High-Quality UAV-Based Orthophotos for Cadastral Mapping: Guidance for Optimal Flight Configurations

Abstract

During the past years, unmanned aerial vehicles (UAVs) gained importance as a tool to quickly collect high-resolution imagery as base data for cadastral mapping. However, the fact that UAV-derived geospatial information supports decision-making processes involving people’s land rights ultimately raises questions about data quality and accuracy. In this vein, this paper investigates different flight configurations to give guidance for efficient and reliable UAV data acquisition. Imagery from six study areas across Europe and Africa provide the basis for an integrated quality assessment including three main aspects: (1) the impact of land cover on the number of tie-points as an indication on how well bundle block adjustment can be performed, (2) the impact of the number of ground control points (GCPs) on the final geometric accuracy, and (3) the impact of different flight plans on the extractability of cadastral features. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure is within a range of few centimeters in all datasets, this study reveals large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, the results and recommendations presented in this paper can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows.

Coherence of Cadastral Data in Land Management—A Case Study of Rural Areas in Poland

“…Lack of data currency is a frequent phenomenon and they have to be modernised to maintain their adequate quality. One should not forget the rapid technological progress [15][16][17][18][19][20], related to measurement capabilities, which makes data on the plot area and individual methods of land use more accurate. New technologies enable adding another (third) cadastral data dimension [21][22][23][24][25].…”

“…Effective data verification and regular updating are essential in land management [6][7][8], although it is difficult given the amount of accumulated data [4,[9][10][11][12]. Cadastral data quality has been studied extensively [10][11][12][13][14][15][16][17][18] and it covers a wide range of topics. Data necessary to update cadastral data are increasingly often acquired using cuttingedge technologies [13,[15][16][17][18][19][20], including unmanned aerial vehicles (UAV).…”

“…Cadastral data quality has been studied extensively [10][11][12][13][14][15][16][17][18] and it covers a wide range of topics. Data necessary to update cadastral data are increasingly often acquired using cuttingedge technologies [13,[15][16][17][18][19][20], including unmanned aerial vehicles (UAV). The issue of data transformation from 2D to 3D has been widely dealt with [21][22][23][24][25].…”

“…The real estate tax revenue accounts for part of the commune budget. Although an analysis of cadastral data has been dealt with in many pieces of scientific research in recent years [13][14][15][16][17][18][19][20], more studies are needed as they have a direct and indirect impact on socio-economic processes and methods of land use, etc. [8,34,35].…”

Deep Learning for Detection of Visible Land Boundaries from UAV Imagery

“…In cadastral applications, UAVs have gained prominence as a powerful technology that can bridge the gap between slow but accurate field surveys and the fast approach of conventional aerial surveys [13]. Sensors on UAVs provide low-cost, efficient and flexible systems for high-resolution spatial data acquisition, enabling the production of orthoimages, digital surface models and point clouds [14].…”