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Checked by:
Category:
Language:
English
Total Size:
3.2 GB
Info Hash:
B35D042D2DEBD37904A94C92A8B1480355DB80B7
Added By:
Added:
Sept. 25, 2025, 11:35 p.m.
Stats:
|
(Last updated: Sept. 25, 2025, 11:38 p.m.)
| File | Size |
|---|---|
| Get Bonus Downloads Here.url | 180 bytes |
| 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 |
| 6 - StepbyStep Guide to GPU Setup.mkv | 172.7 MB |
| 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 |
| 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 |
| Center_Erbil.cpg | 5 bytes |
| Center_Erbil.dbf | 1.7 KB |
| Center_Erbil.prj | 145 bytes |
| Center_Erbil.sbn | 132 bytes |
| Center_Erbil.sbx | 116 bytes |
| Center_Erbil.shp | 3.5 KB |
| Center_Erbil.shp.DESKTOP-U07KOO1.15896.13176.sr.lock | 0 bytes |
| Center_Erbil.shx | 108 bytes |
| Erbil_Admi_3.cpg | 5 bytes |
| Erbil_Admi_3.dbf | 20.5 KB |
| Erbil_Admi_3.ebb | 872 bytes |
| Erbil_Admi_3.ed1 | 24.0 KB |
| Erbil_Admi_3.eq1 | 208 bytes |
| Erbil_Admi_3.prj | 143 bytes |
| Erbil_Admi_3.qpj | 257 bytes |
| Erbil_Admi_3.qtr | 136 bytes |
| Erbil_Admi_3.sbn | 324 bytes |
| Erbil_Admi_3.sbx | 132 bytes |
| Erbil_Admi_3.shp | 41.9 KB |
| Erbil_Admi_3.shx | 268 bytes |
| 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 |
| DrnMppr-DEM-AOI.tif | 17.1 MB |
| DrnMppr-DTM-AOI.tif | 8.6 MB |
| DrnMppr-ORT-AOI.tif | 119.6 MB |
| aoi.cpg | 5 bytes |
| aoi.dbf | 252 bytes |
| aoi.prj | 384 bytes |
| aoi.shp | 316 bytes |
| aoi.shx | 108 bytes |
| dem.tif | 17.1 MB |
| dtm.tif | 8.6 MB |
| ortho.tif | 119.6 MB |
| plant_count.cpg | 5 bytes |
| plant_count.dbf | 261 bytes |
| plant_count.prj | 384 bytes |
| plant_count.shp | 316 bytes |
| plant_count.shx | 108 bytes |
| plots_1.cpg | 5 bytes |
| plots_1.dbf | 5.0 KB |
| plots_1.prj | 384 bytes |
| plots_1.shp | 24.8 KB |
| plots_1.shx | 1.1 KB |
| plots_2.dbf | 4.3 KB |
| plots_2.prj | 384 bytes |
| plots_2.shp | 15.3 KB |
| plots_2.shx | 748 bytes |
| 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 |
| DrnMppr-DEM-AOI.tif | 17.1 MB |
| DrnMppr-DTM-AOI.tif | 8.6 MB |
| DrnMppr-ORT-AOI.tif | 119.6 MB |
| aoi.cpg | 5 bytes |
| aoi.dbf | 252 bytes |
| aoi.prj | 384 bytes |
| aoi.shp | 316 bytes |
| aoi.shx | 108 bytes |
| dem.tif | 17.1 MB |
| dtm.tif | 8.6 MB |
| ortho.tif | 119.6 MB |
| plant_count.cpg | 5 bytes |
| plant_count.dbf | 261 bytes |
| plant_count.prj | 384 bytes |
| plant_count.shp | 316 bytes |
| plant_count.shx | 108 bytes |
| plots_1.cpg | 5 bytes |
| plots_1.dbf | 5.0 KB |
| plots_1.prj | 384 bytes |
| plots_1.shp | 24.8 KB |
| plots_1.shx | 1.1 KB |
| plots_2.dbf | 4.3 KB |
| plots_2.prj | 384 bytes |
| plots_2.shp | 15.3 KB |
| plots_2.shx | 748 bytes |
| 28 - Course Summary and Key Takeaways.html | 5.7 KB |
| 29 - Next Steps and Additional Resources.html | 8.3 KB |
| Bonus Resources.txt | 70 bytes |
Name
DL
Uploader
Size
S/L
Added
-
413.5 MB
[15
/
4]
2024-12-03
| Uploaded by SunRiseZone | Size 413.5 MB | Health [ 15 /4 ] | Added 2024-12-03 |
-
780.5 MB
[0
/
0]
2023-06-02
| Uploaded by freecoursewb | Size 780.5 MB | Health [ 0 /0 ] | Added 2023-06-02 |
NOTE
SOURCE: Udemy Geospatial Ai Deep Learning For Satellite Imagery
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