Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting Urban Changes with Recurrent Neural Networks from
  Multitemporal Sentinel-2 Data   \begin{abstract} The advent of multitemporal high resolution data, like the
Copernicus Sentinel-2, has enhanced significantly the potential of monitoring
the earth's surface and environmental dynamics. In this paper, we present a
novel deep learning framework for urban change detection which combines
state-of-the-art fully convolutional networks (similar to U-Net) for feature
representation and powerful recurrent networks (such as LSTMs) for temporal
modeling. We report our results on the recently publicly available bi-temporal
Onera Satellite Change Detection (OSCD) Sentinel-2 dataset, enhancing the
temporal information with additional images of the same region on different
dates. Moreover, we evaluate the performance of the recurrent networks as well
as the use of the additional dates on the unseen test-set using an ensemble
cross-validation strategy. All the developed models during the validation phase
have scored an overall accuracy of more than 95%, while the use of LSTMs and
further temporal information, boost the F1 rate of the change class by an
additional 1.5%.

15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio