welcome and Course Overview.html (6.0 KB)
    • 2 - Introduction to Geospatial Analysis.mkv (127.2 MB)
    • 3 - Introduction to Artificial Intelligence.mkv (17.7 MB)
    • 4 - Why Python is the Top Choice for AI.mkv (13.8 MB)
    • 5 - Overview of Deep Learning in Geospatial Applications.mkv (124.2 MB)
    2 - Setting Up Your Deep Learning Environment
    • 6 - StepbyStep Guide to GPU Setup.mkv (172.7 MB)
    3 - CloudBased AI with Google Colab
    • 10 - Running PyTorch Models in the Cloud.mkv (61.0 MB)
    • 11 - Saving and Sharing Colab Notebooks.mkv (78.4 MB)
    • 7 - Introduction to Goggle Colab.mkv (36.9 MB)
    • 8 - Setting Up Google Colab for AI Projects.mkv (78.5 MB)
    • 9 - Running TensorFlow Models in the Cloud.mkv (83.9 MB)
    4 - Preprocessing Satellite Imagery for Deep Learning
    • 12 - Calculating Geospatial Indices.mkv (104.8 MB)
    • 13 - Import and Clean Datasets in Jupyter Notebook with Pandas.mkv (129.9 MB)
    • 14 - Calculate-zonal-statistics-1.ipynb (5.9 KB)
    • 14 - Chirps-Erbil.tif (9.0 KB)
    • 14 - Conducting Zonal Statistics in Python.mkv (53.4 MB)
    • 15 - Image-Preprocessing-for-Deep-Learning.ipynb (6.3 KB)
    • 15 - Preprocessing Real Sentinel2 Imagery for Deep Learning.mkv (113.7 MB)
    • 16 - Integrating Google Earth Engine for Data Pipelines.mkv (71.5 MB)
    • 16 - Integrating-Google-Earth-Engine-for-Data-Pipelines.ipynb (5.0 KB)
    • 17 - Working with LargeScale Geospatial Data.mkv (134.7 MB)
    • 17 - Working-with-Large-Scale-Geospatial-Data.ipynb (8.0 KB)
      • Erbil_Shapefile
      • Center_Erbil.cpg (0.0 KB)
      • Center_Erbil.dbf (1.7 KB)
      • Center_Erbil.prj (0.1 KB)
      • Center_Erbil.sbn (0.1 KB)
      • Center_Erbil.sbx (0.1 KB)
      • Center_Erbil.shp (3.5 KB)
      • Center_Erbil.shp.DESKTOP-U07KOO1.15896.13176.sr.lock (0.0 KB)
      • Center_Erbil.shx (0.1 KB)
      • Erbil_Admi_3.cpg (0.0 KB)
      • Erbil_Admi_3.dbf (20.5 KB)
      • Erbil_Admi_3.ebb (0.9 KB)
      • Erbil_Admi_3.ed1 (24.0 KB)
      • Erbil_Admi_3.eq1 (0.2 KB)
      • Erbil_Admi_3.prj (0.1 KB)
      • Erbil_Admi_3.qpj (0.3 KB)
      • Erbil_Admi_3.qtr (0.1 KB)
      • Erbil_Admi_3.sbn (0.3 KB)
      • Erbil_Admi_3.sbx (0.1 KB)
      • Erbil_Admi_3.shp (41.9 KB)
      • Erbil_Admi_3.shx (0.3 KB)
      5 - Building Convolutional Neural Networks CNNs for Geospatial Tasks
      • 18 - Introduction to CNNs for Satellite Imagery Analysis.mkv (150.7 MB)
      • 19 - Crop-Health-Using-RS-data-and-Neural-Networks.ipynb (67.2 KB)
      • 19 - Designing a CNN Model for Crop Health Classification.mkv (124.8 MB)
      • 20 - Visualizing AI Model Performance.mkv (178.0 MB)
      • 20 - Visualizing-ML-Model-Performance.ipynb (69.5 KB)
      • 20 - crop-health.csv (32.8 KB)
      • 21 - Evaluating models Accuracy precision recall and crossvalidation.mkv (48.9 MB)
      • 21 - Evaluating-Models-Accuracy-Precision-Recall-and-Cross-Validation.ipynb (4.5 KB)
      • 21 - crop-health.csv (32.8 KB)
      • 22 - Hyperparameter Tuning with Grid Search and Random Search in Python.mkv (113.7 MB)
