Distributed processing of Dutch AHN laser altimetry changes of the built-up area

Cserép, Máté; Lindenbergh, Roderik

doi:10.1016/j.jag.2022.103174

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Sometimes it can be useful if we combine images with Lidar data. This part of Giwer has not been completed, but it will in the future [13][14][15].…”

Section: Workflowbuildermentioning

confidence: 99%

GeoImage Workflow Editing Resources: GIWER

Elek¹

2023

New Trends and Challenges in Open Data

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While various open source and commercial image processing software exist, they often lack high level image processing skills, like the flexibility to assemble custom algorithm workflows, where the experience of the evaluator can be built into the system.We have developed the Giwer (acronym for GeoImage Workflow Editing Resources, pronounce gaiver) open source software package for handling, processing and analysing drone images. Giwer can be used to process large quantities of drone images – both traditional RGB and multispectral recordings. Organized into a database, the images can be searched and collected based on their attribute data. Processing is aided by a rich library of functions such as digital filter bank, classification, clustering, mathematical statistics, raster calculator, image algebra, and more. From the functions available in Giwer, arbitrary processing processes, i.e. workflows can be created, so the user can compile the most suitable processing procedures based on his own knowledge and experience, which he can apply not only for one image, but also for many images at once.

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“…Sometimes it can be useful if we combine images with Lidar data. This part of Giwer has not been completed, but it will in the future [13][14][15].…”

Section: Workflowbuildermentioning

confidence: 99%

GeoImage Workflow Editing Resources: GIWER

Elek¹

2023

New Trends and Challenges in Open Data

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“…Meanwhile, many countries have been actively collecting nationwide 3D elevation data using airborne lidar to address the growing demand for high-quality height information [29][30][31]. As a result, the amount of airborne lidar data has reached an unprecedented level in terms of spatial and temporal coverage.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the amount of airborne lidar data has reached an unprecedented level in terms of spatial and temporal coverage. It posed challenges in developing new scalable processing techniques to handle these 'big data' efficiently [31][32][33]. Differencing multitemporal topographic data also confronts such challenges such that the previous solutions against the remarkable artificial elevation changes are unscalable and often become impractical at larger spatial scales.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Method to Improve Large-Scale Lidar Topographic Differencing Results

Jung,

Jung

2023

Remote Sensing

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Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors along flight paths are often included in the differencing results, hindering the accurate analysis of the topographic changes. This paper proposes a new scalable method to alleviate the problematic systematic errors with a high degree of automation in consideration of the practical limitations raised when processing the rapidly increasing amount of large-scale lidar datasets. The proposed method focused on estimating the displacements caused by vertical positioning errors, which are the most critical error source, and adjusting the DTMs already produced as basic lidar products without access to the point cloud and raw data from the laser scanner. The feasibility and effectiveness of the proposed method were evaluated with experiments with county-level multitemporal airborne lidar datasets in Indiana, USA. The experimental results demonstrated that the proposed method could estimate the vertical displacement reasonably along the flight paths and improve the county-level lidar differencing results by reducing the problematic errors and increasing consistency across the flight paths. The improved differencing results presented in this paper are expected to provide more consistent information about topographic changes in Indiana. In addition, the proposed method can be a feasible solution to upcoming problems induced by rapidly increasing large-scale multitemporal lidar given recent active government-driven lidar data acquisition programs, such as the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP).

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