Die gut erhaltene Terrassenlandschaft oberhalb von Ramosch hat eine lange Geschichte, deren ackerbauliche Nutzung im Mittelalter durch historische Quellen belegt ist. Doch bereits die... Show moreDie gut erhaltene Terrassenlandschaft oberhalb von Ramosch hat eine lange Geschichte, deren ackerbauliche Nutzung im Mittelalter durch historische Quellen belegt ist. Doch bereits die prähistorische Siedlung auf der Mottata beweist, dass sie in eine stark vom Menschen überprägte Landschaft eingebettet war. Um der Frage nach der Entwicklung der Terrassen nachzugehen, engagiert sich seit einigen Jahren ein interdisziplinäres Forschungsprojekt mit dem Ursprung dieser Anlagen. Show less
Zingman, I.; Saupe, D.; Penatti, O.A.B.; Lambers, K. 2016
We develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately... Show moreWe develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfectly regular enclosures but also for ruined ones with distorted angles, fragmented walls, or even a completely missing wall. Furthermore, it has a zero value for spurious structures with less than three sides of a perceivable rectangle. We show how the detection performance can be improved by learning a linear combination of the rectangularity and size features from just a few available representative examples and a large number of negatives. Our approach allowed detection of enclosures in the Silvretta Alps that were previously unknown. A comparative performance analysis is provided. Among other features, our comparison includes the state-of-the-art features that were generated by pretrained deep convolutional neural networks (CNNs). The deep CNN features, although learned from a very different type of images, provided the basic ability to capture the visual concept of the LEs. However, our handcrafted rectangularity-size features showed considerably higher performance. Show less
