In recent years, unstable quay walls are a problem in The Netherlands. 100-year-old quay walls in cities like Amsterdam are collapsing and endanger people and property. The government needs to renovate unstable quay walls quickly. With 600 kilometre of quay wall in Amsterdam alone, this is a great challenge. Currently, unstable walls are found by deformation monitoring using tacheometry, which takes too much time for large scale monitoring. To increase both speed and coverage, a photogrammetric deformation analysis is proposed. In multiple epochs, at months interval, a series of images of the quay wall is made from a boat. In these images, feature points are identified and matched, where part of the feature points are matched across multiple epochs. All feature point observations are put in a multi-epoch least squares adjustment. This adjustment integrates both feature point observations of individual epochs and point deformations between multiple epochs. Using photogrammetry in combination with such a deformation adjustment has not been done previously, but has great advantages. The least squares adjustment allows to take the stochastic nature of the observations into account. This enables proper error propagation, such that not only quay wall stability can be assessed, but also the corresponding error budget. Results show that using two epochs 300 multi-epoch feature points can be found per square meter quay wall. With these points, sub-centimetre deformation can be estimated.

Photogrammetry is a well-established technique that has a significant impact on the use of images for mapping purposes. Employing feature extraction, matching, and the bundle adjustment, a large number of images can be automatically processed. Ingenieursbureau Geodelta developed an application for measurements in processed (oblique) aerial images. However, the quality of these measurements and the added value of oblique images on the adjustment's quality were unclear. This will be assessed by means of the theoretical standard deviation resulting from the bundle adjustment. Upon estimating this quality, an adapted RANSAC method is proposed that incorporates this quality as weights within its algorithm to extract geometric features. The objective is to evaluate whether this enhances the RANSAC results and could be applied to 3D reconstructions. The results indicate three key factors that influence the theoretical standard deviation: high tie point availability, larger observation angles, and image viewing direction. With this, the theoretical standard deviation for tie points in both Nadir and Oblique image sets separately approximates 3 centimeters in the horizontal direction and about 10 in the height direction. Combining the two sets enhances the results by nearly a factor of three in all directions because the Nadir images connect the Oblique images, combining the strong characteristics of both sets. This demonstrates the value of both Nadir and Oblique imagery. For image exteriors, the improvement is even more pronounced, yielding an improvement factor of 4 or 5. However, propagating this quality metric through a dense matching algorithm in an adapted, weighted RANSAC algorithm does not show significant improvements in the number of planes found or the percentage of points classified as inliers of those planes. Furthermore, the RANSAC method does not converge to a better result in fewer iterations using the proposed method.