Unmanned Aerial System Imagery, Land Data and User Needs: A Socio-Technical Assessment in Rwanda

Stöcker, Claudia; Ho, Serene; Nkerabigwi, Placide; Schmidt, Cornelia; Koeva, Mila; Bennett, Rohan; Zevenbergen, J.A.

doi:10.3390/rs11091035

Cited by 21 publications

(19 citation statements)

References 31 publications

(32 reference statements)

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“…Finally, in a detailed socio-technical study for Rwanda, Stöcker et al [40] determine the overarching alignment between land administration stakeholder perceptions, the characteristics of the UAV data acquisition workflows, and the final spatial data products obtainable. Additionally, three different UAVs are tested for the quality of data obtainable and the possibilities for using of the technology within the current institutional setting.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Remote Sensing for Land Administration

et al. 2020

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Land administration constitutes the socio-technical systems that govern land tenure, use, value and development within a jurisdiction. The land parcel is the fundamental unit of analysis. Each parcel has identifiable boundaries, associated rights, and linked parties. Spatial information is fundamental. It represents the boundaries between land parcels and is embedded in cadastral sketches, plans, maps and databases. The boundaries are expressed in these records using mathematical or graphical descriptions. They are also expressed physically with monuments or natural features. Ideally, the recorded and physical expressions should align, however, in practice, this may not occur. This means some boundaries may be physically invisible, lacking accurate documentation, or potentially both. Emerging remote sensing tools and techniques offers great potential. Historically, the measurements used to produce recorded boundary representations were generated from ground-based surveying techniques. The approach was, and remains, entirely appropriate in many circumstances, although it can be timely, costly, and may only capture very limited contextual boundary information. Meanwhile, advances in remote sensing and photogrammetry offer improved measurement speeds, reduced costs, higher image resolutions, and enhanced sampling granularity. Applications of unmanned aerial vehicles (UAV), laser scanning, both airborne and terrestrial (LiDAR), radar interferometry, machine learning, and artificial intelligence techniques, all provide examples. Coupled with emergent societal challenges relating to poverty reduction, rapid urbanisation, vertical development, and complex infrastructure management, the contemporary motivation to use these new techniques is high. Fundamentally, they enable more rapid, cost-effective, and tailored approaches to 2D and 3D land data creation, analysis, and maintenance. This Special Issue hosts papers focusing on this intersection of emergent remote sensing tools and techniques, applied to domain of land administration.

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Section: Overview Of Contributionsmentioning

confidence: 99%

Remote Sensing for Land Administration

et al. 2020

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“…The flight height above the ground for Busogo was 100 m and for Muhoza 90 m. The final Ground Sampling Distance (GSD) was 2.18 cm in Busogo and 2.15 cm in Muhoza. For more detailed information about flight planning and image acquisition refer to [17].…”

Section: Data Preparationmentioning

confidence: 99%

Deep Fully Convolutional Networks for Cadastral Boundary Detection from UAV Images

2019

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There is a growing demand for cheap and fast cadastral mapping methods to face the challenge of 70% global unregistered land rights. As traditional on-site field surveying is time-consuming and labor intensive, imagery-based cadastral mapping has in recent years been advocated by fit-for-purpose (FFP) land administration. However, owing to the semantic gap between the high-level cadastral boundary concept and low-level visual cues in the imagery, improving the accuracy of automatic boundary delineation remains a major challenge. In this research, we use imageries acquired by Unmanned Aerial Vehicles (UAV) to explore the potential of deep Fully Convolutional Networks (FCNs) for cadastral boundary detection in urban and semi-urban areas. We test the performance of FCNs against other state-of-the-art techniques, including Multi-Resolution Segmentation (MRS) and Globalized Probability of Boundary (gPb) in two case study sites in Rwanda. Experimental results show that FCNs outperformed MRS and gPb in both study areas and achieved an average accuracy of 0.79 in precision, 0.37 in recall and 0.50 in F-score. In conclusion, FCNs are able to effectively extract cadastral boundaries, especially when a large proportion of cadastral boundaries are visible. This automated method could minimize manual digitization and reduce field work, thus facilitating the current cadastral mapping and updating practices.

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“…In this context, the degree to which the approach can support participatory mapping could also be investigated. Furthermore, research needs to be done on how to align innovative approaches with existing technical, social, legal and institutional frameworks in land administration [47][48][49]. We are currently pursuing this by developing documentation and testing material [50] that enables surveyors and policy makers in land administration to easily understand, test and adapt our approach.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

et al. 2019

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Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02-0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation.

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Unmanned Aerial System Imagery, Land Data and User Needs: A Socio-Technical Assessment in Rwanda

Cited by 21 publications

References 31 publications

Remote Sensing for Land Administration

Remote Sensing for Land Administration

Deep Fully Convolutional Networks for Cadastral Boundary Detection from UAV Images

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

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