Glossary
Earth Observation & AI/ML Terms
How to Use This Glossary
This glossary defines key terms used throughout the CoPhil EO AI/ML Training Programme. Terms are organized alphabetically within categories for easy reference.
Categories: - Earth Observation Terms - AI/ML Terms - Geospatial Data Terms - Satellite & Sensor Terms - Philippine EO Organizations - Technical Acronyms
Earth Observation Terms
Absorption Band
Wavelength region where atmospheric gases (water vapor, oxygen, CO2) absorb electromagnetic radiation, limiting remote sensing capabilities.
Backscatter
The portion of radar energy reflected back to the sensor from a target. Used in SAR imaging to detect surface properties and moisture.
Cloud Masking
The process of identifying and removing cloud-contaminated pixels from optical satellite imagery to improve data quality.
Composite Image
A single image created by combining multiple images from different dates, often using statistical methods (median, mean) to reduce noise and clouds.
Earth Observation (EO)
The gathering of information about Earth’s physical, chemical, and biological systems through remote sensing technologies, primarily satellites.
False Color Composite
An image display where spectral bands are assigned to RGB colors differently than natural vision (e.g., NIR-Red-Green), revealing features invisible to the human eye.
Ground Truth
Field-collected reference data used to validate remote sensing classifications and train machine learning models.
Image Collection
A set of satellite images covering the same geographic area at different times, used for time series analysis.
Pixel
The smallest unit in a raster image, representing a specific ground area (spatial resolution) and spectral value.
Preprocessing
Steps taken to correct raw satellite data before analysis, including atmospheric correction, geometric correction, and radiometric calibration.
Remote Sensing
The science of obtaining information about objects or areas from a distance, typically using sensors on satellites or aircraft.
Revisit Time
The frequency with which a satellite can observe the same location on Earth (e.g., Sentinel-2 has 5-day revisit with 3 satellites).
Spectral Signature
The unique reflectance pattern of an object across different wavelengths, used to identify materials and land cover types.
True Color Composite
An image display using red, green, and blue bands to create a natural-looking image similar to human vision.
AI/ML Terms
Activation Function
Mathematical function in neural networks that introduces non-linearity (e.g., ReLU, Sigmoid, Tanh), enabling learning of complex patterns.
Artificial Intelligence (AI)
Computer systems capable of performing tasks that typically require human intelligence, including perception, reasoning, and decision-making.
Backpropagation
Algorithm for training neural networks by calculating gradients of loss and adjusting weights to minimize error.
Batch Size
Number of training samples processed before updating model weights. Smaller batches = more updates but noisier; larger batches = smoother but fewer updates.
Classification
Supervised learning task of assigning input data to predefined categories (e.g., forest, water, urban).
Clustering
Unsupervised learning technique that groups similar data points together without predefined labels (e.g., K-means, DBSCAN).
Confusion Matrix
Table showing predicted vs. actual classifications, used to calculate accuracy, precision, recall, and F1-score.
Convolutional Neural Network (CNN)
Deep learning architecture specialized for image analysis, using convolutional layers to detect spatial patterns.
Data Augmentation
Technique to artificially increase training data by applying transformations (rotation, flipping, scaling) to existing samples.
Deep Learning
Subset of machine learning using multi-layer neural networks to learn hierarchical representations of data.
Epoch
One complete pass through the entire training dataset during model training.
Feature Engineering
The process of creating new input variables from raw data to improve model performance.
Feature Extraction
Identifying and extracting relevant patterns or characteristics from raw data for use in machine learning models.
Ground Truth Labels
Verified, accurate labels for training data, typically from field surveys or expert interpretation.
Hyperparameter
Model configuration setting chosen before training (e.g., learning rate, number of layers) that affects model performance.
Loss Function
Mathematical function measuring the difference between predicted and actual values, used to guide model training.
Machine Learning (ML)
Subset of AI enabling systems to learn and improve from experience without explicit programming.
Neural Network
Computing system inspired by biological brains, consisting of interconnected nodes (neurons) organized in layers.
Overfitting
When a model learns training data too well, including noise, resulting in poor performance on new data.
Precision
Proportion of positive predictions that are actually correct. Precision = TP / (TP + FP).
Random Forest
Ensemble learning method using multiple decision trees to improve prediction accuracy and reduce overfitting.
Recall (Sensitivity)
Proportion of actual positives correctly identified. Recall = TP / (TP + FN).
Regression
Supervised learning task of predicting continuous numerical values (e.g., crop yield, temperature).
Supervised Learning
Machine learning where models learn from labeled training data (input-output pairs).
