Topography Class

[Berk Boyraz]

Ihear ArchiCAD has better IFC support, so you might want to try that though. FreeCAD (open-source BIM) also has support for all IfcProduct types if you want to be very selective about how you export.

You might want to try out FreeCAD (get the latest 0.18 version, and if the property sets are out of date, I can point you to a file in the bleeding edge repo which has the latest data), which currently supports the ability to assign any geometric representation to any IFC Product type, and also supports all property sets. I am working on improving this support to include the attributes of each IFC product and updating the values to IFC4Add2, which should be implemented soon.

as @jwouellette says - the IfcGeographicElement is a new class in IFC4. In IFC2x3 coordination view the topography was directly included as a shape representation of IfcSite. In IFC4 it had been separated out, the IfcSite is now a container and top of the spatial structure. The terrain, IfcGeographicElement/TERRAIN is now an independent entity and associated with the site.

Interesting, things must be starting to improve. For the record, tested under Revit 2018, using the IFC exporter, it does not work. When exporting to IFC2X3, Topography becomes IfcSite. When exporting to IFC4, the Topography is simply not exported, regardless of the value in the IfcExportAs parameter, at least in my file.

Indeed, there is a lot of improvement. And indeed the development on Github is only for R2019.

To fully benefit the latest improvements you can build your own solution, or you could try the latest pre-build beta: -ifc/releases/tag/IFC4RV-beta-v0.6.2

For IFC4 RV 1.1 all Classes of IfcElement and IfcSpatialElement are now supported.

Sample with all the RV valid classes (viewable with BIMVision or FZKviewer):

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Human brain electric activity can be measured at high temporal and fairly good spatial resolution via electroencephalography (EEG). The EEG microstate analysis is an increasingly popular method used to investigate this activity at a millisecond resolution by segmenting it into quasi-stable states of approximately 100 ms duration. These so-called EEG microstates were postulated to represent atoms of thoughts and emotions and can be classified into four classes of topographies A through D, which explain up to 90% of the variance of continuous EEG. The present study investigated whether these topographies are primarily driven by alpha activity originating from the posterior cingulate cortex (all topographies), left and right posterior cortices, and the anterior cingulate cortex (topographies A, B, and C, respectively). We analyzed two 64-channel resting state EEG datasets (N = 61 and N = 78) of healthy participants. Sources of head-surface signals were determined via exact low resolution electromagnetic tomography (eLORETA). The Hilbert transformation was applied to identify instantaneous source strength of four EEG frequency bands (delta through beta). These source strength values were averaged for each participant across time periods belonging to a particular microstate. For each dataset, these averages of the different microstate classes were compared for each voxel. Consistent differences across datasets were identified via a conjunction analysis. The intracortical strength and spatial distribution of alpha band activity mainly determined whether a head-surface topography of EEG microstate class A, B, C, or D was induced. EEG microstate class C was characterized by stronger alpha activity compared to all other classes in large portions of the cortex. Class A was associated with stronger left posterior alpha activity than classes B and D, and class B was associated with stronger right posterior alpha activity than A and D. Previous results indicated that EEG microstate dynamics reflect a fundamental mechanism of the human brain that is altered in different mental states in health and disease. They are characterized by systematic transitions between four head-surface topographies, the EEG microstate classes. Our results show that intra-cortical alpha oscillations, which likely reflect decreased cortical excitability, primarily account for the emergence of these classes. We suggest that microstate class dynamics reflect transitions between four global attractor states that are characterized by selective inhibition of specific intra-cortical regions.

Purpose: We investigated the efficiency of a convolutional neural network applied to corneal topography raw data to classify examinations of 3 categories: normal, keratoconus (KC), and history of refractive surgery (RS).

