Automated segmentation parameter selection and classification of urban scenes using open-source software

Bock, S.; Immitzer, Markus; Atzberger, Clement

doi:10.3990/2.449

Cited by 2 publications

(2 citation statements)

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“…The most common software for object-based image analysis is eCognition Developer, owned by Trimble Inc.; however, in recent years the interest for Free and Open-Source Software (FOSS) for GeOBIA is progressively increasing as testified by numerous papers employing combined solutions using Orfeo ToolBox, R, GRASS GISS, QGIS or Doker (Van De Kerchove et al, 2014;Böck et al, 2016;Grippa et al, 2016;Knoth, Nüst, 2017) but also by the development of specific tools such as GeoDMA (Körting et al, 2013) and InterIMAGE (Costa et al, 2010).…”

Section: Overview Of the Methodsmentioning

confidence: 99%

Theory and practice for an object-based approach in archaeological remote sensing

Magnini¹,

Bettineschi²

2019

Journal of Archaeological Science

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Section: Overview Of the Methodsmentioning

confidence: 99%

Theory and practice for an object-based approach in archaeological remote sensing

Magnini¹,

Bettineschi²

2019

Journal of Archaeological Science

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“…For example, [24] developed a workflow for urban Land Use and Land Cover (LULC) classification using GRASS GIS and R. They also published the analysis reproducibly as an executable notebook. The work in [25] used the Orfeo ToolBox and R for automated selection of segmentation parameters and classification of urban scenes. The work in [26] applied GRASS GIS to map impervious surfaces using aerial images and LIDAR (Light Detection and Ranging) data.…”

Section: Foss For Geobiamentioning

confidence: 99%

Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers

Knoth

Nüst

2017

Remote Sensing

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Geographic Object-Based Image Analysis (GEOBIA) mostly uses proprietary software, but the interest in Free and Open-Source Software (FOSS) for GEOBIA is growing. This interest stems not only from cost savings, but also from benefits concerning reproducibility and collaboration. Technical challenges hamper practical reproducibility, especially when multiple software packages are required to conduct an analysis. In this study, we use containerization to package a GEOBIA workflow in a well-defined FOSS environment. We explore the approach using two software stacks to perform an exemplary analysis detecting destruction of buildings in bi-temporal images of a conflict area. The analysis combines feature extraction techniques with segmentation and object-based analysis to detect changes using automatically-defined local reference values and to distinguish disappeared buildings from non-target structures. The resulting workflow is published as FOSS comprising both the model and data in a ready to use Docker image and a user interface for interaction with the containerized workflow. The presented solution advances GEOBIA in the following aspects: higher transparency of methodology; easier reuse and adaption of workflows; better transferability between operating systems; complete description of the software environment; and easy application of workflows by image analysis experts and non-experts. As a result, it promotes not only the reproducibility of GEOBIA, but also its practical adoption.

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Automated segmentation parameter selection and classification of urban scenes using open-source software

Cited by 2 publications

References 0 publications

Theory and practice for an object-based approach in archaeological remote sensing

Theory and practice for an object-based approach in archaeological remote sensing

Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers

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