Support Vector Machine (SVM)
Classification algorithm that finds the optimal hyperplane separating different classes in feature space.
Training Set
Portion of data used to train a machine learning model (typically 70-80% of total data).
Transfer Learning
Reusing a pre-trained model on a new but related task, reducing training time and data requirements.
Underfitting
When a model is too simple to capture the underlying patterns in data, resulting in poor performance.
Unsupervised Learning
Machine learning where models find patterns in unlabeled data without predefined categories.
Validation Set
Data used to evaluate model performance during training and tune hyperparameters (typically 10-15% of data).
Geospatial Data Terms
Affine Transformation
Mathematical operation describing the relationship between pixel coordinates and geographic coordinates in raster data.
Bounding Box
Rectangular area defined by minimum and maximum coordinates [minX, minY, maxX, maxY], used to specify geographic extents.
Coordinate Reference System (CRS)
System defining how coordinates relate to real-world locations, including datum and projection (e.g., WGS84, UTM).
Digital Elevation Model (DEM)
Raster representation of terrain elevation, with each pixel value representing height above a reference level.
Feature
In geospatial terms, a vector object (point, line, or polygon) with associated attributes.
GeoJSON
Open standard JSON format for encoding geographic data structures, widely used for web mapping.
GeoPackage
Open format for geospatial data storage in SQLite database, supporting both vector and raster data.
Geometry
The spatial component of a geographic feature, defining its shape and location (point, line, polygon).
GeoTIFF
Raster image format with embedded geographic metadata (CRS, extent, resolution), standard for geospatial raster data.
NoData Value
Special value in raster data indicating missing or invalid data (e.g., -9999, NaN).
Pixel Resolution (Spatial Resolution)
Ground area represented by one pixel (e.g., 10m means each pixel covers 10m × 10m on the ground).
Projection
Mathematical transformation converting 3D Earth coordinates to 2D map coordinates (e.g., Mercator, UTM).
Raster Data
Grid-based spatial data where each cell (pixel) contains a value, used for continuous phenomena (elevation, temperature, imagery).
Reproject
Converting geospatial data from one coordinate reference system to another.
Shapefile
Popular vector data format for GIS, consisting of multiple files (.shp, .shx, .dbf, .prj).
Spatial Join
Combining attributes from two geospatial datasets based on their spatial relationship (intersection, within, etc.).
Vector Data
Spatial data representing discrete features as points, lines, or polygons with associated attributes.
Well-Known Text (WKT)
Text markup language for representing vector geometry and spatial reference systems.
Satellite & Sensor Terms
Active Sensor
Sensor that emits its own energy and measures the reflected signal (e.g., SAR, LiDAR).
Aperture
Opening in a sensor that controls the amount of light collected, affecting image brightness and quality.
Atmospheric Correction
Processing step removing atmospheric effects (scattering, absorption) to retrieve surface reflectance.
C-band
Radar frequency band (4-8 GHz, wavelength 3.75-7.5 cm) used by Sentinel-1, good for vegetation and soil moisture.
Electromagnetic Spectrum
Range of all electromagnetic radiation wavelengths, from radio waves to gamma rays, including visible light.
Geometric Correction
Correcting image distortions caused by sensor viewing angle, terrain, and Earth’s curvature.
Level 1C (L1C)
Sentinel-2 product with Top-of-Atmosphere (TOA) reflectance, geometrically corrected.
Level 2A (L2A)
Sentinel-2 product with Bottom-of-Atmosphere (BOA) surface reflectance, atmospherically corrected.
LiDAR
Light Detection and Ranging - active sensor using laser pulses to measure distance, creating high-resolution 3D point clouds.
Multispectral
Imaging system capturing data in multiple (typically 3-15) wavelength bands across visible and infrared spectrum.
Near Infrared (NIR)
Electromagnetic radiation with wavelengths 0.7-1.4 μm, strongly reflected by healthy vegetation.
Optical Sensor
Passive sensor detecting reflected sunlight in visible and infrared wavelengths (e.g., Sentinel-2, Landsat).
Orbit
Path of a satellite around Earth, characterized by altitude, inclination, and period.
Panchromatic
Single-band imagery capturing all visible wavelengths, typically at higher spatial resolution than multispectral bands.
Passive Sensor
Sensor detecting naturally available energy, typically reflected sunlight (e.g., optical cameras).
Polarization
Orientation of radar waves (HH, VV, HV, VH), providing information about surface structure and moisture.
Radiometric Calibration
Converting raw sensor digital numbers to physical units (radiance or reflectance).
