Frequently Asked Questions

Common Questions & Troubleshooting

Date

November 17, 2025

General Questions

What is the CoPhil Programme?

The Technical Assistance for the Philippines’ Copernicus Capacity Support Programme (CoPhil) is an EU-Philippines cooperation initiative under the Global Gateway strategy. It aims to:

  • Establish a Copernicus Mirror Site in the Philippines
  • Build capacity in EO data analysis and AI/ML
  • Co-develop pilot services for DRR, CCA, and NRM
  • Create a sustainable Digital Space Campus for training

Key Partners: Philippine Space Agency (PhilSA), Department of Science and Technology (DOST), European Union, European Space Agency (ESA)

Who should attend this training?

This training is designed for:

  • Philippine government employees working in EO, disaster management, agriculture, or environment
  • Researchers at universities and research institutions
  • GIS professionals looking to expand into AI/ML
  • Data scientists interested in geospatial applications
  • PhilSA, NAMRIA, DOST-ASTI, PAGASA, DENR staff

Prerequisites: Basic Python knowledge and familiarity with remote sensing concepts (helpful but not required)

What are the technical requirements?

Minimum: - Google account (Gmail) - Stable internet (5 Mbps+) - Modern web browser (Chrome, Firefox, Safari, Edge) - 4 GB RAM

Recommended: - 10+ Mbps internet - Chrome browser (best compatibility) - 8+ GB RAM - Dual monitors (one for presentation, one for coding)

Note: All exercises run in Google Colaboratory - no local software installation required!

Do I need to install Python locally?

No! All hands-on exercises use Google Colaboratory, which provides:

  • Free cloud computing resources
  • Pre-installed Python libraries
  • Access to GPUs
  • No local installation needed

However, if you prefer working locally, we provide installation guides in the Setup Guide.

Is this training free?

Yes! The CoPhil training programme is fully funded by the European Union under the Global Gateway initiative. There are no fees for participants.

Setup & Account Issues

How do I register for Google Earth Engine?

  1. Go to earthengine.google.com
  2. Click “Get Started” or “Sign Up”
  3. Sign in with your Google account
  4. Select “Noncommercial” (for this training)
  5. Fill out the registration form:
    • Organization: Your institution
    • Project description: “CoPhil EO AI/ML Training”
  6. Submit and wait for approval (24-48 hours)
Important

Register at least 2 days before the training starts to ensure approval!

For detailed instructions, see the Setup Guide.

My Earth Engine registration is taking too long

Normal approval time: 24-48 hours

If it’s been longer: 1. Check your spam folder for the approval email 2. Verify registration status at earthengine.google.com 3. Re-submit registration if it shows as not received 4. Contact Earth Engine support: earthengine-support@google.com

I forgot to authenticate Earth Engine in Colab

Symptoms: ee commands throw errors like “Please set project ID”

Solution:

import ee

# Authenticate (follow prompts)
ee.Authenticate()

# Initialize
ee.Initialize()

This only needs to be done once per Google account. Future sessions will remember your credentials.

Google Colab says “Runtime disconnected”

Causes: - 90 minutes of inactivity - Maximum session length (12 hours for free accounts) - Browser tab closed or crashed

Solution: 1. Click “Reconnect” button 2. Re-run setup cells (imports, authentication) 3. Continue from where you left off

Tip

Prevent disconnections: - Keep browser tab active - Save work to Google Drive regularly - Use Ctrl/Cmd + S to save notebooks

How do I save my work in Google Colab?

Option 1: Save to Drive (Recommended)

from google.colab import drive
drive.mount('/content/drive')

# Save outputs to Drive
output_path = '/content/drive/My Drive/CoPhil_Training/'

Option 2: Download Files - Click folder icon in left sidebar - Right-click file → Download

Option 3: Save Notebook - File → Save a copy in Drive - File → Download .ipynb

Python & Coding Issues

I’m getting “ModuleNotFoundError”

Example: ModuleNotFoundError: No module named 'geopandas'

Cause: Package not installed in current Colab session

Solution:

# Install the missing package
!pip install geopandas

# Then import it
import geopandas as gpd

Common packages to install: - geopandas - rasterio - earthengine-api - geemap

Package installation is failing

Error: ERROR: Could not find a version that satisfies the requirement...

