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

# Land Monitoring & Catastrophe Prediction

> How resource, land management, and hazard analytics teams can use Wherobots to track land change, model risk, and stay ahead of catastrophe.

The following notebook examples discuss how Wherobots enables organizations to: track land change over time, model exposure to environmental catastrophe, and prove compliance across large and remote footprints.

## How to use these solutions

Treat these notebooks as data engineering patterns as you evaluate Wherobots for your business or organization:

<CardGroup cols={2}>
  <Card title="Baseline & Survey Imagery" icon="magnifying-glass-location" iconType="solid" href="/tutorials/example-notebooks/stac-reader">
    Pull satellite and aerial imagery from STAC catalogs to establish a land baseline you can measure every later survey against.
  </Card>

  <Card title="Environmental Monitoring" icon="leaf" iconType="solid" href="/tutorials/example-notebooks/rasterflow-changedetection">
    Detect land-use change, deforestation, and habitat disruption across your footprint using RasterFlow computer vision models on satellite imagery.
  </Card>

  <Card title="Regulatory Compliance" icon="shield-check" iconType="solid" href="/tutorials/example-notebooks/part-4-spatial-joins">
    Overlay operational boundaries with protected areas, water bodies, and jurisdictional zones to automate compliance checks through spatial joins.
  </Card>

  <Card title="Severe Weather Exposure" icon="cloud-bolt" iconType="solid" href="/tutorials/example-notebooks/noaa-swdi">
    Correlate NOAA Severe Weather Data Inventory records with your operational footprint to model exposure to storms, hail, and tornado activity.
  </Card>
</CardGroup>

## Key Capabilities

<AccordionGroup>
  <Accordion title="Satellite-based change detection" icon="satellite">
    Use RasterFlow to run change detection models on multi-temporal satellite imagery. Identify deforestation, land disturbance, and reclamation progress over time without manual inspection.

    <Card title="Change detection with RasterFlow" icon="arrows-rotate" href="/tutorials/example-notebooks/rasterflow-changedetection" horizontal>
      Compare imagery across dates and quantify what moved.
    </Card>
  </Accordion>

  <Accordion title="Canopy and vegetation analysis" icon="tree">
    Estimate canopy height and vegetation density from aerial or satellite imagery to establish environmental baselines and measure recovery against them.

    <Card title="Estimating canopy height" icon="tree" href="/tutorials/example-notebooks/rasterflow-chm" horizontal>
      Predict tree heights from aerial imagery to baseline vegetation.
    </Card>
  </Accordion>

  <Accordion title="Spatial risk modeling" icon="triangle-exclamation">
    Perform floodplain analysis, slope stability assessments, and proximity-to-hazard calculations by joining raster terrain data with vector asset inventories using WherobotsDB.

    <Card title="California coastal flood risk" icon="house-flood-water" href="/tutorials/example-notebooks/california-coastal-flood-risk-analysis" horizontal>
      Join hazard zones against parcel and asset records to quantify what is exposed.
    </Card>
  </Accordion>

  <Accordion title="Custom models on your own imagery" icon="microchip">
    Bring your own drone, aerial, or satellite GeoTIFFs into a RasterFlow mosaic and run your own PyTorch model over them to detect site-specific features such as tailings, wellheads, or haul roads.

    <Columns cols={2}>
      <Card title="Bring your own rasters" icon="image" href="/tutorials/example-notebooks/rasterflow-bring-your-own-rasters-naip" horizontal>
        Mosaic your own GeoTIFFs into analysis-ready imagery.
      </Card>

      <Card title="Bring your own model" icon="wand-magic-sparkles" href="/tutorials/example-notebooks/rasterflow-bring-your-own-model" horizontal>
        Run a custom PyTorch model over that imagery at scale.
      </Card>
    </Columns>
  </Accordion>

  <Accordion title="Land classification at scale" icon="layer-group">
    Classify land cover types across large regions using ESA WorldCover or custom datasets, enabling informed decisions about where to operate and where to avoid.

    <Card title="Exploring ESA WorldCover" icon="layer-group" href="/tutorials/example-notebooks/esa-worldcover" horizontal>
      Classify land cover at 10 m resolution anywhere on earth.
    </Card>
  </Accordion>
</AccordionGroup>

## Wherobots Advantage

| Challenge                                       | How Wherobots Helps                                                                                                              |
| ----------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- |
| Petabytes of imagery and survey data            | Serverless architecture scales to planetary-scale datasets without infrastructure management                                     |
| Multi-format data (vector, raster, point cloud) | Unified analytics engine processes vector and raster data in a single workflow                                                   |
| Remote and distributed sites                    | Cloud-native platform accessible from anywhere with built-in Amazon S3 Tables, AWS Glue, or Databricks Unity Catalog integration |
| Regulatory reporting deadlines                  | Automate spatial compliance checks with Airflow-orchestrated pipelines                                                           |

<Card title="Get started with Wherobots" icon="rocket" href="/get-started/wherobots-cloud/start-notebook" cta="Run your first notebook">
  Try Wherobots with your own geospatial data in minutes.
</Card>