TITLE: Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study 

ABSTRACT: The development of semi-supervised learning techniques is essential to
enhance the generalization capacities of machine learning algorithms. Indeed,
raw image data are abundant while labels are scarce, therefore it is crucial to
leverage unlabeled inputs to build better models. The availability of large
databases have been key for the development of learning algorithms with high
level performance.
  Despite the major role of machine learning in Earth Observation to derive
products such as land cover maps, datasets in the field are still limited,
either because of modest surface coverage, lack of variety of scenes or
restricted classes to identify. We introduce a novel large-scale dataset for
semi-supervised semantic segmentation in Earth Observation, the MiniFrance
suite. MiniFrance has several unprecedented properties: it is large-scale,
containing over 2000 very high resolution aerial images, accounting for more
than 200 billions samples (pixels); it is varied, covering 16 conurbations in
France, with various climates, different landscapes, and urban as well as
countryside scenes; and it is challenging, considering land use classes with
high-level semantics. Nevertheless, the most distinctive quality of MiniFrance
is being the only dataset in the field especially designed for semi-supervised
learning: it contains labeled and unlabeled images in its training partition,
which reproduces a life-like scenario. Along with this dataset, we present
tools for data representativeness analysis in terms of appearance similarity
and a thorough study of MiniFrance data, demonstrating that it is suitable for
learning and generalizes well in a semi-supervised setting. Finally, we present
semi-supervised deep architectures based on multi-task learning and the first
experiments on MiniFrance.
The development of semi-supervised learning techniques is essential to
enhance the generalization capacities of machine learning algorithms. Indeed,
raw image data are abundant while labels are scarce, therefore it is crucial to
leverage unlabeled inputs to build better models. The availability of large
databases have been key for the development of learning algorithms with high
level performance.
  Despite the major role of machine learning in Earth Observation to derive
products such as land cover maps, datasets in the field are still limited,
either because of modest surface coverage, lack of variety of scenes or
restricted classes to identify. We introduce a novel large-scale dataset for
semi-supervised semantic segmentation in Earth Observation, the MiniFrance
suite. MiniFrance has several unprecedented properties: it is large-scale,
containing over 2000 very high resolution aerial images, accounting for more
than 200 billions samples (pixels); it is varied, covering 16 conurbations in
France, with various climates, different landscapes, and urban as well as
countryside scenes; and it is challenging, considering land use classes with
high-level semantics. Nevertheless, the most distinctive quality of MiniFrance
is being the only dataset in the field especially designed for semi-supervised
learning: it contains labeled and unlabeled images in its training partition,
which reproduces a life-like scenario. Along with this dataset, we present
tools for data representativeness analysis in terms of appearance similarity
and a thorough study of MiniFrance data, demonstrating that it is suitable for
learning and generalizes well in a semi-supervised setting. Finally, we present
semi-supervised deep architectures based on multi-task learning and the first
experiments on MiniFrance.
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study
http://arxiv.org/abs/2010.07830v1