ABF awards Bursary to Ryan Benade of Namibia for Master's Research
ABF has awarded Ryan Theodore Benade a tuition and registration bursary to undertake his Master’s in Spatial Sciences at the Namibia University of Science and Technology under the supervision of Professor Oluibukun Gbenga Ajayi.
This award continues a chain of generational mentorship: Professor Ajayi, too, was an ABF bursary recipient during his PhD studies at the Federal University of Technology, Minna. This demonstrates the Fund’s commitment to sustaining professional networks and capacity building in land management. The continuity reflects ABF’s ethos of long-lasting impact through educational support and knowledge sharing across generations of surveyors and spatial scientists.
Namibia’s agricultural potential is constrained by soil degradation, uneven fertilizer application, and limited spatial soil data, which together impede sustainable crop production. Ryan’s project entitled “Mapping Soil Macronutrients Using Multi-Spectral Remote Sensing and Machine Learning” addresses these challenges by predicting the spatial distribution of Nitrogen (N), Phosphorus (P), and Potassium (K) across key farming regions in Namibia. The study will generate continuous surface maps of soil fertility, enabling targeted interventions and optimised land use by fusing ground-sampled nutrient measurements with high-resolution spectral information from Sentinel-2 and Landsat 8.
The ABF bursary will cover a significant portion of his tuition and registration fees, freeing Ryan to dedicate his attention to research activities, field sampling, and data analysis without financial constraints. The expected decision-support tools, like the nutrient distribution maps and predictive models, will empower Namibian farmers and land managers to implement variable-rate fertilisation, thus reducing input waste and minimising nutrient runoff in alignment with UN Sustainable Development Goals 2 (Zero Hunger), 9 (Industry, Innovation and Infrastructure), 12 (Responsible Consumption and Production), 13 (Climate Action), and 15 (Life on Land).
A blended learning course design in Fit for Purposes’ Cadastral Survey
The Aubrey Barker Foundation in collaboration with FIG Foundation Grant will provide a grant of twenty thousand pounds sterling (£20,000), spread over two years, to this project to be run by Dr Trias Aditya (pictured left) from the Department of Geodetic Engineering of Universitas Gadjah Mada (UGM), Indonesia.
Background to the project
This project is set in Indonesia, the home of millions of land parcels spreading across numerous populated islands, both big and small islands, many of which have not been mapped and registered by the country. Under the current legal and institutional framework, systematic land titling activities from village to village are expensive and take a long time to complete. Land titling activities are
impossible without a complete cadastral map and active participation from communities and government of cers.
The International Federation of Surveyors (FIG) has published principles and guidelines to give a way forward to accelerate the land registration progress, called Fit for Purposes Land Administration (FFP-LA).
What is required is a paradigm shift from a top-down traditional cadastral survey and mapping into a bottom-up modern cadastral survey for accelerated land registration. This is currently lacking. To effect the changes requires a combination of modern survey techniques and community participation applying FFP-LA principles in order to accelerate and assure the quality of the land registration Short description of the project The project will develop a learning platform applying blended learning practices (a combination of online courses and eld visit interactions) in FFP Cadastral Survey (FFP-CS) for both in-house students and para surveyors (i.e. local representatives in the community) across the country. The project will develop course objectives, student outcomes and teaching materials of FFP-CS adhering to the Accreditation Board for Engineering and Technology (ABET) standards. An online assessment and certi cation system involving the Project Team’s University, National Land Agency and The Association of Surveyors will also be developed in the project. This blended learning program will produce an excellent capstone design for ABET curriculum and will speed up the community readiness for FFP-CS implementation.
Development of an integrated automatic image registration scheme
Oluibukun Ajayi (pictured above) reports on his PhD project: The Aubrey Barker project grant was awarded for the successful completion of my project which is part of a doctorate degree in Surveying and Geoinformatics at the Federal University of Technology, Minna, Nigeria in May 2018. The title of the funded research is ‘Development of an integrated automatic image registration scheme’ and it is aimed at developing an integrated model for the automatic registration of overlapping images captured from a drone with a view to optimising the process of tting images to existing maps. The grant was spent on eld work which consisted of hiring of equipment, such as the DGPS receivers used for the establishment of Ground Control Points (GCPs) and the Check Points (CPs), hiring of the Phantom 4 drone used for the image data acquisition, hiring of a project vehicle, payment of site allowances for eld/research assistants, beaconing of GCPs and CPs and marking them with re ective materials. Three different methods of describing features were integrated with epipolar correlation in the development of an optimised automatic image registration scheme. The developed algorithms were implemented using JAVA scripts and tested using the drone images in two different registration campaigns. Degraded images with poor spatial resolution and a small size were used for the rst campaign while images with excellent resolution, but of large size, were used for the second campaign.
In the course of this research, a novel integrated automatic image registration algorithm based on epipolar correlation was developed which proved to be more robust (with respect to speed and accuracy) than the automatic image registration algorithms that are based on selected conventional feature descriptors. The novel registration algorithm integrates both the geometric and epipolar constraints which makes it more robust in terms of accuracy and speed (processing time). Finally, an automatic image registration software which contains a stereo comparator and a module for automatic camera calibration was developed for easy implementation of the developed algorithms. The developed registration scheme will be of particular interest to the military, cartographers, radiologists, digital photogrammetrists and remote sensing experts as a tool for educational, training and industrial applications