Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-level Feature Fusion-based CNN for Local Climate Zone
  Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42
  Dataset   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 environments, especially on a large scale. Remote
sensing data-based classification approaches are the key to large-scale mapping
and monitoring of LCZs. The potential of deep learning-based approaches is not
yet fully explored, even though advanced convolutional neural networks (CNNs)
continue to push the frontiers for various computer vision tasks. One reason is
that published studies are based on different datasets, usually at a regional
scale, which makes it impossible to fairly and consistently compare the
potential of different CNNs for real-world scenarios. This study is based on
the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. Using
this dataset, we studied a range of CNNs of varying sizes. In addition, we
proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this
base network, we propose fusing multi-level features using the extended
Sen2LCZ-Net-MF. With this proposed simple network architecture and the highly
competitive benchmark dataset, we obtain results that are better than those
obtained by the state-of-the-art CNNs, while requiring less computation with
fewer layers and parameters. Large-scale LCZ classification examples of
completely unseen areas are presented, demonstrating the potential of our
proposed Sen2LCZ-Net-MF as well as the So2Sat LCZ42 dataset. We also
intensively investigated the influence of network depth and width and the
effectiveness of the design choices made for Sen2LCZ-Net-MF. Our work will
provide important baselines for future CNN-based algorithm developments for
both LCZ classification and other urban land cover land use classification.

14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math