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20182025
most citedSEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal

170 citations · 363 across the 14 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.CV2020

Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data

Gerald Baier, Antonin Deschemps, Michael Schmitt +1

We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images from land cover maps and auxiliary raster data using generative adversarial networks (GANs).…

eess.IV2020★ 109 cited

Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution

Tzu-Hsin Karen Chen, Chunping Qiu, Michael Schmitt +3

Landsat imagery is an unparalleled freely available data source that allows reconstructing horizontal and vertical urban form. This paper addresses the challenge of using Landsat d…

eess.IV2020★ 1 cited

Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset

Chunping Qiu, Xiaochong Tong, Michael Schmitt +2

As a unique classification scheme for urban forms and functions, the local climate zone (LCZ) system provides essential general information for any studies related to urban environ…

cs.CV2020

Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities

Michael Schmitt, Jonathan Prexl, Patrick Ebel +2

Fully automatic large-scale land cover mapping belongs to the core challenges addressed by the remote sensing community. Usually, the basis of this task is formed by (supervised) m…

cs.CV2020

A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks

C. Qiu, M. Schmitt, C. Geiss +2

Human settlement extent (HSE) information is a valuable indicator of world-wide urbanization as well as the resulting human pressure on the natural environment. Therefore, mapping…