Satellite & Aerial Imagery

Remote sensing teams annotate satellite, aerial, and drone imagery for land use classification, mapping, infrastructure monitoring, and environmental analysis. Avala provides polygon, segmentation, and classification tools optimized for overhead imagery, along with dataset management features for organizing large-scale image collections across geographies and time periods.

Data Types

Source	Avala Data Type	Typical Annotation
Satellite imagery	Image	Polygons, segmentation masks, classification
Aerial photography	Image	Bounding boxes, polygons
Drone imagery	Image, Video	Bounding boxes, polygons, segmentation
Orthophotos	Image	Segmentation masks, classification
Time-series composites	Image	Classification, change detection annotations

Common Tasks

Land Use Classification

Segment satellite and aerial images into land cover categories: urban, forest, water, agriculture, barren land, wetland, and other terrain types. Use the segmentation tool for pixel-level classification across the full image, or polygon annotation for region-level labeling. Label taxonomy example:

{
  "labels": [
    { "name": "urban", "color": "#FF4444" },
    { "name": "forest", "color": "#22AA22" },
    { "name": "water", "color": "#4488FF" },
    { "name": "agriculture", "color": "#AADD44" },
    { "name": "barren", "color": "#BBAA88" },
    { "name": "wetland", "color": "#66BBAA" }
  ]
}

For land use classification, define your categories based on the resolution of your imagery. High-resolution satellite (0.3-0.5 m/pixel) supports fine-grained categories like “residential” vs. “commercial.” Lower resolution (10-30 m/pixel) works better with broader categories like “urban” vs. “rural.”

Building Footprint Extraction

Trace polygon boundaries around buildings for mapping, urban planning, and change detection. The polygon tool supports:

Vertex-level editing for precise rooftop boundaries
Edge snapping for rectilinear buildings
Copy-and-adjust for repetitive structures (e.g., row houses)

Building footprint datasets are used in population estimation, disaster response mapping, and construction monitoring.

Vehicle and Object Counting

Detect and count vehicles, ships, aircraft, and other objects in overhead imagery using bounding box annotations. For dense scenes (parking lots, ports, airports), bounding boxes provide fast annotation with sufficient precision for counting and detection models.

Infrastructure Monitoring

Annotate infrastructure elements — roads, bridges, power lines, solar panels, pipelines — for condition assessment and change detection. Common approaches include:

Infrastructure	Annotation Type	Attributes
Roads	Polylines	Surface type, condition, width
Buildings	Polygons	Roof type, damage level, construction status
Power lines	Polylines	Span type, tower presence
Solar panels	Polygons	Panel count, orientation
Water bodies	Polygons	Type (river, lake, reservoir), boundaries

Change Detection

Compare images from different dates to identify changes: new construction, deforestation, flood extent, crop growth. Organize temporal image pairs in the same dataset and annotate changes with classification labels and polygon boundaries. Use metadata fields to tag images with collection date and location:

metadata.collection_date = "2026-01-15"
metadata.region = "san-francisco-bay"
metadata.satellite = "worldview-3"

Then filter with the query language to find specific temporal windows:

metadata.region = "san-francisco-bay" AND metadata.collection_date = "2026-01-15"

Dataset Organization

Satellite and aerial imagery datasets tend to be large — thousands to millions of images across geographies and time periods. Effective organization is critical.

Organization Strategies

Strategy	When to Use	Example
By region	Multi-geography projects	`north-america`, `europe`, `southeast-asia`
By collection date	Temporal analysis	`q1-2026`, `q2-2026`
By resolution	Mixed-resolution sources	`high-res-30cm`, `medium-res-10m`
By task	Different annotation goals	`building-footprints`, `land-cover`, `vehicle-counting`

Using Slices

Slices create virtual subsets without duplicating images:

Create a training slice with 80% of images and a validation slice with 20%
Create slices by geographic region for region-specific model evaluation
Create a difficult-cases slice for images that annotators frequently get wrong

Cloud Storage Integration

For large satellite imagery collections, use cloud storage integration to connect your S3 or GCS bucket directly. Avala reads images from your bucket without requiring a separate upload step.

Cloud storage is recommended for satellite imagery datasets over 10,000 images. It avoids the upload bottleneck and keeps your data in your own storage with your encryption and retention policies.

Avala Features Used

Feature	Purpose	Learn More
Polygon annotation	Building footprints, infrastructure boundaries	Polygon Tool
Segmentation annotation	Pixel-level land cover classification	Segmentation Tool
Bounding box annotation	Vehicle and object detection	Bounding Box Tool
Polyline annotation	Roads, power lines, and linear features	Polyline Tool
Classification	Scene-level land type and condition labels	Classification Tool
AutoTag	Similarity-based grouping for scene discovery	AutoTag
Dataset management	Organize imagery by region, date, and source	Managing Datasets
Slices	Create training/validation splits and region subsets	Slices API
Cloud storage	Connect S3 or GCS for large imagery collections	Cloud Storage
Quality control	Multi-stage review for mapping-grade accuracy	Quality Control

Example Pipeline

Satellite / aerial imagery collection
  -> Store in S3 or GCS bucket
  -> Connect to Avala via cloud storage integration
  -> Create dataset, attach metadata (region, date, satellite)
  -> Create slices for training/validation splits
  -> Create annotation project (segmentation for land cover, polygons for buildings)
  -> Annotators label imagery with review stages
  -> Export in COCO or GeoJSON-compatible format
  -> Train remote sensing model
  -> Use model predictions for next round of auto-labeling

Getting Started

Prepare your imagery

Convert images to JPEG or PNG format. For georeferenced data, keep the coordinate metadata in sidecar files or your GIS system — Avala handles the image pixels.

Upload or connect storage

For small datasets, upload directly. For large collections, connect your S3 or GCS bucket via cloud storage integration.

Organize with metadata and slices

Attach metadata (region, date, source satellite) to items. Create slices for training/validation splits and geographic subsets.

Create annotation project

Define your label taxonomy (land cover classes, building types, infrastructure categories). Choose the annotation type that fits your task.

Annotate and review

Distribute work across your team with work batches. Use multi-stage review for mapping-grade accuracy requirements.

Export and train

Export annotations and integrate with your remote sensing training pipeline via the API or SDK.

Next Steps

Polygon Tool

Precision boundary tracing for building footprints and infrastructure.

Segmentation Tool

Pixel-level classification for land cover and terrain mapping.

Cloud Storage

Connect your S3 or GCS bucket for large imagery collections.

Best Practices

Dataset organization, API usage, and annotation workflow optimization tips.

Getting Started

Platform

Use Cases

Data Types

Common Tasks

Land Use Classification

Building Footprint Extraction

Vehicle and Object Counting

Infrastructure Monitoring

Change Detection

Dataset Organization

Organization Strategies

Using Slices

Cloud Storage Integration

Avala Features Used

Example Pipeline

Getting Started

Next Steps

​Data Types

​Common Tasks

​Land Use Classification

​Building Footprint Extraction

​Vehicle and Object Counting

​Infrastructure Monitoring

​Change Detection

​Dataset Organization

​Organization Strategies

​Using Slices

​Cloud Storage Integration

​Avala Features Used

​Example Pipeline

​Getting Started

​Next Steps

Data Types

Common Tasks

Land Use Classification

Building Footprint Extraction

Vehicle and Object Counting

Infrastructure Monitoring

Change Detection

Dataset Organization

Organization Strategies

Using Slices

Cloud Storage Integration

Avala Features Used

Example Pipeline

Getting Started

Next Steps