Solutions:

1. Update pip first:

!pip install --upgrade pip
!pip install geopandas

2. Install specific version:

!pip install geopandas==0.14.0

3. Use conda (if local):

conda install -c conda-forge geopandas

4. Force reinstall:

!pip install --upgrade --force-reinstall geopandas

My code runs locally but fails in Colab

Common causes:

1. File paths: - Local: C:/Users/name/data.shp - Colab: /content/data.shp or from Drive

2. Package versions: - Check versions: import package; print(package.__version__) - Install specific version if needed

3. Missing files: - Upload files: Click folder icon → Upload - Or mount Google Drive

“MemoryError” or “Kernel crashed”

Causes: - Loading too much data - Processing large rasters - Insufficient RAM

Solutions:

1. Reduce data scope:

# Read smaller window
window = rasterio.windows.Window(0, 0, 1000, 1000)
data = src.read(1, window=window)

# Or downsample
data = src.read(1, out_shape=(src.height // 4, src.width // 4))

2. Use chunking:

# Process in chunks
for window in src.block_windows():
    data = src.read(1, window=window)
    process(data)

3. Enable GPU in Colab: - Runtime → Change runtime type → GPU

4. Upgrade to Colab Pro: - More RAM (up to 50 GB) - Longer sessions - colab.research.google.com/signup

Earth Engine Issues

“User memory limit exceeded” in Earth Engine

Cause: Trying to process too much data at once

Solutions:

1. Increase scale parameter:

# Before (10m resolution)
result = image.reduceRegion(
    reducer=ee.Reducer.mean(),
    geometry=roi,
    scale=10  # 10m pixels
)

# After (100m resolution)
result = image.reduceRegion(
    reducer=ee.Reducer.mean(),
    geometry=roi,
    scale=100  # 100m pixels
)

2. Reduce region size:

# Smaller bounding box
small_roi = ee.Geometry.Rectangle([120.0, 14.0, 120.5, 14.5])

3. Filter dates more strictly:

# Shorter time period
collection = collection.filterDate('2024-01-01', '2024-01-31')  # 1 month instead of 1 year

4. Use maxPixels parameter:

task = ee.batch.Export.image.toDrive(
    image=image,
    scale=10,
    maxPixels=1e13  # Allow more pixels
)

Earth Engine export is stuck at “RUNNING”

Check status:

# Check task status
print(task.status())

Possible statuses: - READY: Queued, waiting to start - RUNNING: Currently processing - COMPLETED: Successfully finished - FAILED: Error occurred (check status for details)

If stuck: 1. Wait - large exports can take hours 2. Check Earth Engine Task Manager 3. Cancel and restart with smaller parameters 4. Check Google Drive storage space

Cloud-free composite still has clouds

Cause: Cloud masking didn’t work perfectly

Solutions:

1. Use better cloud masking:

def aggressive_cloud_mask(image):
    qa = image.select('QA60')
    # Mask both cloud and cirrus
    cloud_mask = qa.bitwiseAnd(1 << 10).eq(0).And(
                 qa.bitwiseAnd(1 << 11).eq(0))
    # Also mask cloud shadows
    scl = image.select('SCL')
    shadow_mask = scl.neq(3)  # 3 = cloud shadow
    return image.updateMask(cloud_mask).updateMask(shadow_mask)

2. Use percentile reduction instead of median:

# Use 20th percentile (darker, less clouds)
composite = collection.reduce(ee.Reducer.percentile([20]))

3. Filter by cloud cover first:

collection = collection.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))

Data & Visualization Issues

My map doesn’t display in Colab

Cause: Missing visualization library

Solution:

# Install geemap for interactive maps
!pip install geemap

import geemap

# Create map
Map = geemap.Map()
Map.centerObject(roi, 10)
Map.addLayer(image, vis_params, 'Image')
Map

Colors in my visualization look wrong

Check visualization parameters:

# For Sentinel-2 true color
vis_params = {
    'bands': ['B4', 'B3', 'B2'],  # Red, Green, Blue
    'min': 0,
    'max': 3000,  # Adjust based on your data
    'gamma': 1.4
}

# For NDVI
ndvi_vis = {
    'min': -1,
    'max': 1,
    'palette': ['red', 'yellow', 'green']
}

Adjust min/max: - Too dark → decrease max value - Too bright → increase max value - Washed out → adjust gamma

GeoPandas plot shows nothing

Common issues:

1. Empty GeoDataFrame:

print(len(gdf))  # Check if it has rows
print(gdf.head())

2. Wrong CRS:

print(gdf.crs)  # Check coordinate reference system
gdf = gdf.to_crs('EPSG:4326')  # Reproject if needed

3. Data outside visible area:

print(gdf.total_bounds)  # Check bounding box
gdf.plot(figsize=(10, 10))  # Larger figure size

Rasterio shows “All-NaN slice encountered”

Cause: Trying to visualize a band with all nodata values

Solution:

# Check for valid data
print(f"Min: {band.min()}, Max: {band.max()}")
print(f"Valid pixels: {np.count_nonzero(~np.isnan(band))}")

# Mask nodata
valid_mask = ~np.isnan(band)
if valid_mask.any():
    plt.imshow(band, cmap='gray')
else:
    print("No valid data in this band")

Philippine-Specific Questions

Where can I get Philippine administrative boundaries?

