In geodetic movement and deformation analysis, a novel application of close-range photogrammetry is presented. The achievable accuracy is increased by inserting a geodetic telescope in front of the camera, if necessary. Raspberry Pi computer and Pi camera module is preferred to take photos or videos. Calibration of the non-metric cameras is an important step to get reliable image coordinates. The images or videos made by the camera can be processed on the fly or post-processing is also available. The open-source Ulyxes system – developed in our department – is fully automated using ArUco markers and the OpenCV library. After some laboratory tests and calibration the system was applied during dynamic test loads and beam facture tests, where the robotic total stations are not fast enough and the GNSS technique is not accurate enough. The first application presented was the dynamic load test of the recently build railway bridge over the Danube in southern Budapest. Two points in the middle of the support gap were observed with two cameras while railway trains passed through with different speeds. The second application took place in a laboratory of our university where reinforced concrete beam fracture tests are made. Sixteen ArUco markers were fixed on the beam and tracked from the video record.

Close range photogrammetry using low-cost cameras was applied to determine movement and deformation. 25-30 fps videos are processed by open-source programs based on OpenCV, which are part of the Ulyxes system.

Open-source solutions in geoinformatics have gradually come into the focus of attention over the past decades, becoming of the most well-promising opportunities in tertiary education. It is indubitable that students have to develop their skills to find, apply and contribute to the existing open-source opportunities, besides developing skills regarding coding and programming logic. At the Budapest University of Technology and Economics, a complex system has been developed to support students on this matter. This system is fully based on open-source software and free cloud services. Consequently, all the teaching materials have creative common licenses supported by the use of open source software. The main entry point of the educational materials is Moodle, considered as one of the most popular learning management systems. The majority of the source codes and explanation texts/notes used for teaching are published in Jupyter notebooks, stored on personalized GitHub pages. For opening and testing the Jupyter notebooks, the students can use Google Colab. Another challenge worth mentioning is the continuous assessment evaluation format. Moodle supports the creation of tests based upon a wide variety of question types (e.g. multiple choice, true/false, drag and drop markers, etc), which are stored in a question bank. The test is generated by randomly selecting a given number of questions; therefore, taking the test a couple of times is highly recommended. As stated by many, this way of self-studying is popular among students these days and efficient in achieving remarkable progress. Our presentation shares either the developed system or the gained experience over the recent years.

In the recent years, point cloud technologies, such as Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanners (TLS), Aerial Laser Scanners (ALS), let alone Mobile Mapping Systems (MMS), have come into the focus of attention and have been a subject of considerable public concern in mapping. Thanks to these new techniques, experts can survey large areas with sufficient and homogenous accuracy with high resolution. It comes from this that there are several areas where the point clouds can be used. One of the possible applications of point clouds is updating land registry maps. Many countries worldwide face the issue that a significant part of their large-scale land registry maps are outdated and inaccurate. One of these countries is Hungary, where more than eighty percent of digital cadastre maps were digitised using analogous maps in a scale range of 1:1000 – 1:4000. In this paper, a novel processing queue is presented to find the footprints of the building. Our solution is based on primarily well-known algorithms (RANSAC, DBSCAN) implemented in open-source Python packages. An automated flow was developed, composed of simple processing steps, to cut the point cloud into wall and roof segments and vectorise the wall points under roofs into building footprints. The algorithms and Python programs were tested in villages where detached houses are typical. Tests were made on three study areas in Hungary and we achieved well-promising results.

A new small open-source project is introduced in this presentation. The Find-GCP project can be used to automatize the measurement of the Ground Control Points (GCP) coordinates on images. It can be used in close photogrammetry tasks. The markers and their unique IDs are detected on the the photos using the ArUco open-source library which is part of the OpenCV contrib package. The output is compatible with OpenDroneMap (ODM) and VisualSfM, two well-known open source project. Beside the command line gcp_find.py tool, there are some utilities in this project to generate ArUco markers, visually check the results and more. This project comes from the Geo4All Lab of the Budapest University of Technology and Economics. Ground Control Points (GCP) are used to improve the accuracy of orthophotos and point clouds generated from images made by Unmanned Aerial Vehicles (UAV). GCPs are marked on the field and the coordinates are measured in a Coordinate Reference System (CRS) and are used to georeference the products of the photogrammetric process. There are open-source projects to process UAV images, the most known among them is probably the OpenDroneMap (ODM). Unfortunately there are no modules or tools to automatize the detection of GCP markers on the images. Our small project tries to fill this gap. It is a time-consuming task to collect the image coordinates of GCPs because of the usual large forward and side overlapping (~80%), one GCP may be visible on eight-ten images. Using unique markers for each CGP they can be found by a software. There have not been such widely used solutions for open-source programs so far. We hope the presented solution can be part of the workflow with ODM and other open-source software. We have used ArUco codes for indoor navigation and movement detection for few years. ArUco is an open-source library (part of the OpenCV contrib package) developed for augmented reality applications. These squared markers have a wide black border and an inner binary matrix. The unique pattern of the binary matrix is identified by an integer ID. We have made tests and our experiences are also presented, for example we realized that black and grey markers are better in sunshine to reduce the burnt in effect of white areas. The source code of the Find-GCP project is available on GitHub.