Boundary classification and two-ended splittings of groups with isolated flats

Grain boundaries are decisive for many properties of materials. Due to short-range stress field their influence is primarily based on their atomic structure. Special character of grain boundary properties related to their structure, follows from the nature of atomic arrangements in the boundary cores, from the interfacial dislocation content and from the boundary mobility. All those aspects of boundary behaviour are strongly influenced by the boundary chemistry including various segregation phenomena. Approaches to the boundary classification and the interpretation of recent experimental results are discussed in the context of the complex relationship between microstructure and material properties. Such findings are essential for Grain Boundary Engineering proposed to improve the performance of polycrystalline materials.

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Multi-temporal Crop Type and Field Boundary Classification with Google Earth Engine

10.20944/preprints202004.0316.v1 ◽

2020 ◽

Author(s):

Michael Marszalek ◽

Maximilian Lösch ◽

Marco Körner ◽

Urs Schmidhalter

Keyword(s):

Vegetation Index ◽

Google Earth ◽

Federal State ◽

Support Vector ◽

Data Sets ◽

Field Boundary ◽

Data Set ◽

Crop Type ◽

Normalised Difference Vegetation Index ◽

Boundary Classification

Crop type and field boundary mapping enable cost-efficient crop management on the field scale and serve as the basis for yield forecasts. Our study uses a data set with crop types and corresponding field borders from the federal state of Bavaria, Germany, as documented by farmers from 2016 to 2018. The study classified corn, winter wheat, barley, sugar beet, potato, and rapeseed as the main crops grown in Upper Bavaria. Corresponding Sentinel-2 data sets include the normalised difference vegetation index (NDVI) and raw band data from 2016 to 2018 for each selected field. The influences of clouds, raw bands, and NDVI on crop type classification are analysed, and the classification algorithms, i.e., support vector machine (SVM) and random forest (RF), are compared. Field boundary detection and extraction are based on non-iterative clustering and a newly developed procedure based on Canny edge detection. The results emphasise the application of Sentinel’s raw bands (B1–B12) and RF, which outperforms SVM with an accuracy of up to 94%. Furthermore, we forecast data for an unknown year, which slightly reduces the classification accuracy. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

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Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs11212505 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2505 ◽

Cited By ~ 10

Author(s):

Crommelinck ◽

Koeva ◽

Yang ◽

Vosselman

Keyword(s):

Remote Sensing ◽

Image Segmentation ◽

Image Resolution ◽

Aerial Imagery ◽

Boundary Line ◽

Data Sets ◽

Remote Sensing Imagery ◽

Manual Delineation ◽

Segmentation Boundary ◽

Boundary Classification

Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02–0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation.

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Interfaces and Properties of Advanced Materials

Materials Science Forum ◽

10.4028/www.scientific.net/msf.512.5 ◽

2006 ◽

Vol 512 ◽

pp. 5-12

Author(s):

Václav Paidar

Keyword(s):

Grain Boundary ◽

Structural Properties ◽

Material Properties ◽

Advanced Materials ◽

Complex Relationship ◽

Grain Boundary Engineering ◽

Properties Of Materials ◽

Boundary Classification

Internal interfaces are decisive for many properties of materials. Both functional and structural properties of interfaces are briefly reviewed on selected examples. Approaches to the grain boundary classification are discussed in the context of the complex relationship between microstructure and material properties. Implications for grain boundary engineering are mentioned.

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