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User perspectives on the general usefulness of the physiographic dataset at Level 3. Results were obtained through an online survey.
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The semi-automated mapping of landforms in Digital Elevation Models (DEMs) and derived products is a major research topic in geomorphometry. Mapping landforms over
vast areas is essential to better understand landform formative processes and the
morphogenesis of a landscape. Due to limitations of cell-based mapping systems,
approaches that use irre...
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Abstract As geomorphological processes operate at various spatial scales, their morphological expressions, i.e., land-surface variables (LSVs) should be scaled accordingly. Most approaches on landslide susceptibility modeling and landslide detection have been performed based on arbitrarily scaled LSVs. We propose a methodology to improve automated...
Citations
... A wide range of geomorphological features and landforms have already been shown to be identifiable and extractable from DTMs. The general methods were covered in various reviews [382][383][384][385][386][387][388][389][390][391][392][393], but those applicable to features specifically relevant to Mars (see Section 3.2) are as follows: rock glaciers [350], glacial terraces and ridges [394], glacial cirques [192,262], polygonal terrain [395][396][397], drumlins [386,398,399], glaciovolcanic features [400,401], landslides [402][403][404][405], deltaic features [406], watersheds [407], loess features [382,408], karst landscapes [409,410], relict patterned ground [411], fluvial terraces [412], and thalwegs [413]. ...
Data analysis methods have scarcely kept pace with the rapid increase in Earth observations, spurring the development of novel algorithms, storage methods, and computational techniques. For scientists interested in Mars, the problem is always the same: there is simultaneously never enough of the right data and an overwhelming amount of data in total. Finding sufficient data needles in a haystack to test a hypothesis requires hours of manual data screening, and more needles and hay are added constantly. To date, the vast majority of Martian research has been focused on either one-off local/regional studies or on hugely time-consuming manual global studies. Machine learning in its numerous forms can be helpful for future such work. Machine learning has the potential to help map and classify a large variety of both features and properties on the surface of Mars and to aid in the planning and execution of future missions. Here, we outline the current extent of machine learning as applied to Mars, summarize why machine learning should be an important tool for planetary geomorphology in particular, and suggest numerous research avenues and funding priorities for future efforts. We conclude that: (1) moving toward methods that require less human input (i.e., self- or semi-supervised) is an important paradigm shift for Martian applications, (2) new robust methods using generative adversarial networks to generate synthetic high-resolution digital terrain models represent an exciting new avenue for Martian geomorphologists, (3) more effort and money must be directed toward developing standardized datasets and benchmark tests, and (4) the community needs a large-scale, generalized, and programmatically accessible geographic information system (GIS).
... Transformation of conceptual semantic models for cirques into rule sets was emphasized by Eisank (2013) as a necessity to develop transparent workflows in order to improve objectivity and transferability of rule sets. This way, it is possible to further automate the creation of maps containing different landscape features (Drağuţ and Blaschke, 2006). ...
Cirques are complex landforms resulting from glacial erosion and can be used to estimate Equilibrium Line Altitudes and infer climate history. Automated extraction of cirques may help research on glacial geomorphology and climate change. Our objective was to test the transferability
of an object-based rule set for the extraction of glacial cirques, using lidar data and color-infrared orthophotos. In Vorarlberg (W-Austria), we selected one training area with well-developed cirque components to parameterize segmentation and classification criteria. The rule set was applied
to three test areas that are positioned in three altitudinal zones. Results indicate that the rule set was successful (81 percent) in the training area and a higher situated area (71 percent). Accuracy decreased in the two lower situated test areas (66 percent and 51 percent). We conclude
that rule sets are transferable to areas with a comparable geomorphological history. Yet, significant deviation from the training area requires a different extraction strategy.
... As the delineation of landforms is still not concretely defined amongst geomorphologists, the outlines of the reference data varies, depending on the expert who delineated the data. Several publications treat the aspect of assessing the accuracy of landform mapping approaches with more than one method in order to derive a more comprehensive estimation on the overall quality of the classification results [14,17,29,39]. ...
This study aims at the detection of gully-affected areas by applying object-based image analysis in the region of Taroudannt, Morocco, which is highly affected by gully erosion while simultaneously representing a major region of agro-industry with a high demand of arable land. As high-resolution optical satellite data are readily available from various sensors and with a much better temporal resolution than 3D terrain data, an area-wide mapping approach to extract gully-affected areas using only optical satellite imagery was developed. The methodology additionally incorporates expert knowledge and freely-available vector data in a cyclic object-based image analysis approach. This connects the two fields of geomorphology and remote sensing. The classification results show the successful implementation of the developed approach and allow conclusions on the current distribution of gullies. The results of the classification were checked against manually delineated reference data incorporating expert knowledge based on several field campaigns in the area, resulting in an overall classification accuracy of 62%. The error of omission accounts for 38% and the error of commission for 16%, respectively. Additionally, a manual assessment was carried out to assess the quality of the applied classification algorithm. The limited error of omission contributes with 23% to the overall error of omission and the limited error of commission contributes with 98% to the overall error of commission. This assessment improves the results and confirms the high quality of the developed approach for area-wide mapping of gully-affected areas in larger regions. In the field of landform mapping, the overall quality of the classification results is often assessed with more than one method to incorporate all aspects adequately.
... ArcGIS 10.1 and python/GDAL were used to calculate the LSPs Slope, Elevation Percentile (EPC), and Openness. The main steps of our analysis are based on the approaches of Anders et al. (2011) andEisank (2013). The sequence of segmentation and classification of the cirque components in the Object-Based Digital Terrain Analysis comprised image segmentation using the region merging multi-resolution segmentation algorithm (Baatz and Schäpe 2000), and classification rule development and ®Aristotle University of Thessaloniki, Greece ...
Für die Kartierung von Landformen auf Basis digitaler Geländemodelle (DGM) existieren verschiedenste halbautomatische Ansätze. Um die Objektivität und Flexibilität von Kartie-rungsansätzen zu erhöhen, wird in diesem Beitrag ein innovatives, objektbasiertes Verfah-ren vorgestellt und getestet. Das zweistufige Verfahren integriert in der ersten Stufe nicht überwachte oder überwachte Methoden für die Optimierung einer multiskalaren, regionsba-sierten Segmentierung von DGM. Das Ergebnis dieser Optimierung sind repräsentative DGM-Objektstrukturen. Diese werden in der zweiten Stufe anhand von wissensbasierten Regeln und Schwellwerten klassifiziert. Um die Objektivität der selektierten Regeln und Schwellwerte zu erhöhen, werden semantische Modelle verwendet. Diese Modelle stellen die in Definitionen meistgenannten Angaben zu Landformen explizit dar und verknüpfen diese Angaben mit den Eigenschaften digitaler Objekte. Das optimierte Verfahren wurde für die halbautomatische Kartierung von Drumlins aus einem DGM 5 m getestet. Das Klas-sifikationsergebnis ist zufriedenstellend, vor allem weil eine höhere Erkennungsrate erzielt wurde als mit einem nicht optimierten objektbasierten Verfahren. Da das neue Verfahren objektiv und universell einsetzbar ist, könnte es zu einem Standardverfahren für die halbautomatische Kartierung von Landformen aus DGM werden.
