Why Not a Single Image? Combining Visualizations to Facilitate Fieldwork and On-Screen Mapping

Kokalj, Žiga; Somrak, Maja

doi:10.3390/rs11070747

Cited by 159 publications

(130 citation statements)

References 56 publications

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“…Although shaded relief map is a widely accepted technique for visually showing DTMs, it has two major drawbacks: Identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. In comparison, sky-view factor can be used as a general relief visualization technique to show relief characteristics [44,45]. Thus, the sky-view factors of OK and the proposed method on s -52 are demonstrated in Figure 8.…”

Section: Public Datasetmentioning

confidence: 99%

A Fast Global Interpolation Method for Digital Terrain Model Generation from Large LiDAR-Derived Data

Chen

2019

Remote Sensing

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Airborne light detection and ranging (LiDAR) datasets with a large volume pose a great challenge to the traditional interpolation methods for the production of digital terrain models (DTMs). Thus, a fast, global interpolation method based on thin plate spline (TPS) is proposed in this paper. In the methodology, a weighted version of finite difference TPS is first developed to deal with the problem of missing data in the grid-based surface construction. Then, the interpolation matrix of the weighted TPS is deduced and found to be largely sparse. Furthermore, the values and positions of each nonzero element in the matrix are analytically determined. Finally, to make full use of the sparseness of the interpolation matrix, the linear system is solved with an iterative manner. These make the new method not only fast, but also require less random-access memory. Tests on six simulated datasets indicate that compared to recently developed discrete cosine transformation (DCT)-based TPS, the proposed method has a higher speed and accuracy, lower memory requirement, and less sensitivity to the smoothing parameter. Real-world examples on 10 public and 1 private dataset demonstrate that compared to the DCT-based TPS and the locally weighted interpolation methods, such as linear, natural neighbor (NN), inverse distance weighting (IDW), and ordinary kriging (OK), the proposed method produces visually good surfaces, which overcome the problems of peak-cutting, coarseness, and discontinuity of the aforementioned interpolators. More importantly, the proposed method has a similar performance to the simple interpolation methods (e.g., IDW and NN) with respect to computing time and memory cost, and significantly outperforms OK. Overall, the proposed method with low memory requirement and computing cost offers great potential for the derivation of DTMs from large-scale LiDAR datasets.

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Section: Public Datasetmentioning

confidence: 99%

A Fast Global Interpolation Method for Digital Terrain Model Generation from Large LiDAR-Derived Data

Chen

2019

Remote Sensing

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“…The cross-validation results from the different types of LiDAR visualizations indicated that the model performed better when trained on the raw 8-bit DSM height values rather than any of the advanced visualizations. It is suspected that whilst these visualizations are effective for human interpretation of archaeological data [11] and also effective for more traditional machine learning techniques [22], because deep CNNs learn their own feature representations during training, it is not desirable to artificially alter the data representation prior to input. However, it also must be taken into account that the CNN chosen in this study was pretrained on 8-bit DSM height values, thereby introducing a bias towards this representation.…”

Section: Discussionmentioning

confidence: 99%

“…Humans are inherently unable to process height data in its native state; therefore, it must be processed to create visualizations that are interpretable by the human eye. This can lead to a loss of information, image artefacts [10] or a bias stemming from the visualization techniques used [11]. As a computer can process the single channel numeric gridded height data directly, this removes some of these issues.…”

Section: Introductionmentioning

confidence: 99%

Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning

et al. 2019

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This article presents a novel deep learning method for semi-automated detection of historic mining pits using aerial LiDAR data. The recent emergence of national scale remotely sensed datasets has created the potential to greatly increase the rate of analysis and recording of cultural heritage sites. However, the time and resources required to process these datasets in traditional desktop surveys presents a near insurmountable challenge. The use of artificial intelligence to carry out preliminary processing of vast areas could enable experts to prioritize their prospection focus; however, success so far has been hindered by the lack of large training datasets in this field. This study develops an innovative transfer learning approach, utilizing a deep convolutional neural network initially trained on Lunar LiDAR datasets and reapplied here in an archaeological context. Recall rates of 80% and 83% were obtained on the 0.5 m and 0.25 m resolution datasets respectively, with false positive rates maintained below 20%. These results are state of the art and demonstrate that this model is an efficient, effective tool for semi-automated object detection for this type of archaeological objects. Further tests indicated strong potential for detection of other types of archaeological objects when trained accordingly.

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“…The image patches were then rescaled individually to the 0-1 range before being remapped to 8-bit integer format. For human interpretation, different LiDAR data visualisations have been shown to greatly enhance interpretation (Kokalj and Somrak, 2019). Following the workflows described in Kokalj and Hesse (2017) the additional data representations of Simplified Local Relief Models (SLRM) and the measures of positive and negative relief openness, calculated as the angular size of a sphere looking either up or down at each pixel location (Doneus, 2013) were generated from the exported tiles using the Relief Visualisation Toolbox (Kokalj and Somrak, 2019).…”

Section: Methodsmentioning

confidence: 99%

Using Deep Learning and Hough Transformations to Infer Mineralised Veins From Lidar Data Over Historic Mining Areas

Gallwey

Yeomans

Tonkins

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper presents a novel technique to improve geological understanding in regions of historic mining activity. This is achieved through inferring the orientations of geological structures from the imprints left on the landscape by past mining activities. Open source high resolution LiDAR datasets are used to fine-tune a deep convolutional neural network designed initially for Lunar LiDAR crater identification. By using a transfer learning approach between these two very similar domains, high accuracy predictions of pit locations can be generated in the form of a raster mask of pit location probabilities. Taking the raster of the predicted pit location centres as an input, a Hough transformation is used to fit lines through the centres of the detected pits. The results demonstrate that these lines follow the patterns of known mineralised veins in the area, alongside highlighting veins which are below the scale of the published geological maps.

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Why Not a Single Image? Combining Visualizations to Facilitate Fieldwork and On-Screen Mapping

Cited by 159 publications

References 56 publications

A Fast Global Interpolation Method for Digital Terrain Model Generation from Large LiDAR-Derived Data

A Fast Global Interpolation Method for Digital Terrain Model Generation from Large LiDAR-Derived Data

Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning

Using Deep Learning and Hough Transformations to Infer Mineralised Veins From Lidar Data Over Historic Mining Areas

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