Governance assessment of UAV implementation in Kenyan land administration system

Flores, César Casiano; Tan, Evrim; Crompvoets, Joep

doi:10.1016/j.techsoc.2021.101664

Cited by 7 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reality that cadastres attempt to depict is complex and dynamic [14,15], and underestimating the dynamics of the relationships between people and land, in reality, has led to outdated cadastral maps. Apart from the advances in surveying and mapping technologies, most of which have already been tested in developing countries [16][17][18][19], cadastral surveying and boundary data maintenance in developed countries continued to be carried out using ground-based techniques such as tacheometry and global navigation satellite systems (GNSS) methods [10,12]. This approach presents many challenges in terms of mapping efficiency, which can be overcome by low-cost and rapid cadastral surveying and indirect mapping techniques.…”

Section: Introductionmentioning

confidence: 99%

Revising Cadastral Data on Land Boundaries Using Deep Learning in Image-Based Mapping

Fetai

Grigillo

Lisec

2022

IJGI

View full text Add to dashboard Cite

One of the main concerns of land administration in developed countries is to keep the cadastral system up to date. The goal of this research was to develop an approach to detect visible land boundaries and revise existing cadastral data using deep learning. The convolutional neural network (CNN), based on a modified architecture, was trained using the Berkeley segmentation data set 500 (BSDS500) available online. This dataset is known for edge and boundary detection. The model was tested in two rural areas in Slovenia. The results were evaluated using recall, precision, and the F1 score—as a more appropriate method for unbalanced classes. In terms of detection quality, balanced recall and precision resulted in F1 scores of 0.60 and 0.54 for Ponova vas and Odranci, respectively. With lower recall (completeness), the model was able to predict the boundaries with a precision (correctness) of 0.71 and 0.61. When the cadastral data were revised, the low values were interpreted to mean that the lower the recall, the greater the need to update the existing cadastral data. In the case of Ponova vas, the recall value was less than 0.1, which means that the boundaries did not overlap. In Odranci, 21% of the predicted and cadastral boundaries overlapped. Since the direction of the lines was not a problem, the low recall value (0.21) was mainly due to overly fragmented plots. Overall, the automatic methods are faster (once the model is trained) but less accurate than the manual methods. For a rapid revision of existing cadastral boundaries, an automatic approach is certainly desirable for many national mapping and cadastral agencies, especially in developed countries.

show abstract

Section: Introductionmentioning

confidence: 99%

Revising Cadastral Data on Land Boundaries Using Deep Learning in Image-Based Mapping

Fetai

Grigillo

Lisec

2022

IJGI

View full text Add to dashboard Cite

show abstract

“…For example, advanced and innovative techniques are being tested and applied mainly in developing countries with low cadastral coverage to establish a LAS [9,10]. These are generally indirect techniques, such as delineating visible land boundaries using remote sensing data, including satellite imagery, and, increasingly, imagery from unmanned aerial vehicles (UAVs) [11][12][13][14]. In developed contexts, when countries have full cadastral coverage and registered land rights, there are few case studies reporting on the ability of UAV-based cadastral mapping to update land data and meet accuracy requirements compared to ground-based methods [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Inconsistencies in Cadastral Boundary Data—Digitisation and Maintenance

Fetai

Tekavec

Fras

et al. 2022

Land

View full text Add to dashboard Cite

Most cadastral systems today are coordinate-based and contain only a weak or no reference to measurements or the origin of the information. In some contexts, this is largely due to the transition of land data management and maintenance from an analogue to a digital environment. This study focuses on analysing the importance of the measurement-based cadastre and the digitisation process in North Macedonia and Slovenia. The survey-based boundary data and their integration into the digital environment were not considered in either case study. The positional differences between the survey-based boundary coordinates and the graphical coordinates of the boundaries are significant. The RMSE(2D) for Trebosh was 48 cm, and the RMSE(2D) for Ivanjševci was 56 cm. Consequently, the differences in location affected the areas of the cadastral parcels, resulting in an RMSE of 26 m2 and 23 m2 for Trebosh and Ivanjševci, respectively. These differences can be considered as differences within the cadastral boundary data. Therefore, before harmonising the data between the cadastre and the land register, the inconsistencies within the cadastral data should be eliminated first. The differences in the location of cadastral boundaries and parcel area create new challenges in cadastral procedures (formatting of parcels), conflicts in the relocation of boundaries, and impacts on the land market. The solution lies in the way data is maintained, avoiding duplication of attributes or eliminating inconsistencies (after duplication). Both solutions require further modifications of the legal framework for cadastral procedures related to boundary adjustments and data compliance. This study provides a basis for evaluating inconsistencies in cadastral data and highlights the importance of proper source data selection in the digitization process.

show abstract

Role of Drone Technology in Sustainable Rural Development: Opportunities and Challenges

Mandla¹,

Chokkavarapu

Peddinti

2023

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

Governance assessment of UAV implementation in Kenyan land administration system

Cited by 7 publications

References 36 publications

Revising Cadastral Data on Land Boundaries Using Deep Learning in Image-Based Mapping

Revising Cadastral Data on Land Boundaries Using Deep Learning in Image-Based Mapping

Inconsistencies in Cadastral Boundary Data—Digitisation and Maintenance

Role of Drone Technology in Sustainable Rural Development: Opportunities and Challenges

Contact Info

Product

Resources

About