> ## Documentation Index
> Fetch the complete documentation index at: https://avala.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Satellite & Aerial Imagery

> Annotate satellite, aerial, and drone imagery for remote sensing and mapping applications

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:**

```json theme={null}
{
  "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" }
  ]
}
```

<Tip>
  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."
</Tip>

### 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](/docs/annotation/reference/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](/docs/api-reference/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](/docs/integrations/cloud-storage) to connect your S3 or GCS bucket directly. Avala reads images from your bucket without requiring a separate upload step.

<Tip>
  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.
</Tip>

## Avala Features Used

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

<Steps>
  <Step title="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.
  </Step>

  <Step title="Upload or connect storage">
    For small datasets, upload directly. For large collections, connect your S3 or GCS bucket via cloud storage integration.
  </Step>

  <Step title="Organize with metadata and slices">
    Attach metadata (region, date, source satellite) to items. Create slices for training/validation splits and geographic subsets.
  </Step>

  <Step title="Create annotation project">
    Define your label taxonomy (land cover classes, building types, infrastructure categories). Choose the annotation type that fits your task.
  </Step>

  <Step title="Annotate and review">
    Distribute work across your team with work batches. Use multi-stage review for mapping-grade accuracy requirements.
  </Step>

  <Step title="Export and train">
    Export annotations and integrate with your remote sensing training pipeline via the API or SDK.
  </Step>
</Steps>

## Next Steps

<CardGroup cols={2}>
  <Card title="" icon="draw-polygon" href="/docs/annotation/guides/tools/polygon">
    <p style={{fontWeight: 600, fontSize: '18px', marginBottom: '4px', marginTop: '8px', color: 'inherit'}}>Polygon Tool</p>
    <p style={{fontSize: '14px', marginTop: '0px', opacity: 0.6}}>Precision boundary tracing for building footprints and infrastructure.</p>
  </Card>

  <Card title="" icon="paintbrush" href="/docs/annotation/guides/tools/segmentation">
    <p style={{fontWeight: 600, fontSize: '18px', marginBottom: '4px', marginTop: '8px', color: 'inherit'}}>Segmentation Tool</p>
    <p style={{fontSize: '14px', marginTop: '0px', opacity: 0.6}}>Pixel-level classification for land cover and terrain mapping.</p>
  </Card>

  <Card title="" icon="cloud" href="/docs/integrations/cloud-storage">
    <p style={{fontWeight: 600, fontSize: '18px', marginBottom: '4px', marginTop: '8px', color: 'inherit'}}>Cloud Storage</p>
    <p style={{fontSize: '14px', marginTop: '0px', opacity: 0.6}}>Connect your S3 or GCS bucket for large imagery collections.</p>
  </Card>

  <Card title="" icon="star" href="/docs/resources/best-practices">
    <p style={{fontWeight: 600, fontSize: '18px', marginBottom: '4px', marginTop: '8px', color: 'inherit'}}>Best Practices</p>
    <p style={{fontSize: '14px', marginTop: '0px', opacity: 0.6}}>Dataset organization, API usage, and annotation workflow optimization tips.</p>
  </Card>
</CardGroup>