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README.md

@@ -11,15 +11,15 @@ tags:
   - construction
   - infrastructure
   - geospatial
-  - imagery
-  - classification
+  - street-level-imagery
+  - image-classification
 ---
 
 # Construction & Infrastructure Change Visual Dataset
 
 ## Dataset Description
 
-Construction & Infrastructure Change Visual Dataset is a global wildlife image dataset for geospatial computer vision dataset work focused on urban change, public works, and street-level imagery. Labels are assigned to images by matching each visual record to one of 6 feature targets in the dataset: Construction Site, Roadwork, Crane, Excavator, Scaffolding, and Pothole. The dataset includes `data.csv`, `data.geojson`, `data.parquet`, and a packaged representative image archive (`images-batch-0001.zip`) for sample review and image classification experiments. It is useful for visual search, mapping construction activity, monitoring infrastructure change, and benchmarking multi-class image classification on real-world observations. Region: Global.
+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.
 
 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).
 
@@ -31,43 +31,43 @@ Updated on a bimonthly basis.
 
 ### Features in the dataset
 
-- Construction Site: queries images matching active or completed construction zones.
-- Roadwork: queries images showing road repairs, lane closures, or utility work.
-- Crane: queries images containing cranes used in construction or heavy lifting.
-- Excavator: queries images containing excavators and similar earthmoving equipment.
-- Scaffolding: queries images showing scaffolding around buildings or worksites.
-- Pothole: queries images showing road surface damage and repair targets.
+- `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.
 
 ### 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 records come from Outerview's visual index, which organizes real-world imagery through the Outerview internal visual search pipeline and query-driven embedding match source selection. Each sample is selected to align with one of the defined feature labels across Global coverage.
+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.
 
 ### Annotation
 
-Annotation is label-only and uses the exact feature targets from "Features in the dataset" to assign each record to Construction Site, Roadwork, Crane, Excavator, Scaffolding, or Pothole. No bounding boxes or instance masks are included, and the labels reflect the matched visual feature for each image.
+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.
 
 ## Uses
 
 ### Intended Uses
 
-This dataset is suitable for municipal asset teams tracking construction and road maintenance, AV perception engineers evaluating urban scene recognition, insurance underwriters assessing infrastructure change, and environmental analysts monitoring built-environment activity. Retail site selection teams can also use it for surrounding-area inspection and visual search workflows.
+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.
 
 ### Out-of-Scope Uses
 
@@ -80,11 +80,11 @@ This dataset is released under the [CC BY 4.0](https://creativecommons.org/licen
 ## Citation
 
 ```bibtex
-@dataset{outerview_construction-infrastructure-change-visual-data-8_2026,
+@dataset{outerview_construction-infrastructure-change_2026,
   title={Construction & Infrastructure Change Visual Dataset},
   author={Outerview},
   year={2026},
-  url={https://huggingface.co/datasets/outerview/construction-infrastructure-change-visual-data-8}
+  url={https://huggingface.co/datasets/outerview/construction-infrastructure-change}
 }
 ```