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

Abstract. A huge amount of legacy drilling data is available in geological survey but cannot be used directly as they are compiled and recorded in an unstructured textual form and using different formats depending on the database structure, company, logging geologist, investigation method, investigated materials and/or drilling campaign. They are subjective and plagued by uncertainty as they are likely to have been conducted by tens to hundreds of geologists, all of whom would have their own personal biases. dh2loop (https://github.com/Loop3D/dh2loop, last access: 30 September 2021) is an open-source Python library for extracting and standardizing geologic drill hole data and exporting them into readily importable interval tables (collar, survey, lithology). In this contribution, we extract, process and classify lithological logs from the Geological Survey of Western Australia (GSWA) Mineral Exploration Reports (WAMEX) database in the Yalgoo–Singleton greenstone belt (YSGB) region. The contribution also addresses the subjective nature and variability of the nomenclature of lithological descriptions within and across different drilling campaigns by using thesauri and fuzzy string matching. For this study case, 86 % of the extracted lithology data is successfully matched to lithologies in the thesauri. Since this process can be tedious, we attempted to test the string matching with the comments, which resulted in a matching rate of 16 % (7870 successfully matched records out of 47 823 records). The standardized lithological data are then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling. dh2loop formats legacy data bridging the gap between utilization and maximization of legacy drill hole data and drill hole analysis functionalities available in existing Python libraries (lasio, welly, striplog).

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Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8120551 ◽

2019 ◽

Vol 8 (12) ◽

pp. 551 ◽

Cited By ~ 3

Author(s):

Raphael Knevels ◽

Helene Petschko ◽

Philip Leopold ◽

Alexander Brenning

Keyword(s):

Image Analysis ◽

Open Source ◽

Land Surface ◽

Region Growing ◽

Support Vector ◽

Visual Interpretation ◽

Object Based Image Analysis ◽

Object Based ◽

Geographic Object ◽

Gis Software

With the increased availability of high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), new opportunities for improved mapping of geohazards such as landslides arise. While the visual interpretation of LiDAR, HRDTM hillshades is a widely used approach, the automatic detection of landslides is promising to significantly speed up the compilation of inventories. Previous studies on automatic landslide detection often used a combination of optical imagery and geomorphometric data, and were implemented in commercial software. The objective of this study was to investigate the potential of open source software for automated landslide detection solely based on HRDTM-derived data in a study area in Burgenland, Austria. We implemented a geographic object-based image analysis (GEOBIA) consisting of (1) the calculation of land-surface variables, textural features and shape metrics, (2) the automated optimization of segmentation scale parameters, (3) region-growing segmentation of the landscape, (4) the supervised classification of landslide parts (scarp and body) using support vector machines (SVM), and (5) an assessment of the overall classification performance using a landslide inventory. We used the free and open source data-analysis environment R and its coupled geographic information system (GIS) software for the analysis; our code is included in the Supplementary Materials. The developed approach achieved a good performance (κ = 0.42) in the identification of landslides.

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Quantitative analysis of subcellular distributions with an open-source, object-based tool

Biology Open ◽

10.1242/bio.055228 ◽

2020 ◽

Vol 9 (10) ◽

pp. bio055228 ◽

Cited By ~ 2

Author(s):

Pearl V. Ryder ◽

Dorothy A. Lerit

Keyword(s):

Image Analysis ◽

Open Source ◽

Three Dimensional ◽

Analysis Data ◽

Form And Function ◽

Object Based Image Analysis ◽

Object Based ◽

And Function ◽

Microscopy Images ◽

Complete Process

ABSTRACTThe subcellular localization of objects, such as organelles, proteins, or other molecules, instructs cellular form and function. Understanding the underlying spatial relationships between objects through colocalization analysis of microscopy images is a fundamental approach used to inform biological mechanisms. We generated an automated and customizable computational tool, the SubcellularDistribution pipeline, to facilitate object-based image analysis from three-dimensional (3D) fluorescence microcopy images. To test the utility of the SubcellularDistribution pipeline, we examined the subcellular distribution of mRNA relative to centrosomes within syncytial Drosophila embryos. Centrosomes are microtubule-organizing centers, and RNA enrichments at centrosomes are of emerging importance. Our open-source and freely available software detected RNA distributions comparably to commercially available image analysis software. The SubcellularDistribution pipeline is designed to guide the user through the complete process of preparing image analysis data for publication, from image segmentation and data processing to visualization.This article has an associated First Person interview with the first author of the paper.

