Every single street? Rethinking full coverage across street‐level imagery platforms

Abstract

Abstract Street‐level images taken by vehicles and pedestrians have found a role in various companies’ location‐based intelligence services. Some platforms collect their images using their own cars and drivers, while others rely on crowdsourcing; however, to what extent can we expect crowdsourced approaches to reach the imagery coverage levels obtained by paid drivers? Is capturing every single street a useful or obtainable goal? We use online coverage maps to compare Google Street View, Mapillary, and OpenStreetCam in 24 major world cities and 25 differently sized cities in Brazil. We find that Google has often taken an all‐or‐nothing approach to collecting coverage in world cities, whereas crowdsourced platforms have achieved a more even distribution of coverage across space. Extremely low‐ and high‐income neighborhoods are sometimes omitted due to visible and invisible barriers. Coverage patterns are influenced by how and why each company procures imagery, along with other social, economic, and geographic factors.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 27 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.