MACHINE LEARNING FOR CLASSIFICATION OF AN ERODING SCARP SURFACE USING TERRESTRIAL PHOTOGRAMMETRY WITH NIR AND RGB IMAGERY

Abstract. Increasingly advanced and affordable close-range sensing techniques are employed by an ever-broadening range of users, with varying competence and experience. In this context a method was tested that uses photogrammetry and classification by machine learning to divide a point cloud into different surface type classes. The study site is a peat scarp 20 metres long in the actively eroding river bank of the Rotmoos valley near Obergurgl, Austria. Imagery from near-infra red (NIR) and conventional (RGB) sensors, georeferenced with coordinates of targets surveyed with a total station, was used to create a point cloud using structure from motion and dense image matching. NIR and RGB information were merged into a single point cloud and 18 geometric features were extracted using three different radii (0.02 m, 0.05 m and 0.1 m) totalling 58 variables on which to apply the machine learning classification. Segments representing six classes, dry grass, green grass, peat, rock, snow and target, were extracted from the point cloud and split into a training set and a testing set. A Random Forest machine learning model was trained using machine learning packages in the R-CRAN environment. The overall classification accuracy and Kappa Index were 98% and 97% respectively. Rock, snow and target classes had the highest producer and user accuracies. Dry and green grass had the highest omission (1.9% and 5.6% respectively) and commission errors (3.3% and 3.4% respectively). Analysis of feature importance revealed that the spectral descriptors (NIR, R, G, B) were by far the most important determinants followed by verticality at 0.1 m radius

Cutaneous lesions of the external ear

Skin diseases on the external aspect of the ear are seen in a variety of medical disciplines. Dermatologists, othorhinolaryngologists, general practitioners, general and plastic surgeons are regularly consulted regarding cutaneous lesions on the ear

Machine learning for classification of an eroding scarp surface using terrestrial photogrammetry with nir and rgb imagery

Increasingly advanced and affordable close-range sensing techniques are employed by an ever-broadening range of users, with varying competence and experience. In this context a method was tested that uses photogrammetry and classification by machine learning to divide a point cloud into different surface type classes. The study site is a peat scarp 20 metres long in the actively eroding river bank of the Rotmoos valley near Obergurgl, Austria. Imagery from near-infra red (NIR) and conventional (RGB) sensors, georeferenced with coordinates of targets surveyed with a total station, was used to create a point cloud using structure from motion and dense image matching. NIR and RGB information were merged into a single point cloud and 18 geometric features were extracted using three different radii (0.02 m, 0.05 m and 0.1 m) totalling 58 variables on which to apply the machine learning classification. Segments representing six classes, dry grass, green grass, peat, rock, snow and target, were extracted from the point cloud and split into a training set and a testing set. A Random Forest machine learning model was trained using machine learning packages in the R-CRAN environment. The overall classification accuracy and Kappa Index were 98% and 97% respectively. Rock, snow and target classes had the highest producer and user accuracies. Dry and green grass had the highest omission (1.9% and 5.6% respectively) and commission errors (3.3% and 3.4% respectively). Analysis of feature importance revealed that the spectral descriptors (NIR, R, G, B) were by far the most important determinants followed by verticality at 0.1 m radius

Regulated acid-base transport in the collecting duct

The renal collecting system serves the fine-tuning of renal acid-base secretion. Acid-secretory type-A intercalated cells secrete protons via a luminally expressed V-type H(+)-ATPase and generate new bicarbonate released by basolateral chloride/bicarbonate exchangers including the AE1 anion exchanger. Efficient proton secretion depends both on the presence of titratable acids (mainly phosphate) and the concomitant secretion of ammonia being titrated to ammonium. Collecting duct ammonium excretion requires the Rhesus protein RhCG as indicated by recent KO studies. Urinary acid secretion by type-A intercalated cells is strongly regulated by various factors among them acid-base status, angiotensin II and aldosterone, and the Calcium-sensing receptor. Moreover, urinary acidification by H(+)-ATPases is modulated indirectly by the activity of the epithelial sodium channel ENaC. Bicarbonate secretion is achieved by non-type-A intercalated cells characterized by the luminal expression of the chloride/bicarbonate exchanger pendrin. Pendrin activity is driven by H(+)-ATPases and may serve both bicarbonate excretion and chloride reabsorption. The activity and expression of pendrin is regulated by different factors including acid-base status, chloride delivery, and angiotensin II and may play a role in NaCl retention and blood pressure regulation. Finally, the relative abundance of type-A and non-type-A intercalated cells may be tightly regulated. Dysregulation of intercalated cell function or abundance causes various syndromes of distal renal tubular acidosis underlining the importance of these processes for acid-base homeostasis