Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust object extraction from remote sensing data   The extraction of object outlines has been a research topic during the last
decades. In spite of advances in photogrammetry, remote sensing and computer
vision, this task remains challenging due to object and data complexity. The
development of object extraction approaches is promoted through publically
available benchmark datasets and evaluation frameworks. Many aspects of
performance evaluation have already been studied. This study collects the best
practices from literature, puts the various aspects in one evaluation
framework, and demonstrates its usefulness to a case study on mapping object
outlines. The evaluation framework includes five dimensions: the robustness to
changes in resolution, input, location, parameters, and application. Examples
for investigating these dimensions are provided, as well as accuracy measures
for their qualitative analysis. The measures consist of time efficiency and a
procedure for line-based accuracy assessment regarding quantitative
completeness and spatial correctness. The delineation approach to which the
evaluation framework is applied, was previously introduced and is substantially
improved in this study.

4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat