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@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data.csv filter=lfs diff=lfs merge=lfs -text
+data.geojson filter=lfs diff=lfs merge=lfs -text

README.md

@@ -21,7 +21,7 @@ Rows: 800,000
 
 ## Dataset Description
 
-OSM Food & Hospitality Places Visual Dataset is a Global geospatial computer vision dataset built from a balanced OpenStreetMap node sample for food and hospitality places. The labels are the visual feature classes in the section "Features in the dataset", which are Restaurant, Fast Food, and Hotel. Labels were produced through Outerview's internal visual search pipeline using query-driven embedding match against real-world observations linked to OpenStreetMap place records. The packaged files include `data.csv`, `data.geojson`, and `data.parquet`, which contain the same tabular records in different formats for image classification, visual search, and location-aware analysis. The dataset is useful for geospatial computer vision, street-level imagery review, wildlife image dataset style benchmarking of label pipelines, and site-level spatial classification across cities and countries.
+OSM Food & Hospitality Places Visual Dataset is a Global geospatial computer vision dataset of 800,000 OpenStreetMap node records for food and hospitality places. The labeled classes are Restaurant, Fast Food, and Hotel, produced by Outerview internal visual search pipeline matching query-driven visual embeddings to OpenStreetMap place observations. The package includes `data.csv`, `data.geojson`, and `data.parquet`, which contain the exported records and geospatial fields for visual review, retrieval, and image classification workflows. It is useful for map-based visual search, place-type classification, location intelligence, and training models on real-world street-level imagery and point-of-interest metadata.
 
 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).
 
@@ -33,9 +33,9 @@ Updated on a bimonthly basis.
 
 ### Features in the dataset
 
-- Restaurant: visual records matched to restaurant places and related food-service locations.
-- Fast Food: visual records matched to fast food venues and quick-service dining locations.
-- Hotel: visual records matched to hotel and lodging properties.
+- Restaurant: visual records matched to restaurant-related food service places.
+- Fast Food: visual records matched to fast food outlets and quick-service restaurants.
+- Hotel: visual records matched to hotel and lodging places.
 
 ### Schema
 
@@ -63,17 +63,17 @@ Updated on a bimonthly basis.
 
 ### Source
 
-Records come from Outerview's full visual index of real-world observations, matched to OpenStreetMap place nodes through query-driven embedding search. The source is curated as a balanced sample for food and hospitality places across Region: Global.
+The records come from Outerview's visual index and are selected through query-driven embedding matching against OpenStreetMap place observations. The source is organized for global place search and geospatial computer vision use cases, with Region: Global.
 
 ### Annotation
 
-Feature labels were assigned only for the exact classes Restaurant, Fast Food, and Hotel. The annotations reflect the matched visual feature label and do not include bounding boxes or object localization.
+Labels are assigned at the feature level only using the exact feature set Restaurant, Fast Food, and Hotel. No bounding boxes or object localization labels are included, and the matched class reflects the visual feature label attached to each record.
 
 ## Uses
 
 ### Intended Uses
 
-Municipal asset teams can use this dataset for place-level mapping and inventory workflows. AV perception engineers can use it for geospatial computer vision and image classification benchmarks. Insurance underwriters, environmental analysts, and retail site selection teams can use it for spatial lookup, market coverage checks, and visual search.
+This dataset is suitable for municipal asset teams, AV perception engineers, insurance underwriters, environmental analysts, and retail site selection teams. It can support geospatial image classification, POI categorization, visual search, and quality checks on food and hospitality place data.
 
 ### Out-of-Scope Uses
 

data.csv

@@ -1 +1,3 @@
-record_id,osm_id,feature,osm_tag,name,latitude,longitude,country,state,city,address,website,brand,opening_hours,source,last_seen_at,properties
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+size 430313254

data.geojson

@@ -1,3 +1,3 @@
-{"type":"FeatureCollection","features":[
-
-]}
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data.parquet

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 version https://git-lfs.github.com/spec/v1
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-size 2639
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