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Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

BMC Bioinformatics ◽

10.1186/1471-2105-10-87 ◽

2009 ◽

Vol 10 (1) ◽

pp. 87 ◽

Cited By ~ 159

Author(s):

Navdeep Jaitly ◽

Anoop Mayampurath ◽

Kyle Littlefield ◽

Joshua N Adkins ◽

Gordon A Anderson ◽

...

Keyword(s):

Mass Spectrometry ◽

High Resolution ◽

Open Source ◽

Open Source Software ◽

Software Package ◽

High Resolution Mass Spectrometry ◽

Mass Spectrometry Data ◽

Open Source Software Package ◽

Automated Processing ◽

Resolution Mass

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AN OPTIMISED SYSTEM FOR GENERATING MULTI-RESOLUTION DTMS USING NASA MRO DATASETS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-115-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 115-121

Author(s):

Y. Tao ◽

J.-P. Muller ◽

P. Sidiropoulos ◽

J. Veitch-Michaelis ◽

V. Yershov

Keyword(s):

Open Source ◽

Cross Correlation ◽

Stereo Matching ◽

Uncertainty Estimation ◽

Confidence Estimation ◽

Registration Accuracy ◽

Processing Chain ◽

Trade Offs ◽

Refinement Method ◽

The Eu

Within the EU FP-7 iMars project, a fully automated multi-resolution DTM processing chain, called Co-registration ASP-Gotcha Optimised (CASP-GO) has been developed, based on the open source NASA Ames Stereo Pipeline (ASP). CASP-GO includes tiepoint based multi-resolution image co-registration and an adaptive least squares correlation-based sub-pixel refinement method called Gotcha. The implemented system guarantees global geo-referencing compliance with respect to HRSC (and thence to MOLA), provides refined stereo matching completeness and accuracy based on the ASP normalised cross-correlation. We summarise issues discovered from experimenting with the use of the open-source ASP DTM processing chain and introduce our new working solutions. These issues include global co-registration accuracy, de-noising, dealing with failure in matching, matching confidence estimation, outlier definition and rejection scheme, various DTM artefacts, uncertainty estimation, and quality-efficiency trade-offs.

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OBIA4RTM – towards an operational open-source solution for coupling object-based image analysis with radiative transfer modelling

European Journal of Remote Sensing ◽

10.1080/22797254.2020.1810132 ◽

2020 ◽

pp. 1-12

Author(s):

Lukas Graf ◽

Levente Papp ◽

Stefan Lang

Keyword(s):

Image Analysis ◽

Radiative Transfer ◽

Open Source ◽

Object Based Image Analysis ◽

Object Based ◽

Transfer Modelling

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A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

Remote Sensing ◽

10.3390/rs6076111 ◽

2014 ◽

Vol 6 (7) ◽

pp. 6111-6135 ◽

Cited By ~ 41

Author(s):

Daniel Clewley ◽

Peter Bunting ◽

James Shepherd ◽

Sam Gillingham ◽

Neil Flood ◽

...

Keyword(s):

Image Analysis ◽

Open Source ◽

Object Based Image Analysis ◽

Object Based ◽

Geographic Object

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AUTOMATED CALCULATION OF CHARACTERISTICS OF THE CATCHMENT BASIN AS A TOOL OF RUNOFF MODELLING

Interexpo GEO-Siberia ◽

10.33764/2618-981x-2019-4-1-83-89 ◽

2019 ◽

Vol 4 (1) ◽

pp. 83-89

Author(s):

Nikita Baldakov ◽

Alexey Kudishin

Keyword(s):

Open Source ◽

Open Source Software ◽

Catchment Area ◽

Drainage Basin ◽

Heterogeneous Data ◽

Catchment Basin ◽

Automated Processing

The modeling of runoff from the catchment area requires automated processing of a large amount of heterogeneous data. The article presents the results of the development of tools for automating the calculation of the drainage basin characteristics based on open source software.

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