Skip to main content

​
Start With Your Question

Most spatial work starts as a business question, not a feature request. Open your industry to see the questions Wherobots answers, what each one produces, and the notebook that implements it.
“Where has the land changed since our last survey, and how much?”You get dated change polygons showing disturbance, clearing, or regrowth, by running a change detection model across multi-temporal satellite imagery instead of inspecting scenes by hand.

Change detection with RasterFlow

Compare imagery across dates and quantify what moved.
“Which of our operations fall inside protected areas or hazard zones?”You get a flag on every operational boundary that overlaps a protected area, waterway, or hazard zone, from a single spatial join against regulatory boundaries.

Spatial joins in Wherobots

Join asset footprints against boundary datasets at scale.
“Which buildings in our portfolio have roof conditions that affect insurability?”You get per-building detections with confidence scores, joined back to your asset records, by describing the feature in a text prompt and running SAM3 geometry inference over sub-meter aerial imagery.

Detecting objects from text prompts

Describe a feature in words and get georeferenced geometries back.
“Which sites in our portfolio carry the most location risk?”You get a risk score per facility, ranked across the whole portfolio, by overlaying site locations with flood zones, storm history, and hazard layers.

Exploring storm data

Correlate NOAA severe weather records with your locations.
“How many customers can reach each potential new site in 15 minutes?”You get drive-time catchments for every candidate, with population and competitor counts inside each, by generating isochrones in batch and spatial-joining them to demographic and POI data.

Making drive time isochrones

Build travel-time polygons for thousands of locations at once.
“Where are competitors densest, and where is the gap?”You get competitor counts and market saturation around every candidate site, by joining candidate locations against POI datasets and clustering the results.

Foursquare Places

Work with POI data to map the competitive landscape.

DBSCAN clustering

Find natural groupings in demand and competitor locations.
“Are drivers deviating from the most optimized routes?”You get every trip snapped to real road segments, with true distances and the deviations flagged, by map-matching raw GPS traces against the road network at fleet scale.

Matching vehicle GPS data to road networks

Snap noisy GPS traces to the roads they were actually driven on.
“Which trips look anomalous and deserve a second look?”You get a ranked list of outlier trips and GPS points, by applying Local Outlier Factor to your trajectory data rather than eyeballing dashboards.

Local Outlier Factor

Surface spatial outliers that summary statistics hide.
“What can I infer about crop yield for a future season?”You get per-field vegetation trajectories across the growing season, the input a yield model needs, by mosaicking Sentinel-2 imagery over time and summarizing it inside detected field boundaries.

Building Sentinel-2 mosaics

Assemble cloud-free imagery across an area and a season.

Detecting field boundaries

Delineate every field so metrics can be summarized per parcel.
“How much tree canopy do we have, and where is it declining?”You get a canopy height surface plus per-parcel and per-zone summaries, by estimating height with RasterFlow and aggregating it with zonal statistics.

Estimating canopy height

Predict tree heights from aerial imagery.

Zonal statistics

Summarize raster values inside your own boundaries.
“How much of our property portfolio sits within a flood zone and how much asset value is exposed?”You get exposed parcel counts and assessed value at risk, broken out by city and hazard zone, by spatial-joining policy or parcel records against FEMA hazard polygons in WherobotsDB.

California coastal flood risk

Quantify parcels and assessed value inside FEMA flood zones.
“Are our risks quietly concentrating somewhere dangerous?”You get statistically significant hotspots rather than visual clusters, by applying Getis-Ord Gi* to claims, losses, or insured values across your book.

Hotspot detection

Separate real concentrations from noise with Getis-Ord Gi*.
“What is this watershed actually made of, and how is it shifting?”You get land cover composition per watershed, district, or custom boundary, comparable across years, by classifying land cover at 10 m resolution and summarizing it by zone.

Exploring ESA WorldCover

Classify land cover at 10 m resolution anywhere on earth.

Zonal statistics

Roll land cover up to the boundaries you care about.
“How do we work with satellite imagery without downloading any of it?”You get to query, filter, and process imagery collections in place, by reading STAC-compliant catalogs directly into WherobotsDB.

STAC reader and API

Search and load imagery collections without moving files.
“Is this retail corridor served well by existing walkability?”You get a sidewalk and crosswalk inventory for the corridor, scored for continuity, by detecting pedestrian infrastructure from aerial imagery instead of sending a survey crew.

Detecting sidewalks with RasterFlow

Map sidewalks, crosswalks, and pedestrian paths from imagery.
“Are existing sidewalks viable pathways for autonomous or human delivery?”You get the pedestrian network as routable geometry, with gaps and discontinuities visible, so you can judge which segments carry a delivery robot or a courier on foot.

Detecting sidewalks with RasterFlow

Turn detected pathways into vector geometry you can route on.
“How do we keep our analysis from going stale without re-running it by hand?”You get a scheduled pipeline that refreshes the result on its own cadence and alerts you when it fails, by orchestrating the notebook or SQL job from Airflow.

Wherobots Airflow provider

Run notebooks, SQL, and job runs on a schedule.
Wherobots powers geospatial analytics and AI for organizations that depend on location intelligence. From monitoring land change to keeping a fleet on route, Wherobots delivers the spatial compute, data integration, and AI capabilities these workflows demand. Each page below maps an industry’s workflows to the Wherobots features and runnable example notebooks that support them.

Land Monitoring & Catastrophe Prediction

Land change detection, hazard exposure modeling, compliance checks, and vegetation analysis across large footprints.

Facility Management

Building analytics, portfolio risk scoring, space planning, and infrastructure assessment across distributed properties.

Site Selection

Trade area analysis, demographic scoring, competitive mapping, and accessibility evaluation for optimal location decisions.

Fleet Logistics

GPS map matching, route adherence, fleet analytics, and road infrastructure monitoring at scale.

Agriculture & Forestry

Field boundary detection, crop monitoring, canopy height estimation, and land cover classification from satellite imagery.

Insurance & Risk

Catastrophe exposure analysis, underwriting enrichment, weather analytics, and portfolio concentration monitoring.

Environmental Monitoring

Change detection, climate analytics, habitat mapping, and water resource monitoring at continental scale.

Last Mile Delivery

Pedestrian network mapping, delivery zone modeling, drop-off resolution, and coverage maps for the final leg of the delivery journey.

Ready to get started?

Create a free Wherobots account and run your first spatial analysis in minutes.