Riverine Land Cover Mapping through Semantic Segmentation of Multispectral Point Clouds
Authors: Sopitta Thurachen, Josef Taher, Matti Lehtomäki, Leena Matikainen, Linnea Blåfield, Mikel Calle Navarro, Antero Kukko, Tomi Westerlund, Harri Kaartinen
Organizations: Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, Vuorimiehentie 5, Espoo, 02150, Finland · Faculty of Technology, University of Turku, Turku, 20014, Finland · Department of Geography and Geology, University of Turku, Turku, 20014, Finland · Faculty of Geological Sciences, Complutense University of Madrid, Avda. de Séneca, 2 Ciudad Universitaria, Madrid, 28040, Spain
Accurate land cover mapping in riverine environments is essential for effective river management, ecological understanding, and geomorphic change monitoring. This study explores the use of Point Transformer v2 (PTv2), an advanced deep neural network architecture designed for point cloud data, for land cover mapping through semantic segmentation of multispectral LiDAR data in real-world riverine environments. We utilize the geometric and spectral information from the 3-channel LiDAR point cloud to map land cover classes, including sand, gravel, low vegetation, high vegetation, forest floor, and water. The PTv2 model was trained and evaluated on point cloud data from the Oulanka river in northern Finland using both geometry and spectral features. To improve the model's generalization in new riverine environments, we additionally investigate multi-dataset training that adds sparsely annotated data from an additional river dataset. Results demonstrated that using the full-feature configuration resulted in performance with a mean Intersection over Union (mIoU) of 0.950, significantly outperforming the geometry baseline. Other ablation studies revealed that intensity and reflectance features were the key for accurate land cover mapping. The multi-dataset training experiment showed improved generalization performance, suggesting potential for developing more robust models despite limited high-quality annotated data. Our work demonstrates the potential of applying transformer-based architectures to multispectral point clouds in riverine environments. The approach offers new capabilities for monitoring sediment transport and other river management applications.
Figures & tables
Figure 1: Oulanka river in the northeastern part of Finland
Figure 2: A map of Finland highlighting the three study areas: NS, HN, and JM.
Figure 3: HeliALS-TW system
Scanner
1
2
3
Model
VUX-1HA
miniVUX-3UAV
VQ-840-G
Wavelength (nm)
1,550
905
532
Approx. point density (points/m 2 )
1,400
500
1,600
Number of returns (max.)
12
5
5
Maximum scanning angle ( ∘ )
360
120
28 × 40
Laser beam divergence (mrad)
0.5
0.5 × 1.6
1
Table 1: The specifications of the laser scanners in the multispectral laser scanning system HeliALS for Oulanka river
Point Attribute
Description
3D coordinates
X, Y, Z positional data from LiDAR scanning
Intensity
Strength of the return signal
Reflectance
Surface reflectivity
Amplitude
Peak amplitude of the return waveform
Deviation
The deviation of the return echo waveform from the system impulse response
Return number
Sequential identifier of each return from one laser pulse
Table 2: Point cloud attributes
Class
Description
Sand
Loose, granular surface material
Gravel
Coarse rock fragments
High vegetation
Tall, dense vegetation such as trees
Low vegetation
Shorter vegetation, grasses, or ground cover
Forest floor
Moss or other low vegetation under high vegetation
Water
Rivers and water bodies
Table 3: Land cover classes and their characteristics.
Figure 4: Example images of the six land cover classes used in the dataset (a) sand, (b) gravel, (c) high vegetation, (d) low vegetation, (e) forest floor, (f) water
Table 4: Class distribution (%) of annotated data in the three study areas
Table 5: Details of the three study areas
Figure 5: Oulanka point cloud data colored by 3-channel intensities as RGB and the corresponding orthophotos
Augmentation
Range
Description
Scaling
0.8–1.2
Random uniform resizing of the point cloud
Translation
± 0.2 units
Shifts all points along x, y, and z axes
Table 6: Data augmentation techniques applied during training.
•3D coordinates • 3-channel intensity, reflectance, amplitude, deviation (all from VUX, miniVUX, VQ) • Return number, number of returns
Table 7: Feature configurations for different channel sets in the multispectral point cloud ablation study
Channel set
mIoU
Sand
Gravel
High veg.
Low veg.
Forest floor
Water
XYZ
0.643
0.487
0.194
0.874
0.685
0.765
0.852
Channel 1
0.865
0.849
0.908
0.905
0.773
0.763
0.993
Channels 1–2
0.937
0.988
0.991
0.903
0.934
0.813
0.993
Channels 1–3
0.938
0.993
0.971
0.910
0.938
0.818
0.999
All features
0.950
0.990
0.971
0.931
0.930
0.878
0.998
Table 8: Ablation study results showing IoU for each class and mIoU across all classes on the JM test dataset
Channel set
mAcc
Sand
Gravel
High veg.
Low veg.
Forest floor
Water
XYZ
0.751
0.733
0.258
0.893
0.818
0.857
0.949
Channel 1
0.932
0.849
0.984
0.924
0.912
0.924
0.998
Channels 1–2
0.970
0.988
≈1.000
0.914
0.972
0.950
0.999
Channels 1–3
0.971
0.994
≈1.000
0.919
0.968
0.948
≈1.000
All features
0.977
0.990
0.998
0.938
0.970
0.966
0.999
Table 9: Ablation study results showing Acc for each class and mAcc across all classes on the JM test dataset
Figure 6: Confusion matrix for using all spectral features together with pulse information (row-wise normalization). Values shown are rounded to three decimal places.
Channel set
mPrecision
Sand
Gravel
High veg.
Low veg.
Forest floor
Water
XYZ
0.764
0.592
0.438
0.976
0.808
0.877
0.892
Channel 1
0.924
≈1.000
0.922
0.978
0.835
0.814
0.995
Channels 1–2
0.964
≈1.000
0.992
0.986
0.960
0.850
0.994
Channels 1–3
0.963
≈1.000
0.971
0.985
0.969
0.857
0.999
All features
0.971
≈1.000
0.973
0.992
0.958
0.906
0.999
Table 10: Ablation study results showing Precision for each class and mean Precision across all classes on the JM test dataset
Feature
mIoU
Sand
Gravel
High veg.
Low veg.
Forest floor
Water
Intensity
0.937
0.995
0.980
0.909
0.940
0.802
0.999
Reflectance
0.934
0.993
0.978
0.894
0.930
0.813
0.998
Amplitude
0.767
0.605
0.400
0.908
0.874
0.817
0.999
Deviation
0.719
0.606
0.222
0.883
0.877
0.727
0.997
Table 11: Ablation study results showing IoU and mIoU for each spectral feature. Each model variant is trained using only one input feature at a time with geometry to evaluate the individual contribution of each feature to semantic segmentation performance.
Training data
mIoU
Sand
Gravel
High veg.
Low veg.
Forest floor
Water
NS
0.773
0.857
0.836
0.895
0.571
0.486
0.994
NS + HN
0.950
0.990
0.971
0.931
0.930
0.878
0.998
Table 12: Per-class IoU and mIOU performance comparison comparing models trained on the NS dataset alone and combined NS+HN dataset
Model
mIoU
Sand
Gravel
High veg
Low veg
Forest floor
Water
PTv2
0.950
0.990
0.971
0.931
0.930
0.878
0.998
DGCNN
0.744
0.803
0.331
0.895
0.682
0.787
0.967
RandLA-Net
0.580
0.597
0.384
0.897
0.07
0.544
0.989
RF (500 trees)
0.682
0.718
0.822
0.671
0.577
0.340
0.967
Table 13: Per-class IoU and mIoU comparing PTv2 with RF, DGCNN and RandLA-Net.
Figure 7: Orthophoto (left), prediction using only geometry (middle), and prediction using full feature set (right)
Figure 8: Sediment prediction results: Model prediction highlighting sediment mapping (left) and corresponding orthophoto of the study area (right)
Figure 9: Outer bank mapping: prediction (left) and corresponding orthophoto (right)
Figure 10: Isolated vegetation patch mapping on the upper point bar:prediction (left) and corresponding orthophoto (right)
Figure 11: Forest floor mapping on the upper point bar : prediction (left) and corresponding orthophoto (right)
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Trento, Italy · Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria · Helmholtz-ZentrumDresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology (HIF), Freiberg, Germany +2
Department of Statistics, Computer Science and Mathematics and Institute of Smart Cities (ISC), Public University of Navarre (UPNA), Campus de Arrosadía s/n, Pamplona, 31006, Navarre, Spain · Tracasa Instrumental, Cabárceno, 6, Sarriguren, 31621, Navarre, Spain
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China · Department of Telecommunications and Information Processing, UGent-GAIM, Ghent University, 9000 Ghent, Belgium