Enabling Country-Scale Land Cover Mapping with Meter-Resolution
Satellite Imagery
- URL: http://arxiv.org/abs/2209.00727v2
- Date: Fri, 7 Apr 2023 13:22:01 GMT
- Title: Enabling Country-Scale Land Cover Mapping with Meter-Resolution
Satellite Imagery
- Authors: Xin-Yi Tong, Gui-Song Xia, Xiao Xiang Zhu
- Abstract summary: High-resolution satellite images can provide abundant, detailed spatial information for land cover classification.
Few studies have applied high-resolution images to land cover mapping in detailed categories at large scale.
We present a large-scale land cover dataset, Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images.
- Score: 42.70832378336697
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High-resolution satellite images can provide abundant, detailed spatial
information for land cover classification, which is particularly important for
studying the complicated built environment. However, due to the complex land
cover patterns, the costly training sample collections, and the severe
distribution shifts of satellite imageries, few studies have applied
high-resolution images to land cover mapping in detailed categories at large
scale. To fill this gap, we present a large-scale land cover dataset,
Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150
high-resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category
system covering artificial-constructed, agricultural, and natural classes. In
addition, we propose a deep-learning-based unsupervised domain adaptation
approach that can transfer classification models trained on labeled dataset
(referred to as the source domain) to unlabeled data (referred to as the target
domain) for large-scale land cover mapping. Specifically, we introduce an
end-to-end Siamese network employing dynamic pseudo-label assignment and class
balancing strategy to perform adaptive domain joint learning. To validate the
generalizability of our dataset and the proposed approach across different
sensors and different geographical regions, we carry out land cover mapping on
five megacities in China and six cities in other five Asian countries severally
using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite
images. Over a total study area of 60,000 square kilometers, the experiments
show promising results even though the input images are entirely unlabeled. The
proposed approach, trained with the Five-Billion-Pixels dataset, enables
high-quality and detailed land cover mapping across the whole country of China
and some other Asian countries at meter-resolution.
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