SAR (Synthetic Aperture Radar)
Active microwave imaging system creating high-resolution images independent of sunlight and clouds.
Short-Wave Infrared (SWIR)
Electromagnetic radiation with wavelengths 1.4-3.0 μm, useful for detecting moisture and minerals.
Spectral Resolution
Ability to distinguish between different wavelengths, determined by number and width of spectral bands.
Sun-Synchronous Orbit
Satellite orbit maintaining constant local solar time, ensuring consistent illumination conditions.
Swath Width
Width of the ground strip imaged by a satellite in a single pass (e.g., Sentinel-2 has 290 km swath).
Temporal Resolution
Frequency of repeat observations over the same location (synonymous with revisit time).
Thermal Infrared (TIR)
Electromagnetic radiation with wavelengths 8-14 μm, measuring heat emitted by Earth’s surface.
Philippine EO Organizations
CoPhil Programme
EU-Philippines Copernicus Capacity Support Programme - partnership to strengthen Philippine EO capabilities, establish a Copernicus Mirror Site, and develop AI/ML capacity.
DENR
Department of Environment and Natural Resources - responsible for forest monitoring, land cover mapping, and natural resource management using EO data.
DOST
Department of Science and Technology - leads science and technology advancement, co-chairs CoPhil programme, operates PAGASA and ASTI.
DOST-ASTI
Advanced Science and Technology Institute - ICT research and development, AI platforms (SkAI-Pinas, DIMER, AIPI).
LiPAD
LiDAR Portal for Archiving and Distribution - repository of high-resolution elevation data for the Philippines.
NAMRIA
National Mapping and Resource Information Authority - official mapping agency, operates GeoPortal and Resource Data Analysis Center.
NDRRMC
National Disaster Risk Reduction and Management Council - coordinates disaster response, uses EO data for assessment and planning.
PAGASA
Philippine Atmospheric, Geophysical and Astronomical Services Administration - weather, climate, and astronomical services.
PhilGIS
Philippine GIS Data Clearinghouse - repository of geospatial datasets for the Philippines.
PhilSA
Philippine Space Agency - national space authority, co-chairs CoPhil programme, operates SIYASAT portal and develops Copernicus Mirror Site.
SIYASAT Portal
PhilSA’s secure data archive for NovaSAR-1 data and maritime monitoring products.
Technical Acronyms
AI
Artificial Intelligence - computer systems performing tasks requiring human intelligence.
API
Application Programming Interface - set of functions allowing software to interact with services (e.g., Earth Engine Python API).
ASTER
Advanced Spaceborne Thermal Emission and Reflection Radiometer - NASA sensor providing multispectral imagery.
BOA
Bottom of Atmosphere - surface reflectance after atmospheric correction (Level 2A products).
CCA
Climate Change Adaptation - strategies and actions to adjust to climate change impacts.
CNN
Convolutional Neural Network - deep learning architecture for image analysis.
CRS
Coordinate Reference System - defines how coordinates relate to Earth’s surface.
DEM
Digital Elevation Model - raster representation of terrain elevation.
DL
Deep Learning - subset of ML using multi-layer neural networks.
DRR
Disaster Risk Reduction - strategies to minimize disaster impacts.
EO
Earth Observation - gathering information about Earth through remote sensing.
ESA
European Space Agency - operates Copernicus Sentinel missions.
EVI
Enhanced Vegetation Index - vegetation index less sensitive to atmospheric effects than NDVI.
GEE
Google Earth Engine - cloud platform for planetary-scale geospatial analysis.
GIS
Geographic Information System - software for capturing, managing, and analyzing spatial data.
GPU
Graphics Processing Unit - hardware accelerating deep learning computations.
GNSS
Global Navigation Satellite System - satellite-based positioning (GPS, Galileo, GLONASS).
HDF
Hierarchical Data Format - file format for storing large scientific datasets.
IW
Interferometric Wide Swath - Sentinel-1 acquisition mode with 250 km swath.
JAXA
Japan Aerospace Exploration Agency - operates Japanese Earth observation satellites.
LiDAR
Light Detection and Ranging - laser-based remote sensing for elevation mapping.
LULC
Land Use Land Cover - classification of Earth’s surface into categories (forest, urban, agriculture, etc.).
ML
Machine Learning - algorithms enabling systems to learn from data.
MODIS
Moderate Resolution Imaging Spectroradiometer - NASA sensor providing daily global coverage.
NASA
National Aeronautics and Space Administration - operates Landsat and other EO missions.
NDBI
Normalized Difference Built-up Index - spectral index for detecting built-up areas.
