Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data
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Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data. / Oehmcke, Stefan; Thrysøe, Christoffer; Borgstad, Andreas ; Vaz Salles, Marcos Antonio; Brandt, Martin Stefan; Gieseke, Fabian Cristian.
Proceedings of the IEEE International Conference on Big Data, Big Data 2019: Special Session on Intelligent Data Mining. IEEE, 2019. s. 2403-2412 9006251,.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data
AU - Oehmcke, Stefan
AU - Thrysøe, Christoffer
AU - Borgstad, Andreas
AU - Vaz Salles, Marcos Antonio
AU - Brandt, Martin Stefan
AU - Gieseke, Fabian Cristian
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
M3 - Article in proceedings
SP - 2403
EP - 2412
BT - Proceedings of the IEEE International Conference on Big Data, Big Data 2019
PB - IEEE
T2 - 2019 IEEE International Conference on Big Data
Y2 - 9 December 2019 through 12 December 2019
ER -
ID: 231760061