Methods: A total of 3,000 Orbscan examinations (1,000 of each class) of different patients of our institution were selected for model training and validation. One hundred examinations of each class were randomly assigned to the test set. For each examination, the raw numerical data from "elevation against the anterior best fit sphere (BFS)," "elevation against the posterior BFS" "axial anterior curvature," and "pachymetry" maps were used. Each map was a square matrix of 2,500 values. The 4 maps were stacked and used as if they were 4 channels of a single image.A convolutional neural network was built and trained on the training set. Classification accuracy and class wise sensitivity and specificity were calculated for the validation set.

Conclusion: Using combined corneal topography raw data with a convolutional neural network is an effective way to classify examinations and probably the most thorough way to automatically analyze corneal topography. It should be considered for other routine tasks performed on corneal topography, such as refractive surgery screening.

Geomorphological maps that delineate the topography of similar shapes and similar formation processes have been produced in many countries to estimate the ground strength and establish plans for flood and sediment disaster prevention and regional development. To create a geomorphological map, experts classify the topography by interpolating aerial photographs and other data such as satellite imagery and then clarify the differences in causes, constituent materials, and time of formation using field surveys and other references. However, this type of labor-intensive work is not feasible over a wide area. Around the 1990s, Pike (1988) and Dikau et al. (1991) proposed computer-based methods of automated classification of topography by terrain measurements using digital elevation models (DEMs) (hereafter, terrain classification). Since then, many geomorphological classification methods ranging from landform elements of mountain slopes (MacMillan et al. 2000, 2003; Reuter et al. 2006; Jasiewicz and Stepinski 2013) to landform patterns (Prima et al. 2006; Saadat et al. 2008) have been developed, and geomorphological mapping of global physiography at various scales has been proposed for several applications (Meybeck et al. 2001; Iwahashi and Pike 2007; Drăguţ and Eisank 2012; Sayre et al. 2014; Iwahashi et al. 2018a).

Geological maps have been produced in many countries, but the emphasis is on lithological and chronological classification. There are few maps edited for engineering use, such as to capture differences between land in populated areas (e.g., alluvial fans and flood plains), or to reflect civil and geological features (e.g., soft and hard rock in mountainous areas). Since the landform classes indicate landform materials and are one of the most appropriate proxies (Wakamatsu and Matsuoka 2013; Hengl et al. 2017), the automated terrain classification maps using DEMs, which were modeled after the current geomorphological maps, are often used to estimate earthquake shaking and soil distribution. Note that terrain classifications using DEMs are used to analyze and describe the morphological features of topography regardless of their causes and are strictly different in nature from expert geomorphological maps. Moreover, topography is not a substitute for geological field surveys or field measurements. However, by quantitatively classifying and describing the topography using a DEM, it is possible to obtain an overview of the ground in a wide area efficiently and quickly, which would be helpful in preliminary engineering investigations.

This study aims to develop a classification method that can distinguish landform elements even in urban plains and mountains. This method is used to produce a map of Japan in which geomorphological and geoengineering classifications coexist without large contradictions, and the classification of topography reflects the ground vulnerability of both alluvial plains and mountainous areas, enabling landslide susceptibility zoning.

Main islands of Japan and the central portion of Honshu Island. The shaded relief image of the central portion of Honshu Island (within the black framework) shows the locations of large cities, densely inhabited district (DID), and active volcanoes

The mountains of Japan are steep watersheds that experience frequent slope failures and landslides (Oguchi et al. 2001). Sediments from these mountains are deposited in plains, forming alluvial fans and plains. Japan is regularly impacted by natural disasters such as frequent earthquakes and typhoons (e.g., Mid Niigata Prefecture Earthquake in 2004 (Sato et al. 2005), the 2011 off the Pacific coast of Tohoku Earthquake (often called Eastern Japan Great Earthquake or Great East Japan Earthquake and Tsunami) (Okada et al. 2011), landslides caused by the heavy rainfalls in Hiroshima City in August 2014 (Wang et al. 2015), the 2016 Kumamoto Earthquake (Matsunaga et al. 2019), and Matsunaga et al. 2019 Typhoon Hagibis (NHK WORLD-JAPAN 2019)). In these disasters, various ground disasters such as landslides, liquefaction, and flooding occurred.

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