Population growth, economic development, and rural migration to urban areas have caused rapid expansion of urban centres in Ghana. One reason is that spatial planning and in particular urban planning face different social, economic and political challenges which hinder a structured and planned urban development, therefore causing urban sprawl. We hypothesise that different peri-urban patterns are driven by geographical, historical, cultural and economic discrepancies between southern and northern Ghana, and reflect the effectiveness of land use planning instruments. We tested our hypothesis by comparing patterns of urban development in two case study regions: Takoradi in southern Ghana and Bolgatanga in northern Ghana, representing an economically vibrant and a non-vibrant region, respectively. This paper provides new insights for the study sites based on a mixed-method approach. We applied an interdisciplinary approach combining expert interviews, a literature review, and a bi-temporal change analysis based on remote sensing/geo-information systems. We assigned confidence levels of the findings from the respective methods based on their plausibility and sensitivity. Expert opinion indicated that land use planning fails due to the lack of implementation of legal regulations, to the customary land tenure and lack of participation of local citizens in the planning process. The remote sensing analysis revealed that urban development was stronger in Takoradi (7.1% increase between 2007 and 2013) than in Bolgatanga (1.1% increase between 2007 and 2013). Urban development patterns differ with a dominance of small-scale scattered settlement units (SUs) in Bolgatanga and a mixture of small- and large-scale SUs in Takoradi. Besides population growth, markets and industry are identified as major drivers of urban development in the Takoradi area (large SUs) and customary land tenure in the Bolgatanga area (small SUs)

Up-to-date geodatasets on railway infrastructure are valuable resources for the field of transportation. This paper investigates three methods for mapping the center lines of railway tracks using heterogeneous sensor data: (i) conditional selection of satellite navigation (GNSS) data, (ii) a combination of inertial measurements (IMU data) and GNSS data in a Kalman filtering and smoothing framework and (iii) extraction of center lines from laser scanner data. Several combinations of the methods are compared with a focus on mapping in tree-covered areas. The center lines of the railway tracks are extracted by applying these methods to a test dataset collected by a road-rail vehicle. The guard rails in the test area were also extracted during the center line detection process. The combination of methods (i) and (ii) gave the best result for the track on which the measurement vehicle had moved, mapping almost 100% of the track. The combination of methods (ii) and (iii) and the combination of all three methods gave the best result for the other parallel tracks, mapping between 25% and 80%. The mean perpendicular distance of the mapped center lines from the reference data was 1.49 meters.

With the HMC Data Professionals Survey 2024, the Helmholtz Metadata Collaboration (HMC) assessed the current state, challenges and requirements of Helmholtz staff in the area of research data management (RDM) and FAIR data practices. The survey focused on data professionals involved in RDM-related tasks in the Helmholtz Association. A total of 156 valid responses were collected from 16 out of 18 Helmholtz centres and all six Helmholtz research fields. This diversity ensures that the survey captures a representative range of perspectives. The results show that RDM work is spread across different stakeholders, professions and organisational levels. While most respondents are familiar with their centre’s data policy, the majority have no formal training in RDM - although the data reflects widespread interest in RDM-related training. The results provide a snapshot into the implementation of the FAIR principles in the Helmholtz data ecosystem, and the collaborative use and development of various RDM tools. In particular, the survey highlights the prevalent challenges in RDM work, with many respondents reporting limited resources, gaps in technical knowledge, lack of infrastructure and lack of technical solutions. The survey results, complemented by qualitative feedback from focus groups, will guide HMC’s strategic initiatives to improve the FAIR data ecosystem within the Helmholtz Association. (HMC Report 3) HMC Data Professionals Survey 2024