Unmanned aerial vehicle mapping for settlement upgrading

Gevaert, C.M.

doi:10.3990/1.9789036546355

Cited by 3 publications

(3 citation statements)

References 155 publications

(271 reference statements)

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“…Having the ability to process videos in real time, UAVs can, in principle, also be used in monitoring stress among people and following their movement patterns [44]. Among the many advantages of using UAVs, safety and privacy topics should not be left unmentioned [45]. Nowadays, many countries have regulations related to the usage of UAVs [46]; however, in most of the LMICs, such regulations are still not in place.…”

Section: Introductionmentioning

confidence: 99%

The Spatial Dimension of COVID-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil

Brito

Kuffer

Koeva

et al. 2020

IJGI

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The COVID-19 health emergency is impacting all of our lives, but the living conditions and urban morphologies found in poor communities make inhabitants more vulnerable to the COVID-19 outbreak as compared to the formal city, where inhabitants have the resources to follow WHO guidelines. In general, municipal spatial datasets are not well equipped to support spatial responses to health emergencies, particularly in poor communities. In such critical situations, Earth observation (EO) data can play a vital role in timely decision making and can save many people’s lives. This work provides an overview of the potential of EO-based global and local datasets, as well as local data gathering procedures (e.g., drones), in support of COVID-19 responses by referring to two slum areas in Salvador, Brazil as a case study. We discuss the role of datasets as well as data gaps that hinder COVID-19 responses. In Salvador and other low- and middle-income countries’ (LMICs) cities, local data are available; however, they are not up to date. For example, depending on the source, the population of the study areas in 2020 varies by more than 20%. Thus, EO data integration can help in updating local datasets and in the acquisition of physical parameters of poor urban communities, which are often not systematically collected in local surveys.

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Section: Introductionmentioning

confidence: 99%

The Spatial Dimension of COVID-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil

Brito

Kuffer

Koeva

et al. 2020

IJGI

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“…Automatically extracting visible cadastral boundaries combined with (legal) adjudication and an incorporation of local knowledge from human operators offers the potential to improve current cadastral mapping approaches in terms of time, cost, accuracy and acceptance [6]. High-resolution remote sensing imagery, increasingly captured with Unmanned Aerial Vehicles (UAVs) in land administration [7][8][9][10][11], provides the basis for such a semi-automated delineation of visible boundaries.…”

Section: Introductionmentioning

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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“…Recently, the use of UAVs in land administration is expanding [12,41,241,301,302]: the high-resolution imagery is often used to visually detect and manually delineate cadastral boundaries. Such boundaries outline land parcels, for which additional information such as ownership and value are saved in a corresponding register [303].…”

Section: Introductionmentioning

confidence: 99%

Automating image-based cadastral boundary mapping

Crommelinck¹

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Unmanned aerial vehicles (UAV), also known as drones, Unmanned Aerial Systems (UAS), or Remotely Piloted Aircraft Systems (RPAS) are small aircraft 1.1.4 Boundary Delineation for UAV-based Cadastral Mapping In all case studies, cadastral boundaries are manually detected and delineated from orthoimages. This is realized either in an office with a small group of involved stakeholders for one parcel or in a community mapping approach for several parcels at once. All case studies lack an automatic approach to extract boundary features from the UAV data. An automatic or semi-automatic feature Objective (i) is addressed in chapters 1 and 3, objective (ii) in chapters 4, 5 and 6, and objective (iii) in chapters 7 and 8. The dissertation is structured as follows: Chapter 1 introduces the Ph.D. research. We describe the research gap to be addressed and formulate corresponding research objectives. Chapter 2 provides contextual information on the Ph.D. research. This is done by reviewing approaches for feature extraction from various application fields. These are synthesized into a hypothetical workflow applicable for automated boundary delineation from UAV data. The workflow consists of image segmentation, line extraction, and contour generation. Chapter 3 investigates which method performs best for image segmentation, which is the first step of the hypothetical workflow proposed in chapter 3. This is done by analyzing the transferability of gPb contour detection, a state-ofthe-art computer vision method, to UAV-based cadastral mapping. Chapter 4 investigates which method performs best for line extraction, which is the second step of the hypothetical workflow proposed in chapter 3. This is done by analyzing a superpixel approach, namely simple linear iterative * This chapter is based on: 44.

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Unmanned aerial vehicle mapping for settlement upgrading

Cited by 3 publications

References 155 publications

The Spatial Dimension of COVID-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil

The Spatial Dimension of COVID-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil

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

Automating image-based cadastral boundary mapping

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