DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic
Change Segmentation
- URL: http://arxiv.org/abs/2203.12560v1
- Date: Wed, 23 Mar 2022 17:22:22 GMT
- Title: DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic
Change Segmentation
- Authors: Aysim Toker, Lukas Kondmann, Mark Weber, Marvin Eisenberger, Andr\'es
Camero, Jingliang Hu, Ariadna Pregel Hoderlein, \c{C}a\u{g}lar \c{S}enaras,
Timothy Davis, Daniel Cremers, Giovanni Marchisio, Xiao Xiang Zhu, Laura
Leal-Taix\'e
- Abstract summary: We propose the DynamicEarthNet dataset that consists of daily, multi-spectral satellite observations of 75 selected areas of interest.
These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover classes.
- Score: 43.72597365517224
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Earth observation is a fundamental tool for monitoring the evolution of land
use in specific areas of interest. Observing and precisely defining change, in
this context, requires both time-series data and pixel-wise segmentations. To
that end, we propose the DynamicEarthNet dataset that consists of daily,
multi-spectral satellite observations of 75 selected areas of interest
distributed over the globe with imagery from Planet Labs. These observations
are paired with pixel-wise monthly semantic segmentation labels of 7 land use
and land cover (LULC) classes. DynamicEarthNet is the first dataset that
provides this unique combination of daily measurements and high-quality labels.
In our experiments, we compare several established baselines that either
utilize the daily observations as additional training data (semi-supervised
learning) or multiple observations at once (spatio-temporal learning) as a
point of reference for future research. Finally, we propose a new evaluation
metric SCS that addresses the specific challenges associated with time-series
semantic change segmentation. The data is available at:
https://mediatum.ub.tum.de/1650201.
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