288 research outputs found
Benchmarking Deep Learning Architectures for Urban Vegetation Points Segmentation
Vegetation is crucial for sustainable and resilient cities providing various
ecosystem services and well-being of humans. However, vegetation is under
critical stress with rapid urbanization and expanding infrastructure
footprints. Consequently, mapping of this vegetation is essential in the urban
environment. Recently, deep learning for point cloud semantic segmentation has
shown significant progress. Advanced models attempt to obtain state-of-the-art
performance on benchmark datasets, comprising multiple classes and representing
real world scenarios. However, class specific segmentation with respect to
vegetation points has not been explored. Therefore, selection of a deep
learning model for vegetation points segmentation is ambiguous. To address this
problem, we provide a comprehensive assessment of point-based deep learning
models for semantic segmentation of vegetation class. We have selected four
representative point-based models, namely PointCNN, KPConv (omni-supervised),
RandLANet and SCFNet. These models are investigated on three different
datasets, specifically Chandigarh, Toronto3D and Kerala, which are
characterized by diverse nature of vegetation, varying scene complexity and
changing per-point features. PointCNN achieves the highest mIoU on the
Chandigarh (93.32%) and Kerala datasets (85.68%) while KPConv (omni-supervised)
provides the highest mIoU on the Toronto3D dataset (91.26%). The paper develops
a deeper insight, hitherto not reported, into the working of these models for
vegetation segmentation and outlines the ingredients that should be included in
a model specifically for vegetation segmentation. This paper is a step towards
the development of a novel architecture for vegetation points segmentation.Comment: This work has been submitted to the IEEE for possible publication.
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Deep learning methods applied to digital elevation models: state of the art
Deep Learning (DL) has a wide variety of applications in various
thematic domains, including spatial information. Although with
limitations, it is also starting to be considered in operations
related to Digital Elevation Models (DEMs). This study aims to
review the methods of DL applied in the field of altimetric spatial
information in general, and DEMs in particular. Void Filling (VF),
Super-Resolution (SR), landform classification and hydrography
extraction are just some of the operations where traditional methods
are being replaced by DL methods. Our review concludes
that although these methods have great potential, there are
aspects that need to be improved. More appropriate terrain information
or algorithm parameterisation are some of the challenges
that this methodology still needs to face.Functional Quality of Digital Elevation Models in Engineering’ of the State Agency Research of SpainPID2019-106195RB- I00/AEI/10.13039/50110001103
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