      • 22 - Hyperparameter-Tuning-with-Grid-Search-and-Random-Search.ipynb (8.2 KB)
      • 22 - crop-health.csv (32.8 KB)
        • data
        • DrnMppr-DEM-AOI.tif (17.1 MB)
        • DrnMppr-DTM-AOI.tif (8.6 MB)
        • DrnMppr-ORT-AOI.tif (119.6 MB)
        • aoi.cpg (0.0 KB)
        • aoi.dbf (0.2 KB)
        • aoi.prj (0.4 KB)
        • aoi.shp (0.3 KB)
        • aoi.shx (0.1 KB)
        • dem.tif (17.1 MB)
        • dtm.tif (8.6 MB)
        • ortho.tif (119.6 MB)
        • plant_count.cpg (0.0 KB)
        • plant_count.dbf (0.3 KB)
        • plant_count.prj (0.4 KB)
        • plant_count.shp (0.3 KB)
        • plant_count.shx (0.1 KB)
        • plots_1.cpg (0.0 KB)
        • plots_1.dbf (5.0 KB)
        • plots_1.prj (0.4 KB)
        • plots_1.shp (24.8 KB)
        • plots_1.shx (1.1 KB)
        • plots_2.dbf (4.3 KB)
        • plots_2.prj (0.4 KB)
        • plots_2.shp (15.3 KB)
        • plots_2.shx (0.7 KB)
        6 - Advanced Geospatial AI Applications
        • 23 - Building a Convolutional Neural Network for Image Classification.mkv (211.8 MB)
        • 23 - CNNs-LULC-Classification-EuroSAT-Azad-Rasul.ipynb (813.5 KB)
        • 24 - Building an AI Model for Crop Health Analysis.mkv (124.9 MB)
        • 24 - Crop-Health-Using-RS-data-and-Neural-Networks.ipynb (67.2 KB)
        • 25 - Detecting-and-Counting-Plants-Using-Computer-Vision-Techniques.ipynb (1.3 MB)
        • 25 - Plant Counting with Computer Vision Techniques.mkv (170.1 MB)
        • 26 - 1.1-FourCastNet-A-practical-introduction-to-a-state-of-the-art-deep-learning-global-weather-emulator.ipynb (1.7 MB)
        • 26 - 1.2-FourCastNet-Added-Iraq.ipynb (2.3 MB)
        • 26 - Applying Deep Learning for Global Weather Emulation with FourCastNet.html (1.8 KB)
        • 27 - Validating Biomass Predictions with Ground Truth.mkv (159.0 MB)
        • 27 - Validation-with-Ground-Truth-Biomass-Focus.ipynb (159.3 KB)
          • data
          • DrnMppr-DEM-AOI.tif (17.1 MB)
          • DrnMppr-DTM-AOI.tif (8.6 MB)
          • DrnMppr-ORT-AOI.tif (119.6 MB)
          • aoi.cpg (0.0 KB)
          • aoi.dbf (0.2 KB)
          • aoi.prj (0.4 KB)
          • aoi.shp (0.3 KB)
          • aoi.shx (0.1 KB)
          • dem.tif (17.1 MB)
          • dtm.tif (8.6 MB)
          • ortho.tif (119.6 MB)
          • plant_count.cpg (0.0 KB)
          • plant_count.dbf (0.3 KB)
          • plant_count.prj (0.4 KB)

          • Code:

            • udp://tracker.torrent.eu.org:451/announce
            • udp://tracker.tiny-vps.com:6969/announce
            • http://tracker.foreverpirates.co:80/announce
            • udp://tracker.cyberia.is:6969/announce
            • udp://exodus.desync.com:6969/announce
            • udp://explodie.org:6969/announce
            • udp://tracker.opentrackr.org:1337/announce
            • udp://9.rarbg.to:2780/announce
            • udp://tracker.internetwarriors.net:1337/announce
            • udp://ipv4.tracker.harry.lu:80/announce
            • udp://open.stealth.si:80/announce
            • udp://9.rarbg.to:2900/announce
            • udp://9.rarbg.me:2720/announce
            • udp://opentor.org:2710/announce