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

Knoth, Christian; Nüst, Daniel

doi:10.3390/rs9030290

Cited by 23 publications

(16 citation statements)

References 50 publications

(36 reference statements)

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“…Currently, the most commonly used software implementing OBIA-methods is the privative Definiens-eCognition ([6]), which provides a friendly graphical user-interface for non-programmers. There exist several free and open source OBIA-software but they are less popular ( [7,8]). …”

Section: Introductionmentioning

confidence: 99%

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

et al. 2017

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There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing).

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Section: Introductionmentioning

confidence: 99%

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

et al. 2017

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“…As we will see in Section 2, Docker can be used to share research scenarios and increase their transparency, portability, and reproducibility in the GISc world. Examples of these scenarios or use-cases are packaging algorithms, or object-based analysis of remote sensing images [91]. Apart from Docker, there are also compelling language-specific containerisation solutions.…”

Section: Linked Solutions In the Geospatial Contextmentioning

confidence: 99%

Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research

Zaragozí

Trilles

Navarro-Carrión

2020

IJGI

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Scientific reproducibility is essential for the advancement of science. It allows the results of previous studies to be reproduced, validates their conclusions and develops new contributions based on previous research. Nowadays, more and more authors consider that the ultimate product of academic research is the scientific manuscript, together with all the necessary elements (i.e., code and data) so that others can reproduce the results. However, there are numerous difficulties for some studies to be reproduced easily (i.e., biased results, the pressure to publish, and proprietary data). In this context, we explain our experience in an attempt to improve the reproducibility of a GIScience project. According to our project needs, we evaluated a list of practices, standards and tools that may facilitate open and reproducible research in the geospatial domain, contextualising them on Peng’s reproducibility spectrum. Among these resources, we focused on containerisation technologies and performed a shallow review to reflect on the level of adoption of these technologies in combination with OSGeo software. Finally, containerisation technologies proved to enhance the reproducibility and we used UML diagrams to describe representative work-flows deployed in our GIScience project.

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“…In addition to eCognition, several open‐source platforms have been developed for OBIA analysis [e.g. GEODMA (Körting, Garcia Fonseca, & Câmara, ), InterIMAGE (InterIMAGE, ), Grass GIS (GRASS Development Team, ), also see Knoth & Nüst, ].…”

Section: Object‐based Image Analysis (Obia)mentioning

confidence: 99%

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

Davis

2018

Archaeological Prospection

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Object-based image analysis (OBIA) is a method of assessing remote sensing data that uses morphometric and spectral parameters simultaneously to identify features in remote sensing imagery. Over the past 10-15 years, OBIA methods have been introduced to detect archaeological features. Improvements in accuracy have been attained by using a greater number of morphometric variables and multiple scales of analysis. This article highlights the developments that have occurred in the application of OBIA within archaeology and argues that OBIA is both a useful and necessary tool for archaeological research. Additionally, I discuss future research paths using this method. Some of the suggestions put forth here include: pushing for multifaceted research designs utilizing OBIA and manual interpretation, using OBIA methods for directly studying landscape settlement patterns, and increasing data sharing of methods between researchers. KEYWORDS automated feature extraction, landscape analysis, machine learning, object-based image analysis, pattern recognition, remote sensing

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

Cited by 23 publications

References 50 publications

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

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