Update dataset card row count

README.md

@@ -11,63 +11,63 @@ tags:
   - construction
   - infrastructure
   - geospatial
-  - street-level-imagery
-  - image-classification
+  - imagery
+  - computer-vision
 ---
 
 # Construction & Infrastructure Change Visual Dataset
 
+Rows: 55,801
+
 ## Dataset Description
 
-Construction & Infrastructure Change Visual Dataset is a global wildlife image dataset in the sense of a geographically indexed visual archive, focused here on construction monitoring, public works, and urban change detection. The labeled field in the dataset is `feature`, which records the matched visual feature from the query set: Construction Site, Roadwork, Crane, Excavator, Scaffolding, and Pothole. Labels were produced by Outerview internal visual search pipeline matching observations to query-driven embeddings, with location and timestamp metadata included when available. The files include `data.csv`, `data.geojson`, `data.parquet`, and a packaged image batch `images-batch-0001.zip` containing representative examples. The dataset is useful for image classification, geospatial computer vision dataset experiments, visual search, and monitoring infrastructure activity across Region: Global.
+Construction & Infrastructure Change Visual Dataset is a Global geospatial computer vision dataset of street-level imagery and related visual records focused on construction monitoring, public works, and infrastructure change. The labeled features in the dataset are Construction Site, Roadwork, Crane, Excavator, Scaffolding, and Pothole, each matched from the Outerview internal visual search pipeline using query-driven embedding similarity. The packaged files include tabular metadata in `data.csv`, `data.parquet`, and `data.geojson`, plus representative image archives in `images-batch-0001.zip` for visual review and image classification workflows. The dataset is useful for wildlife image dataset style browsing is not applicable here, but it is useful for visual search, change detection, geospatial analysis, municipal asset monitoring, and benchmark training for construction and infrastructure computer vision models. When image archives are included, the packaged images are representative examples and larger-scale image generation, retrieval, and review should be done through the API.
 
 This dataset is a sample from Outerview's full visual index of 10M+ real-world observations, powered by 35B visuals and observations. Search the full dataset for free at [outerview.ai](https://outerview.ai). API access and full dataset available at [outerview.ai](https://outerview.ai). Developer docs at [docs.outerview.ai](https://docs.outerview.ai).
 
-When image archives are included, the packaged images are representative examples and larger-scale image generation, retrieval, and review should be done through the API.
-
 Updated on a bimonthly basis.
 
 ## Dataset Structure
 
 ### Features in the dataset
 
-- `Construction Site`: query targets active building and site preparation areas.
-- `Roadwork`: query targets road maintenance, resurfacing, and traffic disruption zones.
-- `Crane`: query targets crane equipment visible in urban or industrial scenes.
-- `Excavator`: query targets excavator machinery used in earthmoving and construction.
-- `Scaffolding`: query targets temporary support structures around buildings.
-- `Pothole`: query targets damaged road surfaces and pavement defects.
+- Construction Site: query targets construction site scenes and active build areas.
+- Roadwork: query targets road maintenance and traffic disruption work zones.
+- Crane: query targets cranes and lifting equipment visible in the scene.
+- Excavator: query targets excavators and earthmoving machinery.
+- Scaffolding: query targets scaffolding structures around buildings and worksites.
+- Pothole: query targets potholes and road surface damage.
 
 ### Schema
 
 | Column | Type | Description |
 |---|---|---|
-| `filename` | string | Image filename in the exported image batch. |
-| `image_folder` | string | Relative folder inside the image zip batch. |
-| `date` | string | Capture date or source timestamp when available. |
-| `country` | string | Country associated with the visual record. |
-| `state` | string | State or province associated with the visual record. |
-| `city` | string | City or locality associated with the visual record. |
-| `feature` | string | Matched visual feature label. |
-| `latitude` | float64 | Latitude in WGS84 decimal degrees. |
-| `longitude` | float64 | Longitude in WGS84 decimal degrees. |
-| `source` | string | Source attribution for the visual record. |
+| filename | string | Image filename in the exported image batch. |
+| image_folder | string | Relative folder inside the image zip batch. |
+| date | string | Capture date or source timestamp when available. |
+| country | string | Country associated with the visual record. |
+| state | string | State or province associated with the visual record. |
+| city | string | City or locality associated with the visual record. |
+| feature | string | Matched visual feature label. |
+| latitude | float64 | Latitude in WGS84 decimal degrees. |
+| longitude | float64 | Longitude in WGS84 decimal degrees. |
+| source | string | Source attribution for the visual record. |
 
 ## Dataset Creation
 
 ### Source
 
-The source is Outerview's global visual index, assembled from query-driven matches over large-scale real-world imagery and spatial records. Observations are retrieved through the Outerview internal visual search pipeline using feature queries aligned to construction and infrastructure change classes.
+Records are drawn from Outerview's global visual index and selected through query-driven embedding match against construction and infrastructure-related visual concepts. The source material is organized into metadata and image batch files for downstream analysis and review.
 
 ### Annotation
 
-Annotation is feature-level only and is stored in the `feature` column. The labels correspond to `Construction Site`, `Roadwork`, `Crane`, `Excavator`, `Scaffolding`, and `Pothole`, with no bounding boxes or instance polygons included.
+Labels are feature-level matches for Construction Site, Roadwork, Crane, Excavator, Scaffolding, and Pothole. No bounding boxes are included, and annotation reflects the best matching feature label assigned by the Outerview internal visual search pipeline.
 
 ## Uses
 
 ### Intended Uses
 
-This dataset is suitable for municipal asset teams tracking public works, AV perception engineers evaluating road and construction scene understanding, insurance underwriters assessing infrastructure risk, environmental analysts studying land disturbance, and retail site selection teams analyzing nearby development activity. It also supports geospatial computer vision dataset benchmarking and image classification workflows on street-level imagery.
+Municipal asset teams can use the dataset for monitoring roads, worksites, and infrastructure changes over time. AV perception engineers, insurance underwriters, environmental analysts, and retail site selection teams can use it for image classification, geospatial analysis, visual search, and location-aware model development.
 
 ### Out-of-Scope Uses
 
@@ -94,4 +94,4 @@ Outerview is a research lab focused on understanding the physical world at plane
 
 We are building systems that can organize the world's physical information and make it accessible and usable transforming raw imagery, video, location, and spatial context into knowledge that people and machines can search, interpret, and act on.
 
-Our belief is simple: the physical world should be as searchable and understandable as the digital world.
+Our belief is simple: the physical world should be as searchable and understandable as the digital world.