NDVI
Normalized Difference Vegetation Index - spectral index measuring vegetation health/density.
NDWI
Normalized Difference Water Index - spectral index for detecting water bodies.
NIR
Near Infrared - electromagnetic radiation beyond visible red (0.7-1.4 μm).
NRM
Natural Resource Management - sustainable management of natural resources using EO monitoring.
RGB
Red-Green-Blue - color model using three primary colors, or the corresponding image bands.
RF
Random Forest - ensemble machine learning algorithm using multiple decision trees.
RL
Reinforcement Learning - ML paradigm where agents learn through interaction with environment.
SAR
Synthetic Aperture Radar - active microwave imaging system.
SCL
Scene Classification Layer - Sentinel-2 cloud and land cover classification mask.
SNAP
Sentinel Application Platform - ESA’s free software for processing Sentinel data.
SWIR
Short-Wave Infrared - electromagnetic radiation with wavelengths 1.4-3.0 μm.
SVM
Support Vector Machine - classification algorithm finding optimal separating hyperplane.
TIR
Thermal Infrared - electromagnetic radiation measuring surface temperature (8-14 μm).
TOA
Top of Atmosphere - apparent reflectance before atmospheric correction (Level 1C products).
USGS
United States Geological Survey - distributes Landsat and other EO data.
UTM
Universal Transverse Mercator - widely-used projected coordinate system dividing Earth into zones.
WGS84
World Geodetic System 1984 - global geographic coordinate system (EPSG:4326).
Spectral Indices
NDVI (Normalized Difference Vegetation Index)
Formula: (NIR - Red) / (NIR + Red)
Range: -1 to +1
Interpretation: - High values (0.6-0.9): Dense vegetation - Moderate (0.2-0.5): Sparse vegetation - Near zero: Bare soil, rock - Negative: Water, clouds
Sentinel-2 Bands: (B8 - B4) / (B8 + B4)
NDWI (Normalized Difference Water Index)
McFeeters Formula: (Green - NIR) / (Green + NIR)
Gao Formula: (NIR - SWIR) / (NIR + SWIR)
Range: -1 to +1
Interpretation: - Positive values: Water bodies - Negative: Land surfaces
Sentinel-2 (McFeeters): (B3 - B8) / (B3 + B8)
Sentinel-2 (Gao): (B8 - B11) / (B8 + B11)
NDBI (Normalized Difference Built-up Index)
Formula: (SWIR - NIR) / (SWIR + NIR)
Range: -1 to +1
Interpretation: - Positive values: Built-up areas - Negative: Vegetation, water
Sentinel-2: (B11 - B8) / (B11 + B8)
EVI (Enhanced Vegetation Index)
Formula: 2.5 × ((NIR - Red) / (NIR + 6×Red - 7.5×Blue + 1))
Range: -1 to +1
Advantages: Less sensitive to atmospheric effects and soil background than NDVI
Sentinel-2: 2.5 × ((B8 - B4) / (B8 + 6×B4 - 7.5×B2 + 1))
Quick Reference Tables
Sentinel-2 Band Summary
| Band | Name | Wavelength (nm) | Resolution (m) | Typical Use |
|---|---|---|---|---|
| B1 | Coastal aerosol | 443 | 60 | Atmospheric correction |
| B2 | Blue | 490 | 10 | Bathymetry, soil/vegetation |
| B3 | Green | 560 | 10 | Peak vegetation sensitivity |
| B4 | Red | 665 | 10 | Vegetation discrimination |
| B5 | Red Edge 1 | 705 | 20 | Vegetation health |
| B6 | Red Edge 2 | 740 | 20 | Vegetation stress |
| B7 | Red Edge 3 | 783 | 20 | Vegetation stress |
| B8 | NIR | 842 | 10 | Biomass, water bodies |
| B8A | Narrow NIR | 865 | 20 | Atmospheric correction |
| B9 | Water vapor | 945 | 60 | Atmospheric correction |
| B11 | SWIR 1 | 1610 | 20 | Moisture, soil/vegetation |
| B12 | SWIR 2 | 2190 | 20 | Moisture, burned areas |
Common CRS for Philippines
| Name | EPSG Code | Type | Use Case |
|---|---|---|---|
| WGS84 | 4326 | Geographic | Global datasets, web maps |
| UTM Zone 50N | 32650 | Projected | Western Mindanao |
| UTM Zone 51N | 32651 | Projected | Luzon, Visayas, most of Philippines |
| UTM Zone 52N | 32652 | Projected | Eastern Mindanao |
| PRS92 | 4683 | Geographic | Philippine Reference System |