Sources:

  1. PhilGIS: philgis.org
    • Shapefile format
    • All administrative levels
  2. NAMRIA GeoPortal: geoportal.namria.gov.ph
    • Official government source
    • Registration may be required
  3. Humanitarian Data Exchange: data.humdata.org
    • Open data
    • GeoJSON and Shapefile
  4. In Earth Engine:
# FAO GAUL administrative boundaries
philippines = ee.FeatureCollection("FAO/GAUL/2015/level1") \
    .filter(ee.Filter.eq('ADM0_NAME', 'Philippines'))

How do I get Sentinel data specifically for the Philippines?

In Google Earth Engine:

# Define Philippines bounding box
philippines_bbox = ee.Geometry.Rectangle([116.0, 4.0, 127.0, 21.0])

# Filter Sentinel-2 collection
collection = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') \
    .filterBounds(philippines_bbox) \
    .filterDate('2024-01-01', '2024-12-31') \
    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))

print(f"Found {collection.size().getInfo()} images")

Via Copernicus Data Space: 1. Go to dataspace.copernicus.eu 2. Use SentiBoard to browse visually 3. Draw bounding box over Philippines 4. Filter by date and cloud cover 5. Download tiles

What are the best satellite data sources for Philippine disasters?

Floods: - Sentinel-1 SAR (works through clouds) - Planet Labs (daily imagery, commercial) - Landsat-8/9 (free, 16-day revisit)

Typhoons: - Sentinel-2 (damage assessment) - MODIS (rapid assessment) - Himawari-8 (near real-time, via PAGASA)

Landslides: - PlanetScope (3m resolution) - Sentinel-2 (10m, change detection) - LiDAR (elevation, via LiPAD portal)

Drought: - MODIS (vegetation indices, 8-day) - Sentinel-2 (higher resolution) - SMAP (soil moisture)

Training-Specific Questions

Can I get a certificate for completing this training?

Yes! Participants who complete all 4 days and pass the final assessment will receive a CoPhil Training Programme Certificate issued by PhilSA and DOST.

Requirements: - Attend all 4 days - Complete hands-on exercises - Submit final project - Pass assessment (70% minimum)

Will the training materials be available after the course?

Yes! All materials will remain accessible:

  • Training portal stays online
  • Notebooks available on GitHub
  • Recorded sessions (if applicable)
  • Ongoing access to Digital Space Campus (under development)

Can I share these materials with colleagues?

Yes! All training materials are licensed under Creative Commons BY-SA 4.0, which means you can:

  • Share freely
  • Use for teaching
  • Modify and adapt
  • Use commercially

Requirements: - Provide attribution: “CoPhil EO AI/ML Training Programme” - Share derivatives under the same license

What comes after Day 1?

Day 2: Classical Machine Learning for Land Cover Classification - Random Forests - Support Vector Machines - Feature engineering - Palawan land cover case study

Day 3: Deep Learning for Flood Mapping & Object Detection - U-Net architecture - Sentinel-1 flood detection - YOLOv8 for infrastructure - Central Luzon flood case study

Day 4: Advanced Topics & Practical Application - Foundation models for EO - Time series analysis - Transfer learning - Final project

Technical Support

Who do I contact for technical issues?

During training: - Ask in the live session chat - Consult teaching assistants - Check this FAQ first

Outside training hours: - Email: skotsopoulos@neuralio.ai - GitHub Issues: Report issue

I found an error in the training materials

Thank you for helping improve the training!

To report: 1. GitHub Issues: Create issue 2. Email: skotsopoulos@neuralio.ai 3. During session: Notify instructors

Include: - Which session/notebook - What the error is - Steps to reproduce - Your environment (Colab or local)

Additional Resources

Where can I learn more about Earth Observation?

Online Courses: - Copernicus Training - ESA EO Training - Google Earth Engine Tutorials

Books: - “Remote Sensing and Image Interpretation” - Lillesand et al. - “Python for Geospatial Data Analysis” - Garrard - “Deep Learning for the Earth Sciences” - Camps-Valls et al.

Communities: - Google Earth Engine Developers - Stack Exchange GIS - r/gis

Where can I get help with Python programming?

Learning Resources: - Python.org Tutorial - Real Python - DataCamp

Getting Help: - Stack Overflow - r/learnpython

Still Have Questions?

TipCan’t Find Your Answer?

Contact Us: - Email: skotsopoulos@neuralio.ai - During training: Ask instructors directly

We’re here to help ensure your success in the training!

This FAQ is regularly updated based on participant questions. Last updated: 2025-01-15