An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification

Grippa, Taïs; Lennert, Moritz; Beaumont, Benjamin; Vanhuysse, Sabine; Stéphenne, Nathalie; Wolff, Éléonore

doi:10.3390/rs9040358

Cited by 76 publications

(100 citation statements)

References 49 publications

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“…Python was used in order to chain commands of GRASS GIS and R directly in the same interface, and in a similar fashion as the chain presented in a previous publication 16 and publicly available.…”

Section: Processing Chain Software and Toolsmentioning

confidence: 99%

“…The minsize parameter was fixed in order to match the desired minimum mapping unit of the final map, i.e., 3.75 m². The add-on allows users to select different USPO approaches presented in previous studies 8,12 and its implementation is described in Grippa et al (2017) 16 . It generates a stack of different segmentation results by varying the threshold parameter and selects the one that obtains the largest score for an optimization function.…”

Section: Segmentation and Unsupervised Segmentation Parameter Optimizmentioning

confidence: 99%

“…Each local zone was then segmented using a locally optimized segmentation parameter. The whole framework was based on an open source semi-automated processing chain 16 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

Grippa

Georganos

Vanhuysse

et al. 2017

Remote Sensing Technologies and Applications in Urban Environments II

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Mapping large heterogeneous urban areas using object-based image analysis (OBIA) remains challenging, especially with respect to the segmentation process. This could be explained both by the complex arrangement of heterogeneous land-cover classes and by the high diversity of urban patterns which can be encountered throughout the scene. In this context, using a single segmentation parameter to obtain satisfying segmentation results for the whole scene can be impossible. Nonetheless, it is possible to subdivide the whole city into smaller local zones, rather homogeneous according to their urban pattern. These zones can then be used to optimize the segmentation parameter locally, instead of using the whole image or a single representative spatial subset. This paper assesses the contribution of a local approach for the optimization of segmentation parameter compared to a global approach. Ouagadougou, located in sub-Saharan Africa, is used as case studies. First, the whole scene is segmented using a single globally optimized segmentation parameter. Second, the city is subdivided into 283 local zones, homogeneous in terms of building size and building density. Each local zone is then segmented using a locally optimized segmentation parameter. Unsupervised segmentation parameter optimization (USPO), relying on an optimization function which tends to maximize both intra-object homogeneity and inter-object heterogeneity, is used to select the segmentation parameter automatically for both approaches. Finally, a land-use/land-cover classification is performed using the Random Forest (RF) classifier. The results reveal that the local approach outperforms the global one, especially by limiting confusions between buildings and their bare-soil neighbors.

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Section: Processing Chain Software and Toolsmentioning

confidence: 99%

Section: Segmentation and Unsupervised Segmentation Parameter Optimizmentioning

confidence: 99%

See 1 more Smart Citation

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

Grippa

Georganos

Vanhuysse

et al. 2017

Remote Sensing Technologies and Applications in Urban Environments II

Self Cite

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“…Last but not least, Clewley et al [16] built a modular system accessed through Python to conduct Geographical Object-Based Image Analysis (GEOBIA) as an open-source package with functionality similar to existing GEOBIA packages. Other remote sensing domains have also encompassed efforts for automation; for instance, Grippa et al [17] developed a semi-automated and open-source processing chain for urban object classification.…”

Section: Introductionmentioning

confidence: 99%

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Stratoulias

Tolpekin

et al. 2017

Remote Sensing

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Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.

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“…The primary input data consisted of land-cover (LC) maps (Figures 1 and 2) derived from very-high resolution (VHR) satellite imagery, i.e., WorldView-3 and Pléiades for Ouagadougou and Dakar, respectively, with a spatial resolution of 0.5 m. These were produced using a semiautomated object-based image analysis (OBIA) [33] framework based on open-source solutions [34][35][36][37]. The overall accuracy (OA) of the LC products was 93.4% and 89.5% for Ouagadougou and Dakar, respectively.…”

Section: Input Datamentioning

confidence: 99%

Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics

Grippa

Georganos

Zarougui

et al. 2018

IJGI

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Up-to-date and reliable land-use information is essential for a variety of applications such as planning or monitoring of the urban environment. This research presents a workflow for mapping urban land use at the street block level, with a focus on residential use, using very-high resolution satellite imagery and derived land-cover maps as input. We develop a processing chain for the automated creation of street block polygons from OpenStreetMap and ancillary data. Spatial metrics and other street block features are computed, followed by feature selection that reduces the initial datasets by more than 80%, providing a parsimonious, discriminative, and redundancy-free set of features. A random forest (RF) classifier is used for the classification of street blocks, which results in accuracies of 84% and 79% for five and six land-use classes, respectively. We exploit the probabilistic output of RF to identify and relabel blocks that have a high degree of uncertainty. Finally, the thematic precision of the residential blocks is refined according to the proportion of the built-up area. The output data and processing chains are made freely available. The proposed framework is able to process large datasets, given that the cities in the case studies, Dakar and Ouagadougou, cover more than 1000 km 2 in total, with a spatial resolution of 0.5 m.

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An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification

Cited by 76 publications

References 49 publications

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics

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