Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data

Oehmcke, Stefan; Thrysøe, Christoph; Borgstad, Andreas: Salles, Marcos Vaz; Brandt, Martin; Gieseke, Fabian


Zusammenfassung

Massive amounts of satellite data have been gathered over time, holding the potential to unveil a spatiotemporal chronicle of the surface of Earth. These data allow scientists to investigate various important issues, such as land use changes, on a global scale. However, not all land-use phenomena are equally visible on satellite imagery. In particular, the creation of an inventory of the planet's road infrastructure remains a challenge, despite being crucial to analyze urbanization patterns and their impact. Towards this end, this work advances data-driven approaches for the automatic identification of roads based on open satellite data. Given the typical resolutions of these historical satellite data, we observe that there is inherent variation in the visibility of different road types. Based on this observation, we propose two deep learning frameworks that extend state-of-the-art deep learning methods by formalizing road detection as an ordinal classification task. In contrast to related schemes, one of the two models also resorts to satellite time series data that are potentially affected by missing data and cloud occlusion. Taking these time series data into account eliminates the need to manually curate datasets of high-quality image tiles, substantially simplifying the application of such models on a global scale. We evaluate our approaches on a dataset that is based on Sentinel 2 satellite imagery and OpenStreetMap vector data. Our results indicate that the proposed models can successfully identify large and medium-sized roads. We also discuss opportunities and challenges related to the detection of roads and other infrastructure on a global scale.

Schlüsselwörter
deep learning; remote sensing; road detection



Publikationstyp
Forschungsartikel in Sammelband (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2019

Konferenz
IEEE Big Data, Intelligent Data Mining Special Session

Konferenzort
Los Angeles

Buchtitel
2019 {IEEE} International Conference on Big Data {(IEEE} BigData)

Herausgeber
Baru, Chaitanya K.; Huan, Jun; Khan, Latifur; Hu, Xiaohua; Ak, Ronay; Tian, Yuanyuan; Barga, Roger S.; Zaniolo, Carlo; Lee, Kisung; Ye, Yanfang (Fanny)

Erste Seite
2403

Letzte Seite
2412

Verlag
IEEE

Ort
Los Angeles, USA

Sprache
Englisch

DOI