| 1 - Welcome and Course Overview.html | 6 KB | ||
| 10 - Running PyTorch Models in the Cloud.mkv | 61 MB | ||
| 11 - Saving and Sharing Colab Notebooks.mkv | 78.4 MB | ||
| 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 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 KB | ||
| 17 - Working with LargeScale Geospatial Data.mkv | 134.7 MB | ||
| 17 - Working-with-Large-Scale-Geospatial-Data.ipynb | 8 KB | ||
| 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 | ||
| 2 - Introduction to Geospatial Analysis.mkv | 127.2 MB | ||
| 20 - Visualizing AI Model Performance.mkv | 178 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 | ||
| 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 MB | ||
| 27 - Validation-with-Ground-Truth-Biomass-Focus.ipynb | 159.3 KB | ||
| 28 - Course Summary and Key Takeaways.html | 5.7 KB | ||
| 29 - Next Steps and Additional Resources.html | 8.3 KB | ||
| 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 | ||
| 6 - StepbyStep Guide to GPU Setup.mkv | 172.7 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 | ||
| Bonus Resources.txt | 102.4 B | ||
| Center_Erbil.cpg | 0 B | ||
| Center_Erbil.dbf | 1.7 KB | ||
| Center_Erbil.prj | 102.4 B | ||
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| Center_Erbil.sbx | 102.4 B | ||
| Center_Erbil.shp | 3.5 KB | ||
| Center_Erbil.shp.DESKTOP-U07KOO1.15896.13176.sr.lock | 0 B | ||
| Center_Erbil.shx | 102.4 B | ||
| DrnMppr-DEM-AOI.tif | 17.1 MB | ||
| DrnMppr-DTM-AOI.tif | 8.6 MB | ||
| DrnMppr-ORT-AOI.tif | 119.6 MB | ||
| Erbil_Admi_3.cpg | 0 B | ||
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| Erbil_Admi_3.ed1 | 24 KB | ||
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| Erbil_Admi_3.prj | 102.4 B | ||
| Erbil_Admi_3.qpj | 307.2 B | ||
| Erbil_Admi_3.qtr | 102.4 B | ||
| Erbil_Admi_3.sbn | 307.2 B | ||
| Erbil_Admi_3.sbx | 102.4 B | ||
| Erbil_Admi_3.shp | 41.9 KB | ||
| Erbil_Admi_3.shx | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| aoi.cpg | 0 B | ||
| aoi.dbf | 204.8 B | ||
| aoi.prj | 409.6 B | ||
| aoi.shp | 307.2 B | ||
| aoi.shx | 102.4 B | ||
| dem.tif | 17.1 MB | ||
| dtm.tif | 8.6 MB | ||
| ortho.tif | 119.6 MB | ||
| plant_count.cpg | 0 B | ||
| plant_count.dbf | 307.2 B | ||
| plant_count.prj | 409.6 B | ||
| plant_count.shp | 307.2 B | ||
| plant_count.shx | 102.4 B | ||
| plots_1.cpg | 0 B | ||
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| plots_1.shx | 1.1 KB | ||
| plots_2.dbf | 4.3 KB | ||
| plots_2.prj | 409.6 B | ||
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| plots_2.shx | 716.8 B | ||
| ▲ 119 total files | |||
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!
| torrent name | size | uploader | age | seed | leech |
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| 718.7 MB | freecoursewb | 4 months | 7 | 10 | |
| 413.5 MB | SunRiseZone | 1 year | 4 | 1 | |
| 413.4 MB | SunRiseZone | 1 year | 28 | 25 | |
| 1 GB | freecoursewb | 2 years | 0 | 0 | |
| 3 GB | freecoursewb | 4 years | 0 | 0 |
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