Persistent homology on LiDAR data to detect landslides

Syzdykbayev, Meirman; Karimi, Bobak; Karimi, Hassan A.

doi:10.1016/j.rse.2020.111816

Cited by 21 publications

(12 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The threshold value is sensitive to the smoothing filters because smoothing decreases noise in the DTM that affect the derivative values of the DTM [20]. For complex real-world data, an optimal threshold value is usually determined through a manual process where the value is iteratively selected and compared either visually or by checking against a known dataset; this process was used in [22,56,65], and showed that an optimal threshold value is achieved by two times the curvature's standard deviation.…”

Section: Discussionmentioning

confidence: 99%

A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

Syzdykbayev

Karimi

2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Detection of terrain features (ridges, spurs, cliffs, and peaks) is a basic research topic in digital elevation model (DEM) analysis and is essential for learning about factors that influence terrain surfaces, such as geologic structures and geomorphologic processes. Detection of terrain features based on general geomorphometry is challenging and has a high degree of uncertainty, mostly due to a variety of controlling factors on surface evolution in different regions. Currently, there are different computational techniques for obtaining detailed information about terrain features using DEM analysis. One of the most common techniques is numerically identifying or classifying terrain elements where regional topologies of the land surface are constructed by using DEMs or by combining derivatives of DEM. The main drawbacks of these techniques are that they cannot differentiate between ridges, spurs, and cliffs, or result in a high degree of false positives when detecting spur lines. In this paper, we propose a new method for automatically detecting terrain features such as ridges, spurs, cliffs, and peaks, using shaded relief by controlling altitude and azimuth of illumination sources on both smooth and rough surfaces. In our proposed method, we use edge detection filters based on azimuth angle on shaded relief to identify specific terrain features. Results show that the proposed method performs similar to or in some cases better (when detecting spurs than current terrain features detection methods, such as geomorphon, curvature, and probabilistic methods.

show abstract

Section: Discussionmentioning

confidence: 99%

A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

Syzdykbayev

Karimi

2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, t-LIDAR scanning techniques are commonly used to detect and describe landslide phenomena [19,[40][41][42][43]. Airborne LIDAR deployed by several researchers has also offered a unique opportunity to describe and study landslides on a larger scale [44,45].…”

Section: Early Landslide Phenomena and Detection Techniques-uav Photo...mentioning

confidence: 99%

Detecting and Monitoring Early Post-Fire Sliding Phenomena Using UAV–SfM Photogrammetry and t-LiDAR-Derived Point Clouds

Deligiannakis

Pallikarakis

Papanikolaou

et al. 2021

Fire

View full text Add to dashboard Cite

Soil changes, including landslides and erosion, are some of the most prominent post-fire effects in Mediterranean ecosystems. Landslide detection and monitoring play an essential role in mitigation measures. We tested two different methodologies in five burned sites with different characteristics in Central Greece. We compared Unmanned Aerial Vehicles (UAV)-derived high-resolution Digital Surface Models and point clouds with terrestrial Light Detection and Ranging (LiDAR)-derived point clouds to reveal new cracks and monitor scarps of pre-existing landslides. New cracks and scarps were revealed at two sites after the wildfire, measuring up to 27 m in length and up to 25 ± 5 cm in depth. Pre-existing scarps in both Kechries sites appeared to be active, with additional vertical displacements ranging from 5–15 ± 5 cm. In addition, the pre-existing landslide in Magoula expanded by 8%. Due to vegetation regrowth, no changes could be detected in the Agios Stefanos pre-existing landslide. This high-spatial-resolution mapping of slope deformations can be used as landslide precursor, assisting prevention measures. Considering the lack of vegetation after wildfires, UAV photogrammetry has great potential for tracing such early landslide indicators and is more efficient for accurately recording soil changes.

show abstract

“…buildings, cars, trees) are distinguishable. Examples of applications in the environmental domain include tree species mapping and classification (Hamraz et al, 2019;Cao et al, 2019;Mäyrä et al, 2021), mangrove mapping (Li et al, 2021), crop monitoring (Lin and Habib, 2021), fire damage assessment (García et al, 2020), landslide detection (Syzdykbayev et al, 2020) and glacier analysis (Telling et al, 2017). Similarly, the technology has been applied to the urban environment, for example, to understand changes in the urban environment as a result of development (Zhou et al, 2020) or natural disaster (Wang and Li, 2020).…”

Section: Applications With Lidar Datamentioning

confidence: 99%

Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction

Stevenson,

Mues,

Bravo

2021

Preprint

View full text Add to dashboard Cite

LiDAR (short for "Light Detection And Ranging" or "Laser Imaging, Detection, And Ranging") technology can be used to provide detailed three-dimensional elevation maps of urban and rural landscapes. To date, airborne LiDAR imaging has been predominantly confined to the environmental and archaeological domains. However, the geographically granular and open-source nature of this data also lends itself to an array of societal, organizational and business applications where geo-demographic type data is utilised. Arguably, the complexity involved in processing this multi-dimensional data has thus far restricted its broader adoption. In this paper, we propose a series of convenient task-agnostic tile elevation embeddings to address this challenge, using recent advances from unsupervised Deep Learning. We test the potential of our embeddings by predicting seven English indices of deprivation (2019) for small geographies in the Greater London area. These indices cover a range of socio-economic outcomes and serve as a proxy for a wide variety of downstream tasks to which the embeddings can be applied. We consider the suitability of this data not just on its own but also as an auxiliary source of data in combination with demographic features, thus providing a realistic use case for the embeddings. Having trialled various model/embedding configurations, we find that our best performing embeddings lead to Root-Mean-Squared-Error (RMSE) improvements of up to 21% over using standard demographic features alone. We also demonstrate how our embedding pipeline, using Deep Learning combined with K-means clustering, produces coherent tile segments which allow the latent embedding features to be interpreted.

show abstract

Persistent homology on LiDAR data to detect landslides

Cited by 21 publications

References 43 publications

A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

Detecting and Monitoring Early Post-Fire Sliding Phenomena Using UAV–SfM Photogrammetry and t-LiDAR-Derived Point Clouds

Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction

Contact Info

Product

Resources

About