Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps

Sen, Alper; Suleymanoglu, Baris; Soycan, Metin

doi:10.1080/00396265.2018.1532704

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…Dominant application fields include land-cover/land-use (urban areas, farmlands, roads, water bodies), biogeosciences, and military applications, and there especially change detection of airplanes present/absent at terminals (see, for example, [37,59,89,[93][94][95][96]). Neural nets and other ML methods are increasingly finding applications in the geosciences.…”

Section: Recent Applications Of Nns In Geosciencesmentioning

confidence: 99%

Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

Herzfeld,

Hessburg,

Trantow

et al. 2024

Remote Sensing

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The objectives of this paper are to investigate the trade-offs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach (“VarioCNN”). Our solution is provided in the framework of a cyberinfrastructure that includes a +newly designed ML software, GEOCLASS-image (v1.0), modern high-resolution satellite image data sets (Maxar WorldView data), and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100–200 times its normal velocity. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in six simplified classes.

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Section: Recent Applications Of Nns In Geosciencesmentioning

confidence: 99%

Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

Herzfeld,

Hessburg,

Trantow

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Dominant application fields include land-cover/land-use (urban areas, farmlands, roads, water bodies), biogeosciences, and military applications, there especially change detection of airplanes present/absent at terminals (see, for example, [37,60,90,[94][95][96][97]). Neural Nets and other ML methods are increasingly finding applications in the geosciences.…”

Section: Recent Applications Of Nns In Geosciencesmentioning

confidence: 99%

Combining "Deep Learning" and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges

Herzfeld,

Hessburg,

Trantow

et al. 2024

Preprint

View full text Add to dashboard Cite

The objectives of this paper are to investigate the tradeoffs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach ("VarioCNN"). Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed ML software, GEOCLASS-image, modern high-resolution satellite image data sets (Maxar WorldView data) and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100-200 times its normal velocity. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in six simplified classes.

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“…Hence, studies have turned to work directly on classifying discrete LiDAR point clouds based on height and intensity features using ML [46], and AdaBoost [47]. Different experiments applied point-based clustering using Weighted Self-Organizing Maps [48], and object-based classification of voxelized LiDAR data using the PointNet++ deep learning technique [49]. Other researchers generated eigenvalues, geometric, and spatial descriptors from LiDAR's height features.…”

Section: Introductionmentioning

confidence: 99%

“…Sen et al [48] carried out an unsupervised classification on airborne Li-DAR point clouds acquired for a residential urban area using the weighted self-organizing maps clustering technique. They applied Pearson's chi-squared independence test to weigh the normalized data attributes, 3D coordinates, and a single intensity.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Lidar-imagery Data Fusion and Multilevel Classification of Lidar Point Clouds for Urban Features Extraction

Abumosallam

2024

Preprint

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The world is witnessing massive demographic residents transitions from rural to urban, resulting in alarming consequences. These shifts highlight the necessity of comprehensive urban land-use mapping for current assessment and future city planning and management. The integration of Light Detection and Ranging (LiDAR) and imagery data allows using their complementary geometric and radiometric characteristics, respectively, with a potential increase in urban mapping accuracies. However, airborne LiDAR data may not always come with onboard optical images collected during the same ﬂight mission. Indirect geo-referencing can be adopted if ancillary imagery data are available. Nevertheless, automatic recognition of control primitives in LiDAR and imagery datasets becomes challenging when collected on diﬀerent dates. This dissertation proposes a generic geo-registration mechanism using the Phase Congruency (PC) model and scene abstraction to overcome the stated challenges. The Root Mean Square Error (RMSE) is between one to two pixels. The proposed workﬂow is computationally eﬃcient, especially with small datasets, and generic enough to apply to various urban morphologies. A point-based classiﬁcation was then performed on a point cloud covering a residential area using machine learning. The data was geo-registered to an aerial image. The classiﬁcation tried multiple combinations of the inherited image spectra and the height as the only geometric feature in the classifying spaces. The acquired radiometric features added to the height gave an overall accuracy above 97% using Multi-Layer Perceptron (MLP) neural networks, surpassing those achieved with height-based geometric-oriented feature spaces. However, misclassifications occurred among different classes due to the independent acquisition of the aerial and LiDAR data and shadow and orthorectification problems from the aerial images. The dissertation then moves on to modify the color-based segmentation algorithm for the object-based classification of the multispectral LiDAR version of the same point cloud. The dissertation proposes a multilevel classification through vectors of inherited and calculated height-based geometric and spectral features structured in different classification scenarios (combinations). An overall mapping accuracy higher than 98% was achieved using MLP neural networks, combining the entire feature vectors and their optimized output space projected by the Principal Component Analysis (PCA). This combination also eliminates some of the abovementioned misclassifications.

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Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps

Cited by 9 publications

References 23 publications

Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

Combining "Deep Learning" and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges

Multispectral Lidar-imagery Data Fusion and Multilevel Classification of Lidar Point Clouds for Urban Features Extraction

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