Geospatial Analysis

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Geospatial Analysis

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Step 1: Create Your First Notebook

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Quick Start

1. Navigate to Notebooks: Click on “Notebooks” in the left sidebar
2. Click ”+” Button: Click the ”+” button in the notebook list
3. Select “Blank Notebook”: Choose “Blank Notebook” from the dropdown menu
4. Auto-Save Enabled: Your notebook is automatically saved

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Rename Your Notebook

• Click Title Area: Click on the notebook title at the top left
• Type New Name: Enter a descriptive name like “Geospatial Analysis Project”
• Professional Naming: Use clear, descriptive names for your projects

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Step 2: Start Your Machine

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Select Machine Configuration

• CPUx3: 3 CPU cores, 24GB RAM, ready in under 30 seconds (recommended for beginners)
• CPUx7: 7 CPU cores, 53GB RAM, ready in over 2 minutes
• GPU Machines: For machine learning and complex processing

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Start Machine

1. Click “Start Machine”: Click the prominent white button
2. Wait for Ready: Machine will be ready in 15-40 seconds
3. Monitor Status: Hover over the green dot to check machine status

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Step 3: Add Content Blocks

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Using the ”+” Icon

1. Find ”+” Button: Look for the ”+” icon on the left of any row
2. Click ”+”: Click to add a new block
3. Select Block Type: Choose from code, markdown, alerts, or media blocks

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Using Keyboard Shortcut

• Press ”/”: Type ”/” in any empty row to trigger block selection
• Choose Block: Select the desired block type from the menu

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Available Block Types

• Code Block: For Python code execution
• Alert Block: Highlight important information
• Headings: Create section headers
• Media: Insert images, videos, or file attachments

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Step 4: Run Your Code

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Multiple Execution Methods

1. Hover Menu: Hover over code block and click the play button (▶️)
2. Keyboard Shortcut: Select block and press Shift+Enter
3. Run All: Use “Run All” button in top-right toolbar for sequential execution

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Streaming Execution

• Real-time Output: Results stream in from the kernel
• Background Processing: Long-running code continues even if you close the workspace
• Return Later: Come back hours later to see completed results

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Step 5: Use Pre-installed Geospatial Libraries

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GDAL and CUDA Ready

• GDAL 3.9.1: Geospatial Data Abstraction Library
• CUDA 12.2: NVIDIA CUDA for GPU acceleration
• Python 3.10/3.11: Multiple Python versions available

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Example Geospatial Code

# Import geospatial libraries
import rasterio
import geopandas as gpd
import numpy as np

# Load raster data
with rasterio.open('/workspace/data/satellite.tif') as src:
    data = src.read()
    print(f"Raster size: {src.width} x {src.height}")

# Load vector data
gdf = gpd.read_file('/workspace/data/shapefile.shp')
print(f"Number of features: {len(gdf)}")

# Perform analysis
ndvi = (data[3] - data[2]) / (data[3] + data[2])
print(f"NDVI calculated successfully")

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Step 6: Use AI-Powered Features

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Quick AI Edit

• Press Cmd/Ctrl + K: Get AI suggestions for code improvements
• Green Suggestions: AI suggestions appear in green text
• Preview Execution: Test suggested changes before accepting
• Accept/Reject: Choose which suggestions to keep

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Error Resolution with GAIA

• “Resolve in GAIA” Button: Click when you encounter errors
• AI Analysis: GAIA analyzes your entire notebook context
• Chat Interface: Ask questions and get AI assistance
• Direct Modifications: AI can modify your notebook directly

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Step 7: Publish Your Analysis

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Prepare for Publishing

1. Finalize Your Analysis: Ensure all code blocks are executed
2. Add Documentation: Include markdown blocks explaining your work
3. Test Functionality: Verify all interactive features work

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Publish Your Notebook

1. Right-click Notebook: Right-click on your notebook in the list
2. Select “Publish”: Choose the publish option from the context menu
3. Fill Required Fields: Enter title*, version*, and category*
4. Generate Thumbnail: Use AI generation if you don’t have a thumbnail
5. Set Visibility: Choose Team Only or Public access
6. Click Publish: Finalize your publication

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View Published Notebook

• Click Arrow Icon: Click the arrow icon in the publish dialog
• New Tab Opens: Your published notebook opens in a new tab
• Static View: Notebook is view-only for others
• Public Hub: If public, appears in the NikaHub

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Key Features Summary

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Easy to Use

• Auto-save: Everything is automatically saved
• Streaming Execution: Long-running tasks continue in background
• Multiple Execution Methods: Hover, keyboard shortcuts, or run all
• Block Reordering: Easy reordering with arrow buttons

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Powerful Analysis

• Pre-installed Libraries: GDAL, CUDA, and Python ready to use
• Large Dataset Support: Work with TB-PB sized geospatial data
• GPU Acceleration: Use CUDA for faster processing
• Real-time Linting: Code quality feedback as you type

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AI-Powered

• Quick AI Edit: Get code suggestions with Cmd/Ctrl + K
• Error Resolution: Use GAIA for intelligent error fixing
• Context-Aware: AI understands your entire notebook
• Chat Interface: Natural language interaction with AI

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Collaboration Ready

• Team Publishing: Share with team members
• Public Hub: Share with the broader community
• Version Control: Maintain different versions of your work
• Easy Updates: Modify and republish as needed

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Next Steps

1. Explore Libraries: Check Supported Python Libraries
2. Advanced Features: Learn about AI Coding in Notebook
3. Machine Management: Understand Starting Your Machine
4. Publishing: Master Publishing